From 8d750d3e621b97dc6ba66abab92b67deb023c007 Mon Sep 17 00:00:00 2001
From: Louis Moresi <louis.moresi@anu.edu.au>
Date: Mon, 26 Aug 2024 10:21:33 +1000
Subject: [PATCH 1/4] Add small offset in box point capture

---
 src/underworld3/meshing.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/src/underworld3/meshing.py b/src/underworld3/meshing.py
index 3ca298a81..517ed27ed 100644
--- a/src/underworld3/meshing.py
+++ b/src/underworld3/meshing.py
@@ -207,6 +207,8 @@ class boundary_normals_3D(Enum):
 
     def box_return_coords_to_bounds(coords):
 
+        epsilon = 1.0e-3
+
         x00s = coords[:, 0] < minCoords[0]
         x01s = coords[:, 0] > maxCoords[0]
         x10s = coords[:, 1] < minCoords[1]
@@ -216,14 +218,14 @@ def box_return_coords_to_bounds(coords):
             x20s = coords[:, 2] < minCoords[2]
             x21s = coords[:, 2] > maxCoords[2]
 
-        coords[x00s, 0] = minCoords[0]
-        coords[x01s, 0] = maxCoords[0]
-        coords[x10s, 1] = minCoords[1]
-        coords[x11s, 1] = maxCoords[1]
+        coords[x00s, 0] = minCoords[0] + epsilon
+        coords[x01s, 0] = maxCoords[0] - epsilon
+        coords[x10s, 1] = minCoords[1] + epsilon
+        coords[x11s, 1] = maxCoords[1] - epsilon
 
         if dim == 3:
-            coords[x20s, 2] = minCoords[2]
-            coords[x21s, 2] = maxCoords[2]
+            coords[x20s, 2] = minCoords[2] + epsilon
+            coords[x21s, 2] = maxCoords[2] - epsilon
 
         return coords
 
@@ -369,6 +371,7 @@ class boundary_normals_3D(Enum):
             gmsh.model.set_physical_name(2, 99999, "Elements")
 
             nx, ny = elementRes
+            print("Structured box element resolution", nx, ny)
 
             gmsh.model.mesh.set_transfinite_curve(
                 tag=l1, numNodes=nx + 1, meshType="Progression", coef=1.0

From 876d0710f65580b357187c4b48943060d6486b0d Mon Sep 17 00:00:00 2001
From: Louis Moresi <louis.moresi@anu.edu.au>
Date: Mon, 26 Aug 2024 10:23:35 +1000
Subject: [PATCH 2/4] Clarifications in nodal swarm

---
 src/underworld3/swarm.py | 66 ++++++++++++++++++++++++++--------------
 1 file changed, 43 insertions(+), 23 deletions(-)

diff --git a/src/underworld3/swarm.py b/src/underworld3/swarm.py
index 224d35fe6..1b0d4d90d 100644
--- a/src/underworld3/swarm.py
+++ b/src/underworld3/swarm.py
@@ -1,3 +1,4 @@
+from posixpath import pardir
 import petsc4py.PETSc as PETSc
 
 import numpy as np
@@ -447,9 +448,7 @@ def save(
             raise RuntimeError("The filename must end with .h5")
 
         if h5py.h5.get_config().mpi == True and not force_sequential:
-            with h5py.File(
-                f"{filename[:-3]}.h5", "w", driver="mpio", comm=comm
-            ) as h5f:
+            with h5py.File(f"{filename[:-3]}.h5", "w", driver="mpio", comm=comm) as h5f:
                 with self.swarm.access(self):
                     if compression == True:
                         h5f.create_dataset(
@@ -1140,9 +1139,7 @@ def save(
             with self.access():
                 data_copy = self.data[:].copy()
 
-            with h5py.File(
-                f"{filename[:-3]}.h5", "w", driver="mpio", comm=comm
-            ) as h5f:
+            with h5py.File(f"{filename[:-3]}.h5", "w", driver="mpio", comm=comm) as h5f:
                 if compression == True:
                     h5f.create_dataset(
                         "coordinates",
@@ -1491,9 +1488,11 @@ def __init__(self, swarm):
                 self.em_swarm = swarm
 
             def __enter__(self):
+
                 pass
 
             def __exit__(self, *args):
+
                 for var in self.em_swarm.vars.values():
                     # only de-access variables we have set access for.
                     if var not in deaccess_list:
@@ -1520,6 +1519,14 @@ def __exit__(self, *args):
 
                     cellid[:] = self.em_swarm.mesh.get_closest_cells(coords).reshape(-1)
 
+                    # num_lost = np.where(cellid == -1)[0].shape[0]
+                    # print(
+                    #     f"{uw.mpi.rank} - EM 1: illegal_cells - {num_lost}", flush=True
+                    # )
+
+                    # if num_lost != 0:
+                    #     print("LOST: ", coords[np.where(cellid == -1)])
+
                     self.em_swarm.dm.restoreField("DMSwarmPIC_coor")
                     self.em_swarm.dm.restoreField("DMSwarm_cellid")
                     # now migrate.
@@ -1678,27 +1685,28 @@ def advection(
         for step in range(0, substeps):
 
             with self.access(X0):
-                X0.data[...] = self.data[...]
+                X0.data[...] = self.particle_coordinates.data[...]
 
             # Mid point algorithm (2nd order)
 
             if order == 2:
                 with self.access(self.particle_coordinates):
-                    v_at_Vpts = np.zeros_like(self.data)
+                    v_at_Vpts = np.zeros_like(self.particle_coordinates.data)
 
                     if evalf:
                         for d in range(self.dim):
                             v_at_Vpts[:, d] = uw.function.evalf(
-                                V_fn_matrix[d], self.data
+                                V_fn_matrix[d], self.particle_coordinates.data
                             ).reshape(-1)
                     else:
                         for d in range(self.dim):
                             v_at_Vpts[:, d] = uw.function.evaluate(
-                                V_fn_matrix[d], self.data
+                                V_fn_matrix[d], self.particle_coordinates.data
                             ).reshape(-1)
 
                     mid_pt_coords = (
-                        self.data[...] + 0.5 * delta_t * v_at_Vpts / substeps
+                        self.particle_coordinates.data[...]
+                        + 0.5 * delta_t * v_at_Vpts / substeps
                     )
 
                     # validate_coords to ensure they live within the domain (or there will be trouble)
@@ -1706,24 +1714,23 @@ def advection(
                     if restore_points_to_domain_func is not None:
                         mid_pt_coords = restore_points_to_domain_func(mid_pt_coords)
 
-                    self.data[...] = mid_pt_coords[...]
+                    self.particle_coordinates.data[...] = mid_pt_coords[...]
 
                     del mid_pt_coords
 
                     ## Let the swarm be updated, and then move the rest of the way
 
-                with self.access(self.particle_coordinates):
                     v_at_Vpts = np.zeros_like(self.data)
 
                     if evalf:
                         for d in range(self.dim):
                             v_at_Vpts[:, d] = uw.function.evalf(
-                                V_fn_matrix[d], self.data
+                                V_fn_matrix[d], self.particle_coordinates.data
                             ).reshape(-1)
                     else:
                         for d in range(self.dim):
                             v_at_Vpts[:, d] = uw.function.evaluate(
-                                V_fn_matrix[d], self.data
+                                V_fn_matrix[d], self.particle_coordinates.data
                             ).reshape(-1)
 
                     # if (uw.mpi.rank == 0):
@@ -1735,7 +1742,7 @@ def advection(
                     if restore_points_to_domain_func is not None:
                         new_coords = restore_points_to_domain_func(new_coords)
 
-                    self.data[...] = new_coords[...]
+                    self.particle_coordinates.data[...] = new_coords[...]
 
                     del new_coords
                     del v_at_Vpts
@@ -1802,8 +1809,6 @@ def advection(
                 * self.mesh._radii[cellid[swarm_size::]].reshape(-1, 1)
             )
 
-            # print(f"{perturbation}")
-
             coords[swarm_size::] = self.mesh.particle_X_orig[:, :] + perturbation
             cellid[swarm_size::] = self.mesh.particle_CellID_orig[:, 0]
             rmsh[swarm_size::] = 0
@@ -1895,9 +1900,12 @@ class NodalPointSwarm(Swarm):
     def __init__(
         self,
         trackedVariable: uw.discretisation.MeshVariable,
+        verbose=False,
     ):
         self.trackedVariable = trackedVariable
         self.swarmVariable = None
+        self.verbose = verbose
+
         mesh = trackedVariable.mesh
 
         # Set up a standard swarm
@@ -1916,8 +1924,9 @@ def __init__(
             name,
             nswarm,
             vtype=trackedVariable.vtype,
-            proxy_degree=trackedVariable.degree,
-            proxy_continuous=trackedVariable.continuous,
+            _proxy=False,
+            # proxy_degree=trackedVariable.degree,
+            # proxy_continuous=trackedVariable.continuous,
             varsymbol=symbol,
         )
 
@@ -1933,15 +1942,26 @@ def __init__(
         cellid = nswarm.dm.getField("DMSwarm_cellid")
         coords = nswarm.dm.getField("DMSwarmPIC_coor").reshape((-1, nswarm.dim))
         coords[...] = trackedVariable.coords[...]
-        cellid[:] = self.mesh.get_closest_cells(coords)
+        cellid[:] = self.mesh.get_closest_local_cells(coords)
+
+        # num_lost = np.where(cellid == -1)[0].shape[0]
+        # print(f"1: illegal_cells - {num_lost}", flush=True)
 
         # Move slightly within the chosen cell to avoid edge effects
         centroid_coords = self.mesh._centroids[cellid]
-        shift = 1.0e-3
-        coords[...] = (1.0 - shift) * coords[...] + shift * centroid_coords[...]
+
+        shift = 0.01
+        coords[:, :] = (1.0 - shift) * coords[:, :] + shift * centroid_coords[:, :]
+
+        # cellid does not change if we move slightly towards the cell centroid
+        # cellid[:] = self.mesh.get_closest_local_cells(coords)
+        # num_lost = np.where(cellid == -1)[0].shape[0]
+
+        # print(f"1: illegal_cells - {num_lost}", flush=True)
 
         nswarm.dm.restoreField("DMSwarmPIC_coor")
         nswarm.dm.restoreField("DMSwarm_cellid")
+
         nswarm.dm.migrate(remove_sent_points=True)
 
         with nswarm.access(nX0):

From 462aad7643ff3f2109d96314cf72f87c57c0d219 Mon Sep 17 00:00:00 2001
From: Louis Moresi <louis.moresi@anu.edu.au>
Date: Mon, 26 Aug 2024 10:25:21 +1000
Subject: [PATCH 3/4] ddt - allowing discontinuous fields for advected points
 if required

---
 src/underworld3/systems/ddt.py | 70 ++++++++++++++++++++--------------
 1 file changed, 42 insertions(+), 28 deletions(-)

diff --git a/src/underworld3/systems/ddt.py b/src/underworld3/systems/ddt.py
index ff5de49f6..8a5063393 100644
--- a/src/underworld3/systems/ddt.py
+++ b/src/underworld3/systems/ddt.py
@@ -80,8 +80,21 @@ def __init__(
                 )
             )
 
+        # Working variable that has a potentially different discretisation from psi_star
+        # We project from this to psi_star
+        #
+
+        self._workVar = uw.discretisation.MeshVariable(
+            f"W_{self.instance_number}_{i}",
+            self.mesh,
+            vtype=vtype,
+            degree=2,
+            continuous=False,
+            varsymbol=rf"{{ {varsymbol}^\nabla }}",
+        )
+
         # We just need one swarm since this is inherently a sequential operation
-        nswarm = uw.swarm.NodalPointSwarm(self.psi_star[0])
+        nswarm = uw.swarm.NodalPointSwarm(self._workVar)
         self._nswarm_psi = nswarm
 
         # The projection operator for mapping swarm values to the mesh - needs to be different for
@@ -94,12 +107,14 @@ def __init__(
         elif vtype == uw.VarType.VECTOR:
             self._psi_star_projection_solver = (
                 uw.systems.solvers.SNES_Vector_Projection(
-                    self.mesh, self.psi_star[0], verbose=False,
+                    self.mesh,
+                    self.psi_star[0],
+                    verbose=False,
                 )
             )
 
         elif vtype == uw.VarType.SYM_TENSOR or vtype == uw.VarType.TENSOR:
-            self._WorkVar = uw.discretisation.MeshVariable(
+            self._WorkVarTP = uw.discretisation.MeshVariable(
                 f"W_star_slcn_{self.instance_number}",
                 self.mesh,
                 vtype=uw.VarType.SCALAR,
@@ -109,14 +124,19 @@ def __init__(
             )
             self._psi_star_projection_solver = (
                 uw.systems.solvers.SNES_Tensor_Projection(
-                    self.mesh, self.psi_star[0], self._WorkVar, verbose=False
+                    self.mesh, self.psi_star[0], self._WorkVarTP, verbose=False
                 )
             )
 
-        self._psi_star_projection_solver.uw_function = self.psi_fn
+        # We should find a way to add natural bcs here
+        # (self.Unknowns.u carried as a symbol from solver to solver)
+
+        self._psi_star_projection_solver.uw_function = self._workVar.sym
         self._psi_star_projection_solver.bcs = bcs
         self._psi_star_projection_solver.smoothing = smoothing
 
+        self._smoothing = smoothing
+
         return
 
     @property
@@ -134,14 +154,6 @@ def _object_viewer(self):
 
         super()._object_viewer()
 
-        ## feedback on this instance
-        # display(Latex(r"$\quad\psi = $ " + self.psi._repr_latex_()))
-        # display(
-        #     Latex(
-        #         r"$\quad\Delta t_{\textrm{phys}} = $ "
-        #         + sympy.sympify(self.dt_physical)._repr_latex_()
-        #     )
-        # )
         display(Latex(rf"$\quad$History steps = {self.order}"))
 
     def update(
@@ -186,9 +198,11 @@ def update_pre_solve(
                     + (1 - phi) * self.psi_star[i].data[...]
                 )
 
-        # 2. Compute the upstream values
+        # 2. Compute the upstream values from the psi_fn
 
         # We use the u_star variable as a working value here so we have to work backwards
+        # so we don't over-write the history terms
+
         for i in range(self.order - 1, -1, -1):
             with self._nswarm_psi.access(self._nswarm_psi._X0):
                 self._nswarm_psi._X0.data[...] = self._nswarm_psi.data[...]
@@ -200,12 +214,13 @@ def update_pre_solve(
                 order=2,
                 corrector=False,
                 restore_points_to_domain_func=self.mesh.return_coords_to_bounds,
-                evalf=evalf,
+                evalf=True,  #!
             )
 
             if i == 0:
                 # Recalculate u_star from u_fn
                 self._psi_star_projection_solver.uw_function = self.psi_fn
+                self._psi_star_projection_solver.smoothing = 1.0e-6
                 self._psi_star_projection_solver.solve(verbose=verbose)
 
             if evalf:
@@ -217,29 +232,28 @@ def update_pre_solve(
             else:
                 with self._nswarm_psi.access(self._nswarm_psi.swarmVariable):
                     for d in range(self.psi_star[i].shape[1]):
-                        self._nswarm_psi.swarmVariable.data[:, d] = (
-                            uw.function.evaluate(
-                                self.psi_star[i].sym[d], self._nswarm_psi.data
-                            )
+                        self._nswarm_psi.swarmVariable.data[
+                            :, d
+                        ] = uw.function.evaluate(
+                            self.psi_star[i].sym[d], self._nswarm_psi.data
                         )
 
-            # with self.mesh.access():
-            #     print("1:", self.psi_star[0].data, flush=True)
-
-            # with self._nswarm_psi.access():
-            #     print("1S:", self._nswarm_psi.swarmVariable.data, flush=True)
-
             # restore coords (will call dm.migrate after context manager releases)
             with self._nswarm_psi.access(self._nswarm_psi.particle_coordinates):
                 self._nswarm_psi.data[...] = self._nswarm_psi._nX0.data[...]
 
             # Now project to the mesh using bc's to obtain u_star
 
+            # Push data from swarm back to _workVar.data
 
-            self._psi_star_projection_solver.uw_function = (
-                self._nswarm_psi.swarmVariable.sym
-            )
+            with self.mesh.access(self._workVar):
+                with self._nswarm_psi.access():
+                    self._workVar.data[...] = self._nswarm_psi.swarmVariable.data[...]
 
+            # Project from advected swarm to semi-Lagrangian variables.
+            #
+            self._psi_star_projection_solver.uw_function = self._workVar.sym
+            self._psi_star_projection_solver.smoothing = 1.0e-3
             self._psi_star_projection_solver.solve()
 
             # Copy data from the projection operator if required

From 67ce8642baf12bdca29d824b936e5d6fced8c82e Mon Sep 17 00:00:00 2001
From: Louis Moresi <louis.moresi@anu.edu.au>
Date: Fri, 4 Oct 2024 10:41:15 -0700
Subject: [PATCH 4/4] Add notebooks

---
 docs/user/Installation.qmd                    |  91 +++++++++++
 docs/user/NextSteps.qmd                       |  81 ++++++++--
 .../Notebooks/.jupyter/desktop-settings.json  |   2 +-
 docs/user/Notebooks/1-Meshes.ipynb            |  16 +-
 docs/user/Notebooks/2-Variables.ipynb         |   2 +-
 docs/user/Notebooks/3-Symbolic_Forms.ipynb    |  48 ++++--
 docs/user/Notebooks/4-Solvers.ipynb           |  24 +--
 docs/user/Notebooks/5-Solvers.ipynb           |   2 +-
 docs/user/Notebooks/6-Timestepping.ipynb      |  65 +++-----
 docs/user/Notebooks/7-Unsteady_Flow.ipynb     |   2 +-
 ...leSwarms.ipynb => 8-Particle_Swarms.ipynb} |  30 ++--
 docs/user/Notebooks/Notebook_Index.ipynb      | 147 ++++++++++++++++++
 .../html5/annulus_convection_plot.html        |   4 +-
 docs/user/Notebooks/html5/sine_squared.html   |   4 +-
 .../Notebooks/html5/spherical_mesh_plot.html  |   4 +-
 .../Notebooks/html5/temperature_plot.html     |   4 +-
 .../Notebooks/html5/terrain_flow_plot.html    |   4 +-
 .../Notebooks/media/NotebookScreenshot.png    | Bin 0 -> 1223118 bytes
 docs/user/Notebooks/ns_flow_at_100.png        | Bin 553773 -> 0 bytes
 docs/user/Notebooks/ns_flow_at_50.png         | Bin 469045 -> 0 bytes
 docs/user/_quarto.yml                         |  28 ++--
 docs/user/index.qmd                           |  53 ++++---
 docs/user/media/ExplicitGrains_Example.png    | Bin 0 -> 614879 bytes
 docs/user/media/RelaxingMesh.png              | Bin 0 -> 842654 bytes
 docs/user/media/pyvista/AdaptedSphere.html    |  19 +++
 docs/user/media/pyvista/ChannelFlow.html      |  19 +++
 .../media/pyvista/SphereStreamLinesS.html     |  19 +++
 27 files changed, 504 insertions(+), 164 deletions(-)
 create mode 100644 docs/user/Installation.qmd
 rename docs/user/Notebooks/{8-ParticleSwarms.ipynb => 8-Particle_Swarms.ipynb} (97%)
 create mode 100644 docs/user/Notebooks/Notebook_Index.ipynb
 create mode 100644 docs/user/Notebooks/media/NotebookScreenshot.png
 delete mode 100644 docs/user/Notebooks/ns_flow_at_100.png
 delete mode 100644 docs/user/Notebooks/ns_flow_at_50.png
 create mode 100644 docs/user/media/ExplicitGrains_Example.png
 create mode 100644 docs/user/media/RelaxingMesh.png
 create mode 100644 docs/user/media/pyvista/AdaptedSphere.html
 create mode 100644 docs/user/media/pyvista/ChannelFlow.html
 create mode 100644 docs/user/media/pyvista/SphereStreamLinesS.html

diff --git a/docs/user/Installation.qmd b/docs/user/Installation.qmd
new file mode 100644
index 000000000..26b01435a
--- /dev/null
+++ b/docs/user/Installation.qmd
@@ -0,0 +1,91 @@
+---
+title: "Installation Guide"
+subtitle: "Hints and Tips"
+authors:
+- name: Underworld Team
+
+exports:
+- format: html
+---
+
+## Source code build using mamba
+
+We recommend that you use a `conda` / `mamba` virtual environment to build `underworld3` whenever you want to install on a personal workstation or laptop (linux or macOS).The [`mamba` documentation](https://mamba.readthedocs.io/en/latest/index.html) will help you get started if you are not familiar with the philososphy and practice of this package management system.
+
+The `underworld3` build / run-time dependencies can be installed using
+
+```bash
+    (base) % git clone -b development --single-branch https://github.com/underworldcode/underworld3 /path/to/underworld3
+    (base) % cd /path/to/underworld3
+    (base) % mamba env create -n uw3 -f environment.yml
+    (base) % mamba activate uw3
+
+    (uw3) % pip install .
+```
+
+## Source code build using mamba and existing PETSc
+
+If you have an exisiting build of `PETSc` that you need to use, the following
+instructions work well. We create the environment as before but remove the pre-
+installed `PETSc` and `petsc4py`.
+
+```bash
+    (base) % git clone -b development --single-branch https://github.com/underworldcode/underworld3 /path/to/underworld3
+    (base) % cd /path/to/underworld3
+    (base) % mamba env create -n uw3p -f environment.yml
+    (base) % mamba activate uw3p
+
+    (uw3p) % mamba remove petsc4py
+    (uw3p) % mamba remove petsc
+```
+
+Next set up PETSc as you like it and build it using the tools
+within the current mamba virtual environment. Set the `PETSC_ARCH`
+environment variable to the name of this virtual environment to keep things
+from becoming muddled. See [PETSc Installation]() for details on
+how to configure and build what you need.
+
+```bash
+
+    (uw3p) % export PETSC_DIR=/path/to/petsc
+    (uw3p) % export PETSC_ARCH="uw3p"
+    (uw3p) % git clone -b release https://gitlab.com/petsc/petsc.git $PETSC_DIR
+    (uw3p) % cd $PETSC_DIR
+    (uw3p) % # Configure & Build step
+```
+Next we `pip install` the petsc4py that we just built into the mamba virtual environment. This
+ensures that `petsc4py` is available and also that its internal configuration points to the `PETSc` installation we just created. This way we don't need to manage environment variables to point to the build that corresponds to a given virtual environment.
+
+```bash
+    (uw3p) % mamba install cmake
+    (uw3p) % cd $PETSC_DIR/src/binding/petsc4py
+    (uw3p) % pip install .
+```
+
+Building `underworld3` against this version of `PETSc` should be identical to the mamba version.
+
+```bash
+    (uw3p) % cd /path/to/underworld3
+    (uw3p) % pip install .
+```
+
+#### Did it work ?
+
+First run the tests:
+
+```bash
+    (uw3p) % cd /path/to/underworld3
+    (uw3p) % source test.sh
+```
+
+If you have problems, contact us via the `underworl3` [GitHub issue tracker](https://github.com/underworldcode/underworld3/issues)
+
+## Docker container
+
+Windows users, if you don't want to create a linux partition, you can use our containers with `docker` or `podman`. As the code is still in active development, we are not always able to provide containers for each change to the development branch. We ask that you reach out to us on our [GitHub issue tracker](https://github.com/underworldcode/underworld3/issues) to ask for information (use the "question" label).
+
+## HPC builds
+
+`Underworld3` is inherently parallel and designed for use in high performance computing environments. The symbolic layer is relatively lightweight and should not adversely affect launch time (drawing down libraries from disk) or execution time (very few calculations are done in python itself).
+
+In the HPC environment you may find it difficult to control the software stack. You will need to ensure that you can build against PETSc 3.21 or higher, since important functionality that we need is only available from that release onwards. We are happy to provide assistance with builds on specific machines and ask that you contact us through the [GitHub issue tracker](https://github.com/underworldcode/underworld3/issues) so that other people are able to browse the issues and their fixes.
diff --git a/docs/user/NextSteps.qmd b/docs/user/NextSteps.qmd
index 33c842b42..a90d79a55 100644
--- a/docs/user/NextSteps.qmd
+++ b/docs/user/NextSteps.qmd
@@ -12,31 +12,86 @@ exports:
 
 ### Underworld Documentation and Examples
 
+In addition to the notebooks in this brief set of examples, there are a number of sources of information on using `Underworld3` that you can access:
+
+  - [The Underworld Website / Blog](https://www.underworldcode.org)
+
+  - [The API documentation](https://underworldcode.github.io/underworld3/development_api/index.html)
+  (all the modules and functions and their full sets of arguments) is automatically generated from the source code and uses the same rich markdown content as the notebook help text.
+
+  - The [`underworld3` GitHub repository](https://github.com/underworldcode/underworld3) is the most active development community for the code.
+
 
 ### Benchmarks
 
+The [Underworld3 Benchmarks Repository](https://github.com/underworld-community/UW3-benchmarks) is a useful place to find community benchmarks coded in `underworld3` along with accuracy and convergence analysis. This is an open repository where you can make a pull request with new benchmark submissions once you get the hang of things.
 
 ### The Underworld Community
 
-Sharing codes / fixes
-
-Sharing models
+The [Underworld Community](https://github.com/underworld-community) organisation on Github is a collection of contributed repositories from the underworld user community for all versions of the code and now includes scripts for underworld3.
 
 ### Parallel Execution
 
-Advise using jupytext to render notebooks at .py files. This is helpful
-in shared environments because the files contain no output and play
-better with version control.
+`Underworld3` is inherently parallel and designed for high performance computing. The symbolic layer is a relatively thin veneer that sits on top of the `PETSc` machinery. A script that has been developed in `jupyter` should be transferrable to an HPC environment with no changes (except that inherently serial operations such as visualisation and model reduction are best left to postprocessing / co-processing).
+
+We recommend installing `jupytext` which allows either a seamless two-way conversion (or pairing) between the `ipynb` format and an annotated python script, or the ability to work entirely with annoted python scripts and not use `ipynb` at all. The python form does not store the output of notebook cells but there are advantages to this when scripts are under version control.
+
+Almost all of our notebook examples are annotated python for this reason.An exception is the collection of notebooks in this quick-start guide because we want to show you the rendered output in the static web pages.
+
+```bash
+    mpirun -np 1024 python3 Modelling_Script.py -uw_resolution 96
+```
+
+The main difference between the notebook development environment and HPC is the lack of interactivity, particularly in sending parameters to the script at launch time. Typically, we expect the HPC version to be running at much higher resolution, or for many more timesteps than the development notebook. We use the `PETSc` command line parsing machinery to generate notebooks that also can ingest run-time parameters from a script (as above).
+
+
+### Advanced capabilities
+
+Digging a bit deeper into `underworld3`, there are many capabilities that require a clear understanding of the concepts that underlie the implementation. The following examples are not *plug-and-play*, but they do only require python coding using the `underworld3` API and no detailed knowledge of `petsc4py` or `PETSc`. [Get in touch with us](https://github.com/underworldcode/underworld3/issues) if you want to try this sort of thing but can't figure it out for yourself.
 
-Not such a good idea if you want to have a tutorial with outputs (like this one)
+#### Deforming meshes
+
+In [Example 8](Notebooks/8-Particle_Swarms.ipynb), we made small variations to the mesh to conform to basal topography. We did not remesh, so we had to be careful to apply a smooth, analytic displacement to every node. For more general free-surface models, we need to calculate a smooth function using the computed boundary motions (e.g, solving a poisson equation with known boundary displacements as boundary conditions). We need to step through time and it is common to stabilize the surface motions through a surface integral term that accounts for the interface displacement during the timestep. The example below shows an `underworld3` forward model with internal loads timestepped close to isostatic equilibrium.
+
+![*Stokes flow driven by buoyancy in an annulus defined by two embedded surfaces within an enveloping disk mesh. The surfaces deform in response to the flow. The embedding medium has a very low viscosity but still acts to damp rotational modes. The outer boundary of the disk can be set to a far-field gravitational potential for whole-Earth relaxation models*](media/RelaxingMesh.png){width=50%}
+
+In a more general case, we need to account to horizontal motions. This is more complicated because the horizontal velocities can be large even when vertical equilibrium is achieved. So we need to solve for the advected component of vertical motion in addition to the local component. Hooray for symbolic algebra !
+
+#### Weak / penalty boundary conditions
+
+[Example 8](Notebooks/8-Particle_Swarms.ipynb) introduced the idea of penalty-based boundary conditions where the constraint is weakly enforced by providing a large penalty for violation of the condition. This is very flexible as the penalizing conditions can be adjusted during the run, including changing which part of the boundary is subject to constraints based on the solution or a coupled problem. The channel flow model shown below has a boundary condition that depends on a externally sourced model for ponded material at the base that is derived from a simple topography filling algorithm.
+
+```{=html}
+<center>
+<iframe src="media/pyvista/ChannelFlow.html" width="600" height="300">
+</iframe>
+</center>
+```
+*Live Image: Stokes flow in a channel with multiple obstructions. Flow is driven from the inlet (a velocity boundary condition). The geometry was contructed with `gmsh`. This is an example for education which demonstrates the emergence of an large-scale pressure gradient as a result of the presence of the obstructions, and also the dispersion of tracers through the complicated flow geometry*
+
+The penalty approach does allow the solution to deviate from the exact value of the boundary condition, in a similar way to the iterative solvers delivering a solution to within a specified tolerance. There are some cases, for example, enforcing plate motions at the surface, where there are uncertainties in the applied boundary conditions and that these uncertainties may vary in space and time.
+
+#### Mesh Adaptation
+
+It is also possible to use the PETSc mesh adaption capabilities, to refine the resolution of a model where it is most needed and coarsen elsewhere. Dynamically-adapting meshes are possible but the interface is very low level at present, particularly in parallel.
+
+```{=html}
+<center>
+<iframe src="media/pyvista/AdaptedSphere.html" width="600" height="300">
+</iframe>
+</center>
+```
+*Live Image: Static mesh adaptation to the slope of a field. The driving buoyancy term is three plume-like upwellings and the slope of this field is shown in colour (red high, blue low). The adapted mesh is shown in green.*
 
+```python
 
+    # t is the driving "temperature". We form an isotropic refinement metric from its slope
 
-<!--
-```{iframe} https://github.com/underworldcode/underworld3/raw/VEP-preliminary-implementation/Documentation/media/Sinker.html
-:width: 100%
-:align: center
+    refinement_fn = 1.0 + sympy.sqrt(
+          t.diff(x) ** 2
+        + t.diff(y) ** 2
+        + t.diff(z) ** 2
+    )
 
-Caption
+    icoord, meshA = adaptivity.mesh_adapt_meshVar(mesh0, refinement_fn, Metric, redistribute=True)
 ```
--->
diff --git a/docs/user/Notebooks/.jupyter/desktop-settings.json b/docs/user/Notebooks/.jupyter/desktop-settings.json
index a170a30c4..1e2f9b63e 100644
--- a/docs/user/Notebooks/.jupyter/desktop-settings.json
+++ b/docs/user/Notebooks/.jupyter/desktop-settings.json
@@ -1,3 +1,3 @@
 {
-  "pythonPath": "/Users/lmoresi/mambaforge/envs/underworld3p/bin/python3"
+  "pythonPath": "/Users/lmoresi/mambaforge/envs/underworld3/bin/python"
 }
\ No newline at end of file
diff --git a/docs/user/Notebooks/1-Meshes.ipynb b/docs/user/Notebooks/1-Meshes.ipynb
index 946eae9a8..3a8328be0 100644
--- a/docs/user/Notebooks/1-Meshes.ipynb
+++ b/docs/user/Notebooks/1-Meshes.ipynb
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "5a4437ff-63ae-4ffa-9f2b-8ec1b8b4baeb",
    "metadata": {
     "editable": true,
@@ -74,15 +74,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[esdhcp-137.anu.edu.au:81197] shmem: mmap: an error occurred while determining whether or not /var/folders/tx/95gr762j29z4tt5d1dnqlgth0000gn/T//ompi.esdhcp-137.501/jf.0/1727463424/sm_segment.esdhcp-137.501.66f70000.0 could be created.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| output: false # Suppress warnings in html version\n",
     "\n",
@@ -274,7 +266,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x17c2f2c10>"
+       "<IPython.lib.display.IFrame at 0x155c7fc90>"
       ]
      },
      "execution_count": 6,
@@ -476,7 +468,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/2-Variables.ipynb b/docs/user/Notebooks/2-Variables.ipynb
index 64aaa51ac..9920ce140 100644
--- a/docs/user/Notebooks/2-Variables.ipynb
+++ b/docs/user/Notebooks/2-Variables.ipynb
@@ -367,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/3-Symbolic_Forms.ipynb b/docs/user/Notebooks/3-Symbolic_Forms.ipynb
index 13e18383e..90c71611b 100644
--- a/docs/user/Notebooks/3-Symbolic_Forms.ipynb
+++ b/docs/user/Notebooks/3-Symbolic_Forms.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "id": "5a4437ff-63ae-4ffa-9f2b-8ec1b8b4baeb",
    "metadata": {
     "editable": true,
@@ -46,15 +46,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Lyrebird.local:19272] shmem: mmap: an error occurred while determining whether or not /var/folders/tx/95gr762j29z4tt5d1dnqlgth0000gn/T//ompi.Lyrebird.501/jf.0/95551488/sm_segment.Lyrebird.501.5b20000.0 could be created.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import underworld3 as uw\n",
     "import numpy as np\n",
@@ -329,7 +321,31 @@
     {
      "data": {
       "text/markdown": [
-       "${ \\nabla \\times \\left(\\cal{S} \\mathbf{v}\\right) \\hspace{ 0.0pt } }$"
+       "**Class**: <class 'underworld3.function.expressions.UWexpression'>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\quad$$\\displaystyle { \\nabla \\times \\left(\\cal{S} \\mathbf{v}\\right) \\hspace{ 0.0pt } }$$=$$\\displaystyle - {\\cal{S}}(\\mathbf{x}) {\\mathbf{v}}_{ 0,1}(\\mathbf{x}) + {\\cal{S}}(\\mathbf{x}) {\\mathbf{v}}_{ 1,0}(\\mathbf{x}) + {\\cal{S}}_{,0}(\\mathbf{x}) {\\mathbf{v}}_{ 1 }(\\mathbf{x}) - {\\cal{S}}_{,1}(\\mathbf{x}) {\\mathbf{v}}_{ 0 }(\\mathbf{x})$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "$\\quad$**Description:**  Curl of {\\mathbf{v}}"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -368,10 +384,10 @@
     "curl_sv = uw.function.expression(\n",
     "                    r\"\\nabla \\times \\left(\\cal{S} \\mathbf{v}\\right)\",\n",
     "                    mesh.vector.curl(s.sym * v.sym),\n",
-    "                    f\"Curl of {v.symbol}\"\n",
+    "                    rf\"Curl of {v.symbol}\"\n",
     "                ) \n",
     "\n",
-    "display(curl_sv)\n",
+    "curl_sv.view()\n",
     "display(curl_sv.sym) \n",
     "\n",
     "curl_sv + 1"
@@ -603,7 +619,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x302cfdc90>"
+       "<IPython.lib.display.IFrame at 0x152a4eb10>"
       ]
      },
      "execution_count": 15,
@@ -810,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "59a1273d-ee09-479a-9788-d2e6b399e9b8",
    "metadata": {},
    "outputs": [],
@@ -833,7 +849,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/4-Solvers.ipynb b/docs/user/Notebooks/4-Solvers.ipynb
index 73b49e442..76963c310 100644
--- a/docs/user/Notebooks/4-Solvers.ipynb
+++ b/docs/user/Notebooks/4-Solvers.ipynb
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "042970c2-ab87-4483-b68c-3fe6b8e633a2",
    "metadata": {
     "editable": true,
@@ -57,15 +57,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[esdhcp-137.anu.edu.au:75016] shmem: mmap: an error occurred while determining whether or not /var/folders/tx/95gr762j29z4tt5d1dnqlgth0000gn/T//ompi.esdhcp-137.501/jf.0/676069376/sm_segment.esdhcp-137.501.284c0000.0 could be created.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| output: false # Suppress warnings in html version\n",
     "\n",
@@ -396,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "id": "b27c04cd-7192-446b-86e5-3f5a1867045a",
    "metadata": {},
    "outputs": [
@@ -421,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "6dccf566-8505-4a6e-94a7-f2869b73e3ff",
    "metadata": {
     "editable": true,
@@ -477,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "4bf2b002-3efd-4f02-907b-c68430f282af",
    "metadata": {
     "editable": true,
@@ -502,10 +494,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x30b65fed0>"
+       "<IPython.lib.display.IFrame at 0x157b68bd0>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -550,7 +542,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/5-Solvers.ipynb b/docs/user/Notebooks/5-Solvers.ipynb
index 03abaf16c..6dbe677ee 100644
--- a/docs/user/Notebooks/5-Solvers.ipynb
+++ b/docs/user/Notebooks/5-Solvers.ipynb
@@ -443,7 +443,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/6-Timestepping.ipynb b/docs/user/Notebooks/6-Timestepping.ipynb
index b3defbc6e..5bcf94dce 100644
--- a/docs/user/Notebooks/6-Timestepping.ipynb
+++ b/docs/user/Notebooks/6-Timestepping.ipynb
@@ -48,18 +48,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "id": "dddf1a25-1d89-4c39-90dc-9ca5a45db5dd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Lyrebird.local:85742] shmem: mmap: an error occurred while determining whether or not /var/folders/tx/95gr762j29z4tt5d1dnqlgth0000gn/T//ompi.Lyrebird.501/jf.0/960299008/sm_segment.Lyrebird.501.393d0000.0 could be created.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| output: false # Suppress warnings in html version\n",
     "\n",
@@ -173,7 +165,7 @@
     "    u_Field=t_soln,\n",
     "    V_fn=v_soln,\n",
     "    solver_name=\"adv_diff\",\n",
-    "    order=1,\n",
+    "    order=2,\n",
     "    verbose=False,\n",
     ")\n",
     "\n",
@@ -334,15 +326,15 @@
      "output_type": "stream",
      "text": [
       "Timestep: 5, time 0.007655378472205266\n",
-      "Timestep: 10, time 0.014726556792977562\n",
-      "Timestep: 15, time 0.018483067262087143\n",
-      "Timestep: 20, time 0.02121125820906285\n",
-      "Timestep: 25, time 0.023815525765879905\n",
-      "Timestep: 30, time 0.026692218876534543\n",
-      "Timestep: 35, time 0.029904104575193727\n",
-      "Timestep: 40, time 0.03330476637324635\n",
-      "Timestep: 45, time 0.0367244116173323\n",
-      "Timestep: 50, time 0.040108815466211735\n"
+      "Timestep: 10, time 0.01387213547299994\n",
+      "Timestep: 15, time 0.017197945909787137\n",
+      "Timestep: 20, time 0.019855789890665786\n",
+      "Timestep: 25, time 0.02250450167934171\n",
+      "Timestep: 30, time 0.02546333347384266\n",
+      "Timestep: 35, time 0.028720014807997008\n",
+      "Timestep: 40, time 0.032113891988035236\n",
+      "Timestep: 45, time 0.035501720967277196\n",
+      "Timestep: 50, time 0.038852418750145654\n"
      ]
     }
    ],
@@ -452,7 +444,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x16a987450>"
+       "<IPython.lib.display.IFrame at 0x1582afe90>"
       ]
      },
      "execution_count": 14,
@@ -473,8 +465,15 @@
    "source": [
     "## Exercise - Null space\n",
     "\n",
-    "Based on our previous notebook, can you see how to remove the rigid-body rotation \n",
-    "null-space from the solution ?\n",
+    "Based on our previous notebook, can you see how to calculate and (if necessary) remove rigid-body the rotation \n",
+    "null-space from the solution ? \n",
+    "\n",
+    "The use of a coarse-level singular-value decomposition for the velocity solver should help, here, but it's wise to \n",
+    "check anyway.\n",
+    "\n",
+    "```python\n",
+    "    stokes.petsc_options[\"fieldsplit_velocity_mg_coarse_pc_type\"] = \"svd\"\n",
+    "```\n",
     "\n",
     "## Exercise - Heat flux\n",
     "\n",
@@ -498,23 +497,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f76dfeb-5c4a-4c4c-a790-14ef030f6256",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94e60f1a-7e7f-447a-9539-66c1a29e8da3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a237b652-89a0-4aa7-ad07-14c633de9a5a",
+   "id": "0e5e6ae9-752f-4637-8e8a-9539b6fd2282",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -536,7 +519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/7-Unsteady_Flow.ipynb b/docs/user/Notebooks/7-Unsteady_Flow.ipynb
index 865f69b79..1d612f4e0 100644
--- a/docs/user/Notebooks/7-Unsteady_Flow.ipynb
+++ b/docs/user/Notebooks/7-Unsteady_Flow.ipynb
@@ -471,7 +471,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/8-ParticleSwarms.ipynb b/docs/user/Notebooks/8-Particle_Swarms.ipynb
similarity index 97%
rename from docs/user/Notebooks/8-ParticleSwarms.ipynb
rename to docs/user/Notebooks/8-Particle_Swarms.ipynb
index 6e566ca72..ed4243cb7 100644
--- a/docs/user/Notebooks/8-ParticleSwarms.ipynb
+++ b/docs/user/Notebooks/8-Particle_Swarms.ipynb
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "0974f1a1-60dd-4d52-9855-79e8030450e9",
    "metadata": {},
    "outputs": [],
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "id": "dddf1a25-1d89-4c39-90dc-9ca5a45db5dd",
    "metadata": {},
    "outputs": [],
@@ -195,7 +195,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b567c06831304b4885384029857f9bf3",
+       "model_id": "16110a6f07c942d28b40970a014d0ba4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -323,7 +323,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b5528ee08b945f295f147b2d5f2df21",
+       "model_id": "ddb5d9712b3c4e82b6e2cd5af8d2aaea",
        "version_major": 2,
        "version_minor": 0
       },
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "id": "4948cff8-89da-4822-aee7-f2207aa513b0",
    "metadata": {},
    "outputs": [
@@ -529,8 +529,8 @@
       "text/html": [
        "\n",
        "        <iframe\n",
-       "            width=\"1000\"\n",
-       "            height=\"400\"\n",
+       "            width=\"750\"\n",
+       "            height=\"300\"\n",
        "            src=\"html5/terrain_flow_plot.html\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
@@ -539,10 +539,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x31c29a510>"
+       "<IPython.lib.display.IFrame at 0x1050ee7d0>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -550,17 +550,9 @@
    "source": [
     "#| fig-cap: \"Interactive Image: Convection model output\"\n",
     "from IPython.display import IFrame\n",
-    "IFrame(src=f\"html5/terrain_flow_plot.html\", width=1000, height=400)"
+    "IFrame(src=f\"html5/terrain_flow_plot.html\", width=750, height=300)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "811239df-5cb6-4e2e-a7af-7004fa9d3306",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "c3d1d6ff-68ac-46c0-8a47-e29e95fed462",
@@ -615,7 +607,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/user/Notebooks/Notebook_Index.ipynb b/docs/user/Notebooks/Notebook_Index.ipynb
new file mode 100644
index 000000000..43b3ce695
--- /dev/null
+++ b/docs/user/Notebooks/Notebook_Index.ipynb
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3e09bb55-c60c-402f-b24b-251925da5367",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<div style=\"float: right; width: 30%; padding-left:20px;\">\n",
+    "<img src=\"media/NotebookScreenshot.png\" width=100%>\n",
+    "</div>\n",
+    "\n",
+    "# Tutorials\n",
+    "\n",
+    "Underworld is designed to be run in the jupyter notebook environment where you can take advantage of jupyter's rich display capabilities to explore the mathematics of your problem, visualise results and query classes or live objects.\n",
+    "\n",
+    "You will find it helpful for developing scripts and analysing results. If you use `jupytext` to write notebooks directly as python scripts, then the same notebook codes will run on a parallel machine with no performance penalty.\n",
+    "\n",
+    "The notebooks are rendered as html pages in this guide. They are distributed with the source code on GitHub and can be run on binder.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31afe788-d996-48f7-888c-540b88b3bd79",
+   "metadata": {},
+   "source": [
+    "#### Notebook 1 - Meshes\n",
+    "\n",
+    "**[Meshes](1-Meshes.ipynb)**: Introduces the mesh discretisation that we use in `Underworld3` and how you can build one of the pre-defined meshes. This notebook also show you how to use the `pyvista` visualisation tools for `Underworld3` objects. The mesh holds information on the mesh geometry, boundaries and coordinate systems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f8ef25c-5246-4e24-93b7-4e0c59880015",
+   "metadata": {},
+   "source": [
+    "#### Notebook 2 - Mesh Variables\n",
+    "\n",
+    "**[Variables](2-Variables.ipynb)**: Introduces the concept of `MeshVariables` in `Underworld3`. These are both data containers and `sympy` symbolic objects. We show you how to inspect a `meshVariable`, set the data values in the `MeshVariable` and visualise them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26b5da69-1292-4988-93e4-591bcf607b32",
+   "metadata": {},
+   "source": [
+    "#### Notebook 3 - Symbols and sympy\n",
+    "\n",
+    "[Symbols](3-Symbolic_Forms.ipynb): `meshVariables` are `sympy` objects\n",
+    "that can be composed with other symbolic objects and evaluated numerically when required. They can also be differentiated. Most importantly, `sympy` can manipulate expressions, simplify them and cancel terms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d091b23-709b-435c-ade1-9822ad09d567",
+   "metadata": {},
+   "source": [
+    "#### Notebook 4 - Example: Diffusion Equation\n",
+    "\n",
+    "[Diffusion Solver](4-Solvers.ipynb): Introduces the various solver templates that are available in Underworld, starting with a steady-state diffusion problem. The template requires you to set some constitutive properties and define the unknowns. These are handled through subsitution into symbolic forms and the template equation can be inspected before you need to supply concrete expressions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eff58d66-8892-4af6-92b0-c18ee9cd2ad2",
+   "metadata": {},
+   "source": [
+    "#### Notebook 5 - Example: Stokes Equation\n",
+    "\n",
+    "[Stokes Solver](5-Solvers.ipynb): Stokes equation is a more complicated system of equations to solve. This complexity is mostly hidden when you set the problem up. There are some interesting ways to constrain boundary values which are demonstrated using an annulus mesh (curved, free-slip boundaries) and a $\\delta$ function buoyancy source."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a400f96-5908-4b60-ba0d-ef188c5a66cd",
+   "metadata": {},
+   "source": [
+    "#### Notebook 6 - Example: Time Dependence\n",
+    "\n",
+    "[Timestepping](6-Timestepping.ipynb): *Coupled* Stokes flow plus thermal advection-diffusion gives a simple convection solver. The timestepping loop is written by hand because usually you will want to do some analysis or output some checkpoints."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b784a04-823e-4a88-9b18-a57f763dbf56",
+   "metadata": {},
+   "source": [
+    "#### Notebook 7 - Example: Navier-Stokes Equation\n",
+    "\n",
+    "[Unsteady flow](7-Unsteady_Flow.ipynb)   Using a passive swarm to track the pattern of flow developing in a pipe after an impulsive application of a boundary condition at the inflow. Particles need to be added to the passive swarm close to the inflow at each timestep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f43817-0e0b-458b-9c5b-34799fb60487",
+   "metadata": {},
+   "source": [
+    "#### Notebook 8 - Lagrangian Swarm Variables\n",
+    "\n",
+    "[Swarm Variables](8-Particle_Swarms.ipynb) - exploring how they work for specifying material properties with a swarm used to determine element viscosity. We learn how to use swarm variables in expressions generally and for boundary conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e39f57-b809-4ba6-821d-7bf57883ef32",
+   "metadata": {},
+   "source": [
+    "<!--\n",
+    "Notebook 9 - Shear banding\n",
+    "\n",
+    "-->"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f485a206-2280-4789-8645-93148511562f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/user/Notebooks/html5/annulus_convection_plot.html b/docs/user/Notebooks/html5/annulus_convection_plot.html
index 38a7871c4..af9d28606 100644
--- a/docs/user/Notebooks/html5/annulus_convection_plot.html
+++ b/docs/user/Notebooks/html5/annulus_convection_plot.html
@@ -7,11 +7,11 @@
   <body>
     <div class="content"></div>
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r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof 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t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var 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Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const g=t.numberOfColorsInRange+3,m=t.numberOfColorsInRange+2,h=r?[Math.min(Math.ceil(g/n**0),n),Math.min(Math.ceil(g/n**1),n),Math.min(Math.ceil(g/n**2),n)]:[m,2,1],v=h[0]*h[1]*h[2],y=new Float64Array(v);y.fill(NaN);const T=t.numberOfColorsInRange,b=T+2,x=[0,0,0],C=a[0],S=a[1]-a[0];for(let e=0;e<b;++e){const t=C+S*(e-1)/(T-1);y[(c=h,(l=x)[0]+c[0]*(l[1]+c[1]*l[2]))]=i?10**t:t,ec(x,h)}const A=Ds.newInstance({numberOfComponents:1,values:y}),I=t.lookupTable.mapScalars(A,t.colorMode,0);t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setDimensions(h),t.colorTextureMap.getPointData().setScalars(I),t.lookupTable.setAlpha(s)}var l,c;const u=t.lookupTable.getVectorMode()===Zl.MAGNITUDE&&n.getNumberOfComponents()>1?-1:t.lookupTable.getVectorComponent();t.colorCoordinates=function(e,t,n,r,o,a,i){const s=new Array(arguments.length);for(let e=0;e<arguments.length;++e){const t=arguments[e];s[e]=t.getMTime?.()??t}const l=s.join("/"),c=nc.get(e);if(c&&c.stringHash===l)return c.textureCoordinates;const u=(n[1]-n[0])/(r-1),[d,p]=[n[0]-u,n[1]+u],f=d-.5*u,g=1/(p-d+u),m=d,h=(r+1)/(p-d),v=e.getData(),y=e.getNumberOfTuples(),T=e.getNumberOfComponents(),b=t<0||t>=T,x=o[2]<=1?2:3,C=Ds.newInstance({numberOfComponents:x,values:new Float32Array(y*x)}),S=C.getData(),A=[0,0,0];tc(A,r+2,o);let I=0,w=0;const P=[.5,.5,.5];for(let e=0;e<y;++e){let e;if(b){let t=0;for(let e=0;e<T;++e){const n=v[I+e];t+=n*n}e=Math.sqrt(t)}else e=v[I+t];if(I+=T,a&&(e=Dl.applyLogScale(e,n,n)),_a(e))P[0]=A[0],P[1]=A[1],P[2]=A[2];else if(i){let t=(e-m)*h;t<1?t=0:t>r&&(t=r+1),tc(P,t,o)}else{P[1]=.49;const t=(e-f)*g;P[0]=t>1e3?1e3:t<-1e3?-1e3:t}for(let e=0;e<x;++e)S[w++]=P[e]}return nc.set(e,{stringHash:l,textureCoordinates:C}),C}(n,u,a,t.numberOfColorsInRange,t.colorTextureMap.getDimensions(),i,r)},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=(e,n)=>!((!n||t.colorMode===ql.DIRECT_SCALARS)&&(!t.interpolateScalarsBeforeMapping||t.lookupTable&&t.lookupTable.getIndexedLookup()||!e||t.colorMode===ql.DEFAULT&&e.getDataType()===Ql.UNSIGNED_CHAR||t.colorMode===ql.DIRECT_SCALARS)),e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Jl("AcquireInvertibleLookupTable"),e.valueToColor=Jl("ValueToColor"),e.colorToValue=Jl("ColorToValue"),e.useInvertibleColorFor=Jl("UseInvertibleColorFor"),e.clearInvertibleColor=Jl("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(Wl.ID_LOW24),o=e.getRawPixelBuffer(Wl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===jl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===jl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===Wl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(Wl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===Wl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(Wl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const oc={colorMapColors:null,areScalarsMappedFromCells:!1,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,numberOfColorsInRange:0,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function ac(e,t){let n=arguments.length>2&&void 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0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=lc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=sc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function dc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function pc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Ds.extend(e,t,dc(n)),uc(e,t)}var 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h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[1]<0&&o[0]<0)for(T=Yo(e,c),b=eu.distanceToLine(e,c,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=c):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[0]<0){const t=eu.distanceToLine(e,l,c,n);a.dist2=t.distance}else if(o[1]<0){const t=eu.distanceToLine(e,c,u,n);a.dist2=t.distance}else if(o[2]<0){const t=eu.distanceToLine(e,l,u,n);a.dist2=t.distance}a.evaluation=0}return a},e.evaluateLocation=(e,n,r)=>{const o=[],a=[],i=[];t.points.getPoint(0,o),t.points.getPoint(1,a),t.points.getPoint(2,i);const s=1-e[0]-e[1];for(let t=0;t<3;t++)n[t]=o[t]*s+a[t]*e[0]+i[t]*e[1];r[0]=s,r[1]=e[0],r[2]=e[1]},e.getParametricDistance=e=>{let t,n=0;const r=[];r[0]=e[0],r[1]=e[1],r[2]=1-e[0]-e[1];for(let e=0;e<3;e++)t=r[e]<0?-r[e]:r[e]>1?r[e]-1:0,t>n&&(n=t);return n}}const cu={};function uu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cu,n),Sc.extend(e,t,n),lu(e,t)}var du={newInstance:jt.newInstance(uu,"vtkTriangle"),extend:uu,...su};const pu=["verts","lines","polys","strips"],{vtkWarningMacro:fu}=jt,gu={[Ec]:eu,[Dc]:eu,[Vc]:du};function mu(e,t){t.classHierarchy.push("vtkPolyData"),pu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=fc.newInstance()})),e.getNumberOfCells=()=>pu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),pu.forEach((n=>{t[n]=fc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?Mc:Rc,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Dc:Ec,1===t&&fu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. Check the input."),e+=t+1})),u=u.subarray(r),l=l.subarray(r)}if(o){let e=0;t.polys.getCellSizes().forEach(((t,n)=>{switch(u[n]=e,t){case 3:l[n]=Vc;break;case 4:l[n]=Nc;break;default:l[n]=Bc}t<3&&fu("Building VTK_TRIANGLE ",n," with less than three points, but VTK_TRIANGLE needs at least three points. Check the input."),e+=t+1})),u+=u.subarray(o),l+=l.subarray(o)}if(a){let e=0;l.fill(Lc,0,a),t.strips.getCellSizes().forEach(((t,n)=>{u[n]=e,e+=t+1}))}t.cells=jc.newInstance(),t.cells.setCellTypes(i,s,c)},e.buildLinks=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;void 0===t.cells&&e.buildCells(),t.links=Pc.newInstance(),n>0?t.links.allocate(n):t.links.allocate(e.getPoints().getNumberOfPoints()),t.links.buildLinks(e)},e.getCellType=e=>t.cells.getCellType(e),e.getCellPoints=n=>{const r=e.getCellType(n);let o=null;switch(r){case Rc:case Mc:o=t.verts;break;case Ec:case Dc:o=t.lines;break;case Vc:case Nc:case Bc:o=t.polys;break;case Lc:o=t.strips;break;default:return o=null,{type:0,cellPointIds:null}}const a=t.cells.getCellLocation(n);return{cellType:r,cellPointIds:o.getCell(a)}},e.getPointCells=e=>t.links.getCells(e),e.getCellEdgeNeighbors=(e,n,r)=>{const o=t.links.getLink(n),a=t.links.getLink(r);return o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||gu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const hu={};function vu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hu,n),ou.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),mu(e,t)}var yu={newInstance:jt.newInstance(vu,"vtkPolyData"),extend:vu};function Tu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const bu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function xu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bu,n),jt.obj(e,t),jt.algo(e,t,6,0),jt.get(e,t,["canvas","image","jsImageData","imageLoaded","resizable"]),jt.setGet(e,t,["repeat","edgeClamp","interpolate","mipLevel"]),Tu(e,t)}const Cu={generateMipmaps:(e,t,n,r)=>{const o=[1,2,1],a=e.length/(t*n);let i=t,s=n,l=e;const c=[l];for(let e=0;e<r;e++){const e=[...l];i/=2,s/=2,l=new Uint8ClampedArray(i*s*a);const t=a*i;let n=0;for(let r=0;r<l.length;r+=a){r%t==0&&(n+=2*a*i);for(let o=0;o<a;o++){let i=e[n+o];i+=e[n+a+o],i+=e[n-2*t+o],i+=e[n-2*t+a+o],i/=4,l[r+o]=i}n+=2*a}let r=[...l];for(let e=0;e<l.length;e+=a)for(let n=0;n<a;n++){let i=-(o.length-1)/2,s=4,c=0;for(let l=0;l<o.length;l++){let u=e+n+i*a;const d=u%t-(e+n)%t;d>a&&(u+=t),d<-a&&(u-=t),r[u]?c+=r[u]*o[l]:s-=o[l],i+=1}l[e+n]=c/s}r=[...l];for(let e=0;e<l.length;e+=a)for(let n=0;n<a;n++){let a=-(o.length-1)/2,i=4,s=0;for(let l=0;l<o.length;l++){const c=e+n+a*t;r[c]?s+=r[c]*o[l]:i-=o[l],a+=1}l[e+n]=s/i}c.push(l)}return c}};var Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=gd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const 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input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const 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o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader VAO."))),n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),r.getIsIdentity())o.isUniformUsed("MCVCMatrix")&&o.setUniformMatrix("MCVCMatrix",i.wcvc),o.isUniformUsed("normalMatrix")&&o.setUniformMatrix3x3("normalMatrix",i.normalMatrix);else{const e=t.openGLActor.getKeyMatrices();if(o.isUniformUsed("MCVCMatrix")){const t=new Float64Array(16);T(t,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",t)}if(o.isUniformUsed("normalMatrix")){const t=new Float64Array(9);ve(t,i.normalMatrix,e.normalMatrix),o.setUniformMatrix3x3("normalMatrix",t)}}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():Mm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let n=t.renderable.getLength(),r=t.renderable.getRadius();m&&(n=m[2*e],r=m[2*e+1]);for(let t=0;t<v.length;++t)y=3*e,f[b++]=c[y++],f[b++]=c[y++],f[b++]=c[y++],y=3*e,f[b++]=h[y++]*n,f[b++]=h[y++]*n,f[b++]=h[y++]*n,f[b++]=r,g[x++]=v[t]%2*255,g[x++]=v[t]>=4?255:0,g[x++]=v[t]>=2?255:0,g[x++]=255,T=e*p,d&&(g[x++]=d[T],g[x++]=d[T+1],g[x++]=d[T+2],g[x++]=d[T+3])}a.setElementCount(b/7),a.upload(f,$u.ARRAY_BUFFER),a.getColorBO().upload(g,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Dm={};const Vm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dm,n),sp.extend(e,t,n),Em(e,t)}),"vtkOpenGLStickMapper");rn("vtkStickMapper",Vm);const Lm=[];Lm["-".charCodeAt(0)]=62,Lm["_".charCodeAt(0)]=63;const Bm="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";for(let e=0;e<Bm.length;e++)Lm[Bm.charCodeAt(e)]=e;function Nm(e){return void 0!==Lm[e.charCodeAt(0)]}function _m(e,t,n,r){const{start:o,count:a}=t,i=a%4,s=Math.floor(a/4);let l=o,c=null,u=n;for(let t=0;t<s;t++){for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<18;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<12;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<6;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)],r[u++]=c>>16&255,r[u++]=c>>8&255,r[u++]=255&c}switch(i){case 3:for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<10;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<4;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)]>>2,r[u++]=c>>8&255,r[u++]=255&c;break;case 2:for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<2;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)]>>4,r[u++]=255&c;break;case 1:throw new Error("BASE64: remain 1 should not happen")}return u}function Fm(e,t,n){const r=(e<<16)+(t<<8)+n;return Bm[r>>18]+Bm[r>>12&63]+Bm[r>>6&63]+Bm[63&r]}function km(e){const t=new Uint8Array(e),n=e.byteLength%3,r=e.byteLength-n,o=Array(r/3);for(let e=0;e<o.length;e++){const n=3*e;o[e]=Fm(t[n],t[n+1],t[n+2])}if(n>0){const 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r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return tv("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return tv("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return tv("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return tv("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),ev("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return ev("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return tv("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return a[1]-a[0]<i&&(i=i-a[1]+a[0],a[1]+=i/2,a[0]-=i/2),a[0]<0&&(a[0]=0),a[0]=.99*a[0]-(a[1]-a[0])*t.clippingRangeExpansion,a[1]=1.01*a[1]+(a[1]-a[0])*t.clippingRangeExpansion,a[0]=a[0]>=a[1]?.01*a[1]:a[0],t.nearClippingPlaneTolerance||(t.nearClippingPlaneTolerance=.01),a[0]<t.nearClippingPlaneTolerance*a[1]&&(a[0]=t.nearClippingPlaneTolerance*a[1]),t.activeCamera.setClippingRange(a[0],a[1]),e.invokeEvent(r),!1},e.setRenderWindow=e=>{e!==t._renderWindow&&(t._vtkWindow=e,t._renderWindow=e)},e.visibleActorCount=()=>t.props.filter((e=>e.getVisibility())).length,e.visibleVolumeCount=e.visibleActorCount,e.getMTime=()=>{let e=t.mtime;const n=t.activeCamera?t.activeCamera.getMTime():0;n>e&&(e=n);const r=t._createdLight?t._createdLight.getMTime():0;return r>e&&(e=r),e},e.getTransparent=()=>!!t.preserveColorBuffer,e.isActiveCameraCreated=()=>!!t.activeCamera}const av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&Pv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:xv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an animation`)}},e.switchToXRAnimation=()=>{t.animationRequest&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null),t.xrAnimation=!0},e.returnFromXRAnimation=()=>{t.xrAnimation=!1,0!==r.size&&(t.recentAnimationFrameRate=10,t.animationRequest=requestAnimationFrame(e.handleAnimation))},e.updateXRGamepads=(n,r,o)=>{n.inputSources.forEach((n=>{const a=null==n.gripSpace?null:r.getPose(n.gripSpace,o),i=null==n.gripSpace?null:r.getPose(n.targetRaySpace,o),s=n.gamepad,l=n.handedness;if(s){s.index in t.lastGamepadValues||(t.lastGamepadValues[s.index]={left:{buttons:{}},right:{buttons:{}},none:{buttons:{}}});for(let r=0;r<s.buttons.length;++r)r in t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const r={...Cv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:xv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Ov(o);if(2===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(Av),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Ov(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(Av),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Ov(o);e.recognizeGesture("TouchEnd",t);const a={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Ov(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},wv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void Sv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const ty={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function ny(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ty,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),ey(e,t)}var ry={newInstance:jt.newInstance(ny,"vtkRenderPass"),extend:ny};const{Representation:oy}=ds,{vtkErrorMacro:ay}=jt;function iy(e){const t=gd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function sy(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Ds.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Ds.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",oy.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||ay("Error setting vertexDC in copy shader 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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a;e.onModified((function(){t.renderable&&(t.size[0]===o[0]&&t.size[1]===o[1]||(o[0]=t.size[0],o[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]))),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.addMissingNodes(t.renderable.getChildRenderWindowsByReference()),e.removeUnusedNodes(),e.initialize(),t.children.forEach((t=>{t.setOpenGLRenderWindow?.(e)}))}},e.initialize=()=>{if(!t.initialized){if(t.rootOpenGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.rootOpenGLRenderWindow)t.context2D=e.get2DContext();else{t.context=e.get3DContext(),e.resizeFromChildRenderWindows(),t.context&&(Ny++,_y.forEach((e=>e(Ny)))),t.textureUnitManager=wy.newInstance(),t.textureUnitManager.setContext(t.context),t.shaderCache.setContext(t.context);const 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rendererUBO.SCPCMatrix*vertexBC;"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderTCoord=(e,t,n)=>{t.getShaderDescription("vertex").addOutput("vec2<f32>","tcoordVS")},t.shaderReplacements.set("replaceShaderTCoord",e.replaceShaderTCoord),e.addTextureView=e=>{t.textureViews.includes(e)||t.textureViews.push(e)},e.prepareToDraw=n=>{t.renderEncoder=n,e.updateInput(),e.updateBuffers(),e.updateBindings(),e.updatePipeline()},e.updateInput=()=>{},e.updateBuffers=()=>{},e.updateBindings=()=>{t.bindGroup.setBindables(e.getBindables())},e.computePipelineHash=()=>{},e.registerDrawCallback=n=>{n.registerDrawCallback(t.pipeline,e.draw)},e.prepareAndDraw=n=>{e.prepareToDraw(n),n.setPipeline(t.pipeline),e.draw(n)},e.draw=e=>{const n=e.getBoundPipeline();e.activateBindGroup(t.bindGroup),t.WebGPURenderer&&t.WebGPURenderer.bindUBO(e),n.bindVertexInput(e,t.vertexInput);const r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pb,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),db(e,t)}var gb={newInstance:jt.newInstance(fb),extend:fb};function mb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const r=fT(n);t.bufferEntries.push({name:e,type:n,sizeInBytes:r,offset:t.sizeInBytes,nativeType:gT(n)}),t.sizeInBytes+=r},e.send=e=>{if(!t._buffer){const n={nativeArray:t.Float32Array,usage:Fb.Storage,label:t.label};return t._buffer=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),void t._sendTime.modified()}e.getHandle().queue.writeBuffer(t._buffer.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes*t.numberOfInstances),t._sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes*t.numberOfInstances)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r,o)=>{const a=t._bufferEntryNames.get(n);if(void 0===a)return void kb(`entry named ${n} not found in UBO`);const i=t.bufferEntries[a];e.createView(i.nativeType);const s=t[i.nativeType];s[(i.offset+r*t.sizeInBytes)/s.BYTES_PER_ELEMENT]=o},e.setArray=(n,r,o)=>{const a=t._bufferEntryNames.get(n);if(void 0===a)return void kb(`entry named ${n} not found in UBO`);const i=t.bufferEntries[a];e.createView(i.nativeType);const s=t[i.nativeType],l=(i.offset+r*t.sizeInBytes)/s.BYTES_PER_ELEMENT;for(let e=0;e<o.length;e++)s[l+e]=o[e]},e.setAllInstancesFromArray=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType],s=r.length/t.numberOfInstances;for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<s;t++)i[n+t]=r[e*s+t]}},e.setAllInstancesFromArrayColorToFloat=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType],s=r.length/t.numberOfInstances;for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<s;t++)i[n+t]=r[e*s+t]/255}},e.setAllInstancesFromArray3x3To4x4=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],D=r[2],E=Math.hypot(R,M,D);return E<a?null:(R*=E=1/E,M*=E,D*=E,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+D*o,C=D*R*s-M*o,S=R*M*s-D*o,A=M*M*s+i,I=D*M*s+R*o,w=R*D*s+M*o,P=M*D*s-R*o,O=D*D*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),E(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,D=(1-(p+v))*C,E=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=D,e[6]=E,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+D*I+L*w),e[14]=n[2]+w-(R*A+E*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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Qi(e,t){t.classHierarchy.push("vtkProp3D"),e.addPosition=n=>{t.position=t.position.map(((e,t)=>e+n[t])),e.modified()},e.getOrientationWXYZ=()=>{const e=Ui();N(e,t.rotation);const n=new Float64Array(3),r=function(e,t){var n=2*Math.acos(t[3]),r=Math.sin(n/2);return r>a?(e[0]=t[0]/r,e[1]=t[1]/r,e[2]=t[2]/r):(e[0]=1,e[1]=0,e[2]=0),n}(n,e);return[Ro(r),n[0],n[1],n[2]]},e.rotateX=n=>{0!==n&&(S(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateY=n=>{0!==n&&(A(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateZ=n=>{0!==n&&(I(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateWXYZ=(n,r,o,a)=>{if(0===n||0===r&&0===o&&0===a)return;const i=Oo(n),s=Ui();zi(s,[r,o,a],i);const l=new 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Es,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&t.getDataType()===Tl.UNSIGNED_CHAR||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Es.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Es.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Es.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function Dl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var El={newInstance:jt.newInstance(Dl,"vtkLookupTable"),extend:Dl};let Vl=1,Ll=0;const Bl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function Nl(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;Ll=e}var _l={getResolveCoincidentTopologyAsString:function(){return Bl[Ll]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Vl},getResolveCoincidentTopology:function(){return Ll},setResolveCoincidentTopology:Nl,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){Vl=e},setResolveCoincidentTopologyToDefault:function(){Nl(0)},setResolveCoincidentTopologyToOff:function(){Nl(0)},setResolveCoincidentTopologyToPolygonOffset:function(){Nl(1)}};function Fl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=(e,r)=>{t[n.key]={factor:e,offset:r}}}))}const kl=["Polygon","Line","Point"],Gl={};Fl(Gl,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},kl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var Ul={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(_l).forEach((t=>{e[t]=_l[t]})),Object.keys(Gl).forEach((t=>{e[t]=Gl[t]})),Fl(e,t.topologyOffset,kl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Gl.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Gl,otherStaticMethods:_l,CATEGORIES:kl};const zl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Wl={PassTypes:zl};const{FieldAssociations:Hl}=ol,{staticOffsetAPI:jl,otherStaticMethods:Kl}=Ul,{ColorMode:$l,ScalarMode:ql,GetArray:Xl}=hl,{VectorMode:Yl}=ml,{VtkDataTypes:Zl}=Es;function Ql(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function Jl(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString($l,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(ql,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===ql.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===ql.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===ql.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===ql.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_FIELD_DATA){const t=e.getFieldData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==a){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(n))e.mapScalarsToTexture(o,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},e.scalarToTextureCoordinate=(e,t,n)=>{let r=.5,o=1;return _a(e)||(o=.49,r=(e-t)*n,r>1e3?r=1e3:r<-1e3&&(r=-1e3)),{texCoordS:r,texCoordT:o}},e.createColorTextureCoordinates=(t,n,r,o,a,i,s,l,c)=>{const u=(i[1]-i[0])/l,d=[];d[0]=i[0]-u,d[1]=i[1]+u;const p=1/(d[1]-d[0]),f=n.getData(),g=t.getData();let m=0,h=0;if(a<0||a>=o)for(let t=0;t<r;++t){let t=0;for(let e=0;e<o;++e)t+=g[m]*g[m],m++;let n=Math.sqrt(t);c&&(n=El.applyLogScale(n,s,i));const r=e.scalarToTextureCoordinate(n,d[0],p);f[h]=r.texCoordS,f[h+1]=r.texCoordT,h+=2}else{m+=a;for(let t=0;t<r;++t){let t=g[m];c&&(t=El.applyLogScale(t,s,i));const n=e.scalarToTextureCoordinate(t,d[0],p);f[h]=n.texCoordS,f[h+1]=n.texCoordT,h+=2,m+=o}}},e.mapScalarsToTexture=(n,r)=>{const o=t.lookupTable.getRange(),a=t.lookupTable.usingLogScale();a&&El.getLogRange(o,o);const i=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==r){t.lookupTable.setAlpha(r),t.colorTextureMap=null,t.lookupTable.build();let e=t.lookupTable.getNumberOfAvailableColors();e>4094&&(e=4094),e<64&&(e=64),e+=2;const n=(o[1]-o[0])/(e-2),s=new Float64Array(2*e);for(let t=0;t<e;++t)s[t]=o[0]+t*n-n/2,a&&(s[t]=10**s[t]);for(let t=0;t<e;++t)s[t+e]=NaN;t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setExtent(0,e-1,0,1,0,0);const l=Es.newInstance({numberOfComponents:1,values:s});t.colorTextureMap.getPointData().setScalars(t.lookupTable.mapScalars(l,t.colorMode,0)),t.lookupTable.setAlpha(i)}if(!t.colorCoordinates||e.getMTime()>t.colorCoordinates.getMTime()||e.getInputData(0).getMTime()>t.colorCoordinates.getMTime()||t.lookupTable.getMTime()>t.colorCoordinates.getMTime()){t.colorCoordinates=null;const r=n.getNumberOfComponents(),i=n.getNumberOfTuples();t.colorCoordinates=Es.newInstance({numberOfComponents:2,values:new Float32Array(2*i)});let s=t.lookupTable.getVectorComponent();t.lookupTable.getVectorMode()===Yl.MAGNITUDE&&n.getNumberOfComponents()>1&&(s=-1),e.createColorTextureCoordinates(n,t.colorCoordinates,i,r,s,o,t.lookupTable.getRange(),t.colorTextureMap.getPointData().getScalars().getNumberOfTuples()/2-2,a)}},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=n=>{if(!t.interpolateScalarsBeforeMapping)return!1;if(t.lookupTable&&t.lookupTable.getIndexedLookup())return!1;const r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName),o=r.scalars;return!(!o||r.cellFlag||t.colorMode===$l.DEFAULT&&o.getDataType()===Zl.UNSIGNED_CHAR||t.colorMode===$l.DIRECT_SCALARS)},e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Ql("AcquireInvertibleLookupTable"),e.valueToColor=Ql("ValueToColor"),e.colorToValue=Ql("ColorToValue"),e.useInvertibleColorFor=Ql("UseInvertibleColorFor"),e.clearInvertibleColor=Ql("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(zl.ID_LOW24),o=e.getRawPixelBuffer(zl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===Hl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===Hl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===zl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(zl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===zl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(zl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const ec={colorMapColors:null,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function tc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ec,n),Fs(e,t,n),jt.get(e,t,["colorCoordinates","colorMapColors","colorTextureMap","selectionWebGLIdsToVTKIds"]),jt.setGet(e,t,["colorByArrayName","arrayAccessMode","colorMode","fieldDataTupleId","interpolateScalarsBeforeMapping","lookupTable","populateSelectionSettings","renderTime","scalarMode","scalarVisibility","static","useLookupTableScalarRange","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),Ul.implementCoincidentTopologyMethods(e,t),Jl(e,t)}var nc={newInstance:jt.newInstance(tc,"vtkMapper"),extend:tc,...jl,...Kl,...hl};function rc(e){let t=0;return e.filter(((e,n)=>n===t&&(t+=e+1,!0)))}function oc(e){let t=0;for(let n=0;n<e.length;)n+=e[n]+1,t++;return t}const ac={extractCellSizes:rc,getNumberOfCells:oc};function ic(e,t){t.classHierarchy.push("vtkCellArray");const n={...e};e.getNumberOfCells=n=>void 0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=oc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=rc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function sc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function lc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Es.extend(e,t,sc(n)),ic(e,t)}var cc={newInstance:jt.newInstance(lc,"vtkCellArray"),extend:lc,...ac};const{vtkErrorMacro:uc}=jt,dc=[1,-1,1,-1,1,-1];function pc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return uc(`getBounds called on an array with components of\n        ${e.getNumberOfComponents()}`),dc;const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);return t.bounds[2]=r[0],t.bounds[3]=r[1],t.bounds[4]=0,t.bounds[5]=0,t.bounds},e.computeBounds=e.getBounds,e.setNumberOfComponents(t.numberOfComponents<2?3:t.numberOfComponents)}const fc={empty:!0,numberOfComponents:3,dataType:ys.FLOAT,bounds:[1,-1,1,-1,1,-1]};function gc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fc,n),Es.extend(e,t,n),pc(e,t)}var mc={newInstance:jt.newInstance(gc,"vtkPoints"),extend:gc};function hc(e,t){t.classHierarchy.push("vtkCell"),e.initialize=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n){t.pointsIds=n;let r=t.points.getData();r.length!==3*t.pointsIds.length&&(r=jt.newTypedArray(e.getDataType(),3*t.pointsIds.length));const o=e.getData();t.pointsIds.forEach(((e,t)=>{let n=3*e,a=3*t;r[a]=o[n],r[++a]=o[++n],r[++a]=o[++n]})),t.points.setData(r)}else{t.points=e,t.pointsIds=new 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let 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Ac=0,Ic=1,wc=2,Pc=3,Oc=4,Rc=5,Mc=6,Dc=7,Ec=9,Vc=21,Lc=41,Bc=42,Nc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],_c={getClassNameFromTypeId:function(e){return e<Nc.length?Nc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Nc.findIndex(e)},isLinear:function(e){return e<Vc||e===Lc||e===Bc},hasSubCells:function(e){return e===Mc||e===Oc||e===wc}};function Fc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Ac},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let 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e=P[2];P[2]=P[D],P[D]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function ou(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(nu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=Yc.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),tu(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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su={newInstance:jt.newInstance(iu,"vtkTriangle"),extend:iu,...ru};const lu=["verts","lines","polys","strips"],{vtkWarningMacro:cu}=jt,uu={[Pc]:Yc,[Oc]:Yc,[Rc]:su};function du(e,t){t.classHierarchy.push("vtkPolyData"),lu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=cc.newInstance()})),e.getNumberOfCells=()=>lu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),lu.forEach((n=>{t[n]=cc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?wc:Ic,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Oc:Pc,1===t&&cu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. 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o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||uu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const pu={};function fu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pu,n),eu.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),du(e,t)}var gu={newInstance:jt.newInstance(fu,"vtkPolyData"),extend:fu};function mu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const hu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function vu(e,t){let n=arguments.length>2&&void 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Tu={newInstance:jt.newInstance(vu,"vtkTexture"),extend:vu,...yu};const bu=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],xu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],Cu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Su=[0,1,0,1,0,1,0,1,2,2,2,2],Au=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Iu=new Float64Array(3),wu=new Float64Array(3),Pu=new Float64Array(3),Ou=new Float64Array(3),Ru=new Float64Array(3),Mu=new Float64Array(3),Du=new Float64Array(16);function Eu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function Vu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],Vn(Pu,Iu,r),Pu[0]+=.1,Vn(wu,Pu,o),Pn(Ru,wu,Iu),Pu[0]-=.1,Pu[1]+=.1,Vn(wu,Pu,o),Pn(Mu,wu,Iu);for(let e=0;e<3;e++)Ru[e]/=.05*u[0],Mu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],a[0]<-.5?On(Pu,Ru,a[0]*i-l.width):a[0]>.5?On(Pu,Ru,a[0]*i):On(Pu,Ru,a[0]*i-l.width/2),wn(Iu,Iu,Pu),On(Pu,Mu,a[1]*i-l.height/2),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Pu,Ru,l.width),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Pu,Mu,l.height),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Pu,Ru,l.width),Pn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Du,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Iu[0]=f[3*u],Iu[1]=f[3*u+1],Iu[2]=f[3*u+2],Vn(Pu,Iu,n),Iu[0]=f[3*u+3],Iu[1]=f[3*u+4],Iu[2]=f[3*u+5],Vn(Ou,Iu,n),Pn(Pu,Pu,Ou);const r=[Pu[0],Pu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const Lu=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Vu(e,t)}),"vtkCubeAxesActorHelper");function Bu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Iu[s]=t.dataBounds[a]-.1*r*bu[a][s],Iu[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Iu[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Pu,Iu,e),Iu[s]=t.dataBounds[a],Vn(Ou,Iu,e),Pn(Pu,Ou,Pu),Mn(Pu,Pu),i=Pu[2]>o,t.camera.getParallelProjection()||(Mn(Ou,Ou),i=Dn(Ou,Pu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Au[t][0]].length+2*r[Au[t][1]].length,a+=r[Au[t][0]].length+r[Au[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let 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e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[xu[e][t]]++;const a=[],i=[];for(let e=0;e<3;e++){const n=go().domain([t.dataBounds[2*e],t.dataBounds[2*e+1]]);a[e]=n.ticks(5);const r=n.tickFormat(5);i[e]=a[e].map(r)}e.updatePolyData(r,n,a),e.updateTextData(r,n,a,i),(o||t.forceUpdate)&&e.updateTextureAtlas(i)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Eu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Eu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Eu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Nu(e){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:12,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},...e}}function _u(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nu(n)),hs.extend(e,t,n),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Tu.newInstance(),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=nc.newInstance(),t.polyData=gu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=gu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Bu(e,t)}var Fu={newInstance:jt.newInstance(_u,"vtkCubeAxesActor"),extend:_u,newCubeAxesActorHelper:Lu};function ku(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Gu={};const Uu=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gu,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Fu.newCubeAxesActorHelper(),ku(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Uu);const zu={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var Wu={ObjectType:zu};const{ObjectType:Hu}=Wu;function ju(e,t){function n(e){switch(e){case Hu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Hu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Hu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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L=a.vertexOffset;f=function(e){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;)C(A[e],A,e+1),e+=A[e]+1,T++;return t.elementCount=w,e.upload(O,zu.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,zu.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):Xu("Wrong type for coordScale, expected vec3 or null"):Xu("Wrong type for coordShift, expected vec3 or null")}}const Zu={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),qu.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ed(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const nd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),td(e,t)}var od={newInstance:jt.newInstance(rd,"vtkShader"),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ad(`Links failed: ${t.error}`),0):(ad(t.error),0):(ad(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.fragmentShader.getError()),0):(ad(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ad(`Error linking 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No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . 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No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ad("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ad("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const sd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=od.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=od.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=od.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShaderProgram"),extend:ld,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function ud(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==zu.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const dd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function pd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),ud(e,t)}var fd={newInstance:jt.newInstance(pd,"vtkOpenGLVertexArrayObject"),extend:pd};const gd={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function md(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===gd.Points?t.context.POINTS:e===rs.WIREFRAME||n===gd.Lines||n===gd.TrisEdges||n===gd.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===gd.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===gd.Points?2:t.primitiveType===gd.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const hd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=cd.newInstance(),t.VAO=fd.newInstance(),t.CABO=Ju.newInstance(),md(e,t)}var yd={newInstance:jt.newInstance(vd),extend:vd,primTypes:gd};const Td={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},bd={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var xd={Wrap:Td,Filter:bd};const Cd=new Float32Array(1),Sd=new Int32Array(Cd.buffer);var Ad={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Cd[0]=e;const t=Sd[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Id,Filter:wd}=xd,{VtkDataTypes:Pd}=Es,{vtkDebugMacro:Od,vtkErrorMacro:Rd,vtkWarningMacro:Md}=Kt,{toHalf:Dd}=Ad;function Ed(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Pd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Pd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Dd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Ad.toHalf(Ad.fromHalf(e[u][l+s+t])*h*(1-v)+Ad.fromHalf(e[u][l+p+t])*h*v+Ad.fromHalf(e[u][c+s+t])*m*(1-v)+Ad.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Pd.UNSIGNED_SHORT&&e!==Pd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent()),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(wd.LINEAR),e.setMagnificationFilter(wd.LINEAR)):(e.setMinificationFilter(wd.NEAREST),e.setMagnificationFilter(wd.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Id.REPEAT),e.setWrapS(Id.REPEAT),e.setWrapT(Id.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Md("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Od(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Md("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Od("Unsupported internal texture type!"),Od(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Pd.SHORT:case t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Pd.FLOAT:case Pd.VOID:default:return t.context.FLOAT}switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Pd.FLOAT:case Pd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case wd.NEAREST:return t.context.NEAREST;case wd.LINEAR:return t.context.LINEAR;case wd.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case wd.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case wd.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case wd.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Id.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Id.REPEAT:return t.context.REPEAT;case Id.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Pd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Rd("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Pd.UNSIGNED_CHAR),e.getInternalFormat(Pd.UNSIGNED_CHAR,4),e.getFormat(Pd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Pd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Pd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Es.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Es.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Pd.FLOAT||t.useHalfFloat&&(u===Pd.SHORT||u===Pd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Pd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Pd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Pd.UNSIGNED_CHAR;if(u===Pd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Pd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Pd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Pd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Pd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Vd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Id.CLAMP_TO_EDGE,wrapT:Id.CLAMP_TO_EDGE,wrapR:Id.CLAMP_TO_EDGE,minificationFilter:wd.NEAREST,magnificationFilter:wd.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function Ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),Ed(e,t)}const Bd=Et(Ld,"vtkOpenGLTexture");var Nd={newInstance:Bd,extend:Ld,...xd};rn("vtkTexture",Bd);var _d="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Fd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",kd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=cd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},Gd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=cd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Ud}=ol,{primTypes:zd}=yd,{Representation:Wd,Shading:Hd}=ds,{ScalarMode:jd}=nc,{Filter:Kd,Wrap:$d}=Nd,{vtkErrorMacro:qd}=Kt,Xd={type:"StartEvent"},Yd={type:"EndEvent"},{CoordinateSystem:Zd}=Zi;function Qd(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=zd.Start;e<zd.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=cd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?t.renderable.getInterpolateScalarsBeforeMapping()&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=cd.substitute(i,"//VTK::Color::Impl",c).result):i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=cd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=cd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=cd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:qd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=cd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=cd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=cd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=cd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=cd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=cd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=cd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()||o.getEdgeVisibility()&&o.getRepresentation()===Wd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===zd.Points||o.getRepresentation()===Wd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===zd.Lines||o.getRepresentation()===Wd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==zd.Tris&&e!==zd.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==zd.TrisEdges&&e!==zd.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=cd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==zl.ID_LOW24&&t.lastSelectionState!==zl.ID_HIGH24||(a=cd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=cd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=cd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=cd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case zl.ID_LOW24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case zl.ID_HIGH24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=cd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Hd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||qd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||qd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||qd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===Zd.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():zl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===Wd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===zl.ID_LOW24||t.lastSelectionState===zl.ID_HIGH24);for(let i=zd.Start;i<zd.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===zd.TrisEdges||i===zd.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(Xd),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(Yd),!t.currentInput)return void qd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==jd.USE_CELL_DATA&&i!==jd.USE_CELL_FIELD_DATA&&i!==jd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===jd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Hd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();if(t.openGLActor.getActiveTextures()||(c=null),t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),t.internalColorTexture||(t.internalColorTexture=Nd.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Kd.NEAREST),e.setMagnificationFilter(Kd.NEAREST),e.setWrapS($d.CLAMP_TO_EDGE),e.setWrapT($d.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const u=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==u){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],d=r.getProperty().getEdgeVisibility()&&l===Wd.SURFACE;for(let e=zd.Start;e<zd.End;e++)e!==zd.TrisEdges&&e!==zd.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):d?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,Wd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Jd={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:zl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ep(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jd,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=zd,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=zd.Start;e<zd.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),Qd(e,t)}const tp=Et(ep,"vtkOpenGLPolyDataMapper");var np={newInstance:tp,extend:ep};rn("vtkMapper",tp);const{ColorMode:rp,ScalarMode:op,GetArray:ap}=hl;function ip(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString(rp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(op,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===op.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===op.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===op.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===op.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_FIELD_DATA){const t=e.getFieldData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const sp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function lp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),ip(e,t)}var cp={newInstance:jt.newInstance(lp,"vtkMapper2D"),extend:lp};const up=1,{primTypes:dp}=yd,{ScalarMode:pp}=cp,{vtkErrorMacro:fp}=Kt,gp={type:"StartEvent"},mp={type:"EndEvent"};function hp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=dp.Start;e<dp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(gp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(mp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==pp.USE_CELL_DATA&&a!==pp.USE_CELL_FIELD_DATA&&a!==pp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===pp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=mc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[dp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[dp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[dp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[dp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=dp.Start;a<dp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=cd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=cd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=cd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=cd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=cd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=cd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=cd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=cd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=cd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||fp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||fp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||fp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===up?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const vp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const yp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vp,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=dp,t.tmpMat4=g(new Float64Array(16));for(let e=dp.Start;e<dp.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),hp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",yp);const{VectorMode:Tp}=Pl;function bp(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function xp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Cp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Sp(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;bp(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,bp(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{bp(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Tp.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Es.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Ap=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=nc.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=gu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Tu.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Sp(e,t)}),"vtkScalarBarActorHelper");function Ip(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(xp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Cp())}}function wp(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Pp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wp(n)),t.autoLayout||(t.autoLayout=xp(0,t)),t.generateTicks||(t.generateTicks=Cp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Ip(e,t)}var Op={newInstance:jt.newInstance(Pp,"vtkScalarBarActor"),extend:Pp,newScalarBarActorHelper:Ap};function Rp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Mp={};const Dp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Mp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Op.newScalarBarActorHelper(),Rp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Dp);const{vtkErrorMacro:Ep}=Kt;function Vp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Es.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ep("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||Ep("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const Lp={context:null};const Bp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lp,n),Zt.extend(e,t,n),t.openGLTexture=Nd.newInstance(),t.tris=yd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Vp(e,t)}));rn("vtkSkybox",Bp);const{FieldAssociations:Np}=ol;function _p(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Fp={fieldAssociation:Np.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function kp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),_p(e,t)}var Gp={newInstance:jt.newInstance(kp,"vtkHardwareSelector"),extend:kp};function Up(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>{const e=[0,0];return null!==t.glFramebuffer&&(e[0]=t.glFramebuffer.width,e[1]=t.glFramebuffer.height),e},e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Nd.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(bd.LINEAR),r.setMagnificationFilter(bd.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const zp={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function Wp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zp,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Up(e,t)}var Hp={newInstance:Et(Wp,"vtkFramebuffer"),extend:Wp};function jp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Kp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function $p(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),jp(e,t)}var qp={newInstance:jt.newInstance($p,"vtkSelectionNode"),extend:$p,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:Xp}=Wl,{SelectionContent:Yp,SelectionField:Zp}=qp,{FieldAssociations:Qp}=ol,{vtkErrorMacro:Jp}=jt;function ef(e){return`${e.propID} ${e.compositeID}`}function tf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function nf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function rf(e,t){let n=t;return n<<=24,n|=e,n}function of(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=nf(n[0],n[1],e.pixBuffer[Xp.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=nf(n[0],n[1],e.pixBuffer[Xp.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[Xp.ID_LOW24]&&0===tf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area))return a;const s=nf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area),l=nf(n[0],n[1],e.pixBuffer[Xp.ID_HIGH24],e.area);return a.attributeID=rf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=of(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=of(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=of(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function af(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=qp.newInstance();switch(l.setContentType(Yp.INDICES),e){case Qp.FIELD_ASSOCIATION_CELLS:l.setFieldType(Zp.CELL);break;case Qp.FIELD_ASSOCIATION_POINTS:l.setFieldType(Zp.POINT);break;default:Jp("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function sf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=of(e,[n,t],0,u);if(r&&r.valid){const t=ef(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return af(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function lf(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e[0]!==n[0]||e[1]!==n[1]?(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer()):t.framebuffer.bind()}else t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return sf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return Jp("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=Xp.MIN_KNOWN_PASS;t.currentPass<=Xp.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===Xp.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===Xp.ID_HIGH24){if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===Xp.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=nf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===Xp.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===Xp.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(Xp,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=nf(e[0],e[1],t.pixBuffer[Xp.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=nf(e[0],e[1],t.pixBuffer[Xp.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[Xp.ID_LOW24]&&0===tf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area))return a;const s=nf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area),l=nf(e[0],e[1],t.pixBuffer[Xp.ID_HIGH24],t.area);return a.attributeID=rf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=ef(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return af(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const cf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cf,n),Gp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),lf(e,t)}var df={newInstance:jt.newInstance(uf,"vtkOpenGLHardwareSelector"),extend:uf,...Wl};const{vtkErrorMacro:pf}=Kt,{Representation:ff}=ds,{ObjectType:gf}=qu,{PassTypes:mf}=df,hf={type:"StartEvent"},vf={type:"EndEvent"};function yf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(hf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(vf),!t.currentInput)return void pf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=cd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=cd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=cd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=cd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=cd.substitute(o,"//VTK::Color::Impl",s).result,o=cd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=cd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=cd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=cd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=cd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===ff.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===mf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||pf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||pf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=qu.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=qu.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=qu.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=qu.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,gf.ARRAY_BUFFER),t.normalBuffer.upload(n,gf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),gf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,gf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Tf={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const bf=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tf,n),np.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),yf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",bf);const{vtkErrorMacro:xf}=jt;class Cf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Sf(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void xf("Invalid or missing input");const r=gu.newInstance();r.shallowCopy(n);const o=new Cf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(cc.newInstance({values:c,name:"faces"})),t[0]=r}}const Af={};function If(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),jt.obj(e,t),jt.algo(e,t,1,1),Sf(e,t)}var wf={newInstance:jt.newInstance(If,"vtkClosedPolyLineToSurfaceFilter"),extend:If};const{vtkErrorMacro:Pf}=Kt;function Of(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Pf("Invalid or missing input");if(!t.cutFunction)return void Pf("Missing cut function");const o=gu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const 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o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Rf={cutFunction:null,cutScalars:null,cutValue:0};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Of(e,t)}var Df={newInstance:Et(Mf,"vtkCutter"),extend:Mf};const Ef=e=>e,Vf=1e-6;class Lf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:Ef}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=Dn(n,r);return a>=1||(En(this.tmp,n,r),Sn(this.tmp)<Vf&&(En(this.tmp,[1,0,0],e),Sn(this.tmp)<Vf&&En(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Bf=function(){return new Lf(!0)},Nf=function(){return new Lf(!1)};const _f=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Ff=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function kf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=gu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Es.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Es.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Bf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Nf().translate(...t.center).apply(o),t.matrix){Nf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Nf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const Gf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=cc.newInstance({values:Uint16Array.from(Ff)}),t._lineCells=cc.newInstance({values:Uint16Array.from(_f)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),kf(e,t)}var zf={newInstance:jt.newInstance(Uf,"vtkCubeSource"),extend:Uf};const{vtkErrorMacro:Wf}=jt;function Hf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void Wf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):Wf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const jf={};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=zf.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkImageDataOutlineFilter"),extend:Kf};let qf;function Xf(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>qf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const Yf={preMultiplyFlag:!1,matrix:[...vo]};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),Xf(e,t)}qf=jt.newInstance(Zf,"vtkTransform");var Qf={newInstance:qf,extend:Zf};const Jf={NEAREST:0,LINEAR:1};var eg={InterpolationType:Jf};const{vtkErrorMacro:tg}=Kt;function ng(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function rg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function og(e,t){t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const n=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):tg("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Jf.NEAREST?(t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.openGLTexture.setMinificationFilter(bd.LINEAR),t.openGLTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime(),e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(!r)return;const o=r.getPointData()?.getScalars();if(!o)return;t._scalars!==o&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=o);const a=o.getNumberOfComponents();let i=`${r.getMTime()}A${o.getMTime()}`;const s=t._openGLRenderWindow.getGraphicsResourceForObject(o);if(s?.vtkObj&&s?.hash===i&&t.openGLTextureString===i)t.openGLTexture=s.vtkObj,t.openGLTextureString=s.hash;else{t.openGLTexture||(t.openGLTexture=Nd.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const e=r.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],o),t.openGLTextureString=i,o&&t._openGLRenderWindow.setGraphicsResourceForObject(o,t.openGLTexture,t.openGLTextureString)}const l=n.getProperty(),c=l.getIndependentComponents(),u=c?a:1,d=c?2*u:1,p=l.getRGBTransferFunction();i=ng(l,p,u);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===i&&t.colorTextureString===i)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(e*d*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),p){const r=new Float32Array(3*e);for(let t=0;t<u;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),c)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=i,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}const g=l.getPiecewiseFunction();i=ng(l,g,u);const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);if(m?.vtkObj&&m?.hash===i&&t.pwfTextureString===i)t.pwfTexture=m.vtkObj,t.pwfTextureString=m.hash;else{const e=1024,n=e*d,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),g){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<u;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=i,g&&t._openGLRenderWindow.setGraphicsResourceForObject(g,t.pwfTexture,t.pwfTextureString)}const h=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==h){const e=Es.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Es.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:tg("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=h,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",rg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",rg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=cd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:tg("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=cd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:tg("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=cd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=cd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=cd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`));const t=ue(n?.getDirection());ge(t,t);const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getDimensions(),l=[0,i[0]-1,0,i[1]-1,0,i[2]-1];let c=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[c+a]=r[a],e[c+o[0]]=l[2*o[0]+n],e[c+o[1]]=l[2*o[1]+t],c+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const u=new Uint16Array(8);u[0]=3,u[1]=0,u[2]=1,u[3]=3,u[4]=3,u[5]=0,u[6]=3,u[7]=2;const d=s.getNormal();za.normalize(d);const p=new Float32Array(12);for(let e=0;e<4;++e)p[3*e]=d[0],p[3*e+1]=d[1],p[3*e+2]=d[2];t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(u,1);const f=Es.newInstance({numberOfComponents:3,values:p,name:"Normals"});t.resliceGeom.getPointData().setNormals(f)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=gu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Es.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else tg("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)}}const ag={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,openGLTextureString:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1,_scalars:null};const ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ag,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=$f.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=gu.newInstance(),t.cutter=Df.newInstance(),t.lineToSurfaceFilter=wf.newInstance(),t.transform=Qf.newInstance(),bt(e,t,["openGLTexture"]),og(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",ig);var sg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:lg}=Kt,{SlicingMode:cg}=sg;function ug(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function dg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function pg(e,t){t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(n.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=cd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=cd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:lg("Unsupported number of independent coordinates.")}}if(i=cd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=cd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=cd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=cd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:lg("Unsupported number of independent coordinates.")}i=cd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=cd.substitute(i,"//VTK::TCoord::Impl",[...dg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); \n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (actualThickness == 0) {\n                    gl_FragData[0] = vec4(0.0, 0.0, 1.0, 1.0);\n                    return;\n                  }\n                  if (opacityToUse > 0.01) {\n                    for (int i = -actualThickness; i <= actualThickness; i++) {\n                      for (int j = -actualThickness; j <= actualThickness; j++) {\n                        if (i == 0 || j == 0) {\n                          continue;\n                        }\n                        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                          gl_FragCoord.y + float(j),\n                          gl_FragCoord.z, gl_FragCoord.w);\n                        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                        float value = texture2D(texture1, neighborPosIS.xy).r;\n                        if (value != centerValue) {\n                          pixelOnBorder = true;\n                          break;\n                        }\n                      }\n                      if (pixelOnBorder == true) {\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      gl_FragData[0] = vec4(tColor, outlineOpacity);\n                    }\n                    else {\n                      gl_FragData[0] = vec4(tColor, opacityToUse);\n                    }\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||lg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||lg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):lg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=ug(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.vtkObj&&m?.hash===g&&t.colorTextureString===g)t.colorTexture=m.vtkObj,t.colorTextureString=m.hash;else{const e=1024,n=new Uint8ClampedArray(e*p*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST)):(t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR)),f){const r=new Float32Array(3*e);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),u)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,p,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=g,f&&t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,t.colorTextureString)}const h=l.getPiecewiseFunction(),v=ug(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.vtkObj&&y?.hash===v&&t.pwfTextureString===v)t.pwfTexture=y.vtkObj,t.pwfTextureString=y.hash;else{const e=1024,n=e*p,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),h){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<d;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),u)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,p,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=v,h&&t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,t.pwfTextureString)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===cg.I&&(S=x-C[0]),T===cg.J&&(S=x-C[2]),T!==cg.K&&T!==cg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Nd.newInstance({resizable:!0}),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===Jf.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.NEAREST)):t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(bd.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(bd.LINEAR)),t.openGLTexture.setWrapS(Td.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Td.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[cg.X,cg.Y,cg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===cg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===cg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===cg.K||T===cg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):lg("Reformat slicing not yet supported.");const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);m?.vtkObj?(t.openGLTexture=m.vtkObj,t.VBOBuildString=m.hash):(t._scalars!==g&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=g),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t._openGLRenderWindow.setGraphicsResourceForObject(g,t.openGLTexture,t.VBOBuildString)),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const h=Es.newInstance({numberOfComponents:3,values:r});h.setName("points");const v=Es.newInstance({numberOfComponents:2,values:l});v.setName("tcoords");const y=new Uint16Array(8);y[0]=3,y[1]=0,y[2]=1,y[3]=3,y[4]=3,y[5]=0,y[6]=3,y[7]=2;const C=Es.newInstance({numberOfComponents:1,values:y});t.tris.getCABO().createVBO(C,"polys",rs.SURFACE,{points:h,tcoords:v,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=e=>{t.labelOutlineThicknessTexture||(t.labelOutlineThicknessTexture=Nd.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]}}const fg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,_scalars:null};const gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fg,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),pg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",gg);const{vtkErrorMacro:mg}=jt;function hg(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function vg(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===Jf.NEAREST?(t.volumeTexture.setMinificationFilter(bd.NEAREST),t.volumeTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.volumeTexture.setMinificationFilter(bd.LINEAR),t.volumeTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=hg(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=hg(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Es.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Es.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Es.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Es.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getCenterlineTangentDirections(),r=new Float32Array(3*i),o=new Float32Array(3*i);for(let t=0,a=0;t<n;++t){const n=3*t;for(let t=0;t<4;++t)r[a+0]=e[n+0],r[a+1]=e[n+1],r[a+2]=e[n+2],o[a+0]=e[n+3],o[a+1]=e[n+4],o[a+2]=e[n+5],a+=3}const a=Es.newInstance({numberOfComponents:3,values:r,name:"centerlineTopDirection"}),s=Es.newInstance({numberOfComponents:3,values:o,name:"centerlineBotDirection"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const s=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],l=t.renderable.getUseUniformOrientation();l?s.push("out vec3 centerlineDirVSOutput;","uniform vec3 centerlineDirection;"):s.push("out vec3 centerlineTopDirVSOutput;","out vec3 centerlineBotDirVSOutput;","out float centerlineAngleVSOutput;","attribute vec3 centerlineTopDirection;","attribute vec3 centerlineBotDirection;"),a=cd.substitute(a,"//VTK::Color::Dec",s).result;const c=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];l?c.push("centerlineDirVSOutput = centerlineDirection;"):c.push("vec3 sumVec = centerlineTopDirection + centerlineBotDirection;","float sumLen2 = dot(sumVec, sumVec);","float diffLen2 = 4.0 - sumLen2;","if (diffLen2 < 0.001) {","  // vectors are too close to each other, use lerp","  centerlineAngleVSOutput = -1.0; // use negative angle as a flag for lerp","  centerlineTopDirVSOutput = centerlineTopDirection;","  centerlineBotDirVSOutput = centerlineBotDirection;","} else if (sumLen2 == 0.0) {","  // vector are opposite to each other, don't make a choice for the user","  // use slerp without direction, it will display the centerline color on each row of pixel","  centerlineAngleVSOutput = 0.0;","  centerlineTopDirVSOutput = vec3(0.0);","  centerlineBotDirVSOutput = vec3(0.0);","} else {","  // use slerp","  centerlineAngleVSOutput = 2.0 * atan(sqrt(diffLen2/sumLen2));","  float sinAngle = sin(centerlineAngleVSOutput);","  centerlineTopDirVSOutput = centerlineTopDirection / sinAngle;","  centerlineBotDirVSOutput = centerlineBotDirection / sinAngle;","}"),a=cd.substitute(a,"//VTK::Color::Impl",c).result;const u=t.volumeTexture.getComponents(),d=o.getProperty().getIndependentComponents();let p=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];l?p.push("in vec3 centerlineDirVSOutput;"):p.push("in vec3 centerlineTopDirVSOutput;","in vec3 centerlineBotDirVSOutput;","in float centerlineAngleVSOutput;");const f=t.renderable.getCenterPoint();if(f&&p.push("uniform vec3 globalCenterPoint;"),d){for(let e=1;e<u;e++)p=p.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(u){case 1:p=p.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:p=p.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:mg("Unsupported number of independent coordinates.")}}i=cd.substitute(i,"//VTK::TCoord::Dec",p).result;let g=[];if(l?g.push("vec3 interpolatedCenterlineDir = centerlineDirVSOutput;"):g.push("vec3 interpolatedCenterlineDir;","if (centerlineAngleVSOutput < 0.0) {","  // Lerp","  interpolatedCenterlineDir = quadOffsetVSOutput.y * centerlineTopDirVSOutput + (1.0 - quadOffsetVSOutput.y) * centerlineBotDirVSOutput;","} else {","  // Slerp","  float topInterpolationAngle = quadOffsetVSOutput.y * centerlineAngleVSOutput;","  float botInterpolationAngle = centerlineAngleVSOutput - topInterpolationAngle;","  interpolatedCenterlineDir = sin(topInterpolationAngle) * centerlineTopDirVSOutput + sin(botInterpolationAngle) * centerlineBotDirVSOutput;","}","// Slerp should give a normalized vector but when sin(angle) is small, rounding error occurs","// Normalize for both lerp and slerp","interpolatedCenterlineDir = normalize(interpolatedCenterlineDir);"),f?g.push("float baseOffset = dot(interpolatedCenterlineDir, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):g.push("float horizontalOffset = quadOffsetVSOutput.x;"),g.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * interpolatedCenterlineDir;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(volumeTexture, volumePosTC);"),d){const e=["r","g","b","a"];for(let t=0;t<u;++t)g=g.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(u){case 1:g=g.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:g=g.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:mg("Unsupported number of independent coordinates.")}}else switch(u){case 1:g=g.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:g=g.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",g).result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||mg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&n.getProgram().isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),e.name,e.offset,n.getCABO().getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&mg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const a=t.volumeTexture.getTextureUnit();if(n.getProgram().setUniformi("volumeTexture",a),n.getProgram().setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4f("backgroundColor",...t.renderable.getBackgroundColor()),n.getProgram().isUniformUsed("centerlineDirection")){const e=t.renderable.getUniformDirection();n.getProgram().setUniform3fArray("centerlineDirection",e)}if(n.getProgram().isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();n.getProgram().setUniform3fArray("globalCenterPoint",e)}const i=t.currentImageDataInput,s=i.getWorldToIndex(),l=P(new Float32Array(16),Rn([],i.getDimensions())),c=oe(l,l,s);if(n.getProgram().setUniformMatrix("MCTCMatrix",c),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const yg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Tg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),kd(e,t,n),jt.algo(e,t,2,0),t.tris=yd.newInstance(),t.volumeTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.pwfTexture=Nd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),vg(e,t)}),"vtkOpenGLImageCPRMapper");function bg(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Tg);const xg={context:null,keyMatrixTime:null,keyMatrices:null};const Cg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),bg(e,t)}),"vtkOpenGLImageSlice");function Sg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Cg);const Ag={};const Ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ag,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Sg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Ig);const wg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Pg={FRACTIONAL:0,PROPORTIONAL:1};var Og={InterpolationType:wg,OpacityMode:Pg};const Rg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5};var Mg={BlendMode:Rg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Dg,vtkErrorMacro:Eg}=Kt;function Vg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function Lg(e,t){t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(n,r)=>{if(n){t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const n=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera()),e.renderPiece(r,n)}},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define vtkIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n#ifdef vtkImageLabelOutlineOn\n\nuniform float outlineOpacity;\nuniform float vpWidth;\nuniform float vpHeight;\nuniform float vpOffsetX;\nuniform float vpOffsetY;\nuniform mat4 PCWCMatrix;\nuniform mat4 vWCtoIDX;\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents >= 3\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#endif\n#if vtkNumComponents >= 4\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef vtkIndependentComponentsOn\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.a = tmp.g;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n#if defined(vtkGradientOpacityOn)\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n#else\n  return 1.0;\n#endif\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n#ifdef vtkClippingPlanesOn\n  void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n  {\n    vec3 g1VC[3];\n    for (int i = 0; i < 3; ++i)\n    {\n      g1VC[i] = IStoVC(texPos[i]);\n    }\n    vec3 posVC = IStoVC(pos);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      for (int j = 0; j < 3; ++j)\n      {\n        if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n        {\n          g1[j] = 0.0;\n        }\n      }\n    }\n  }\n#endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    #ifdef vtkGradientOpacityOn\n      vec4 computeDensityNormal(float gradientMag, vec3 scalarInterp[2])\n      {\n    #else\n      //if gradient opacity not on but using density gradient\n      vec4 computeDensityNormal(vec3 scalarInterp[2])\n      {\n    #endif\n        vec3 opacityG1, opacityG2;\n        opacityG1.x = texture2D(otexture, vec2(scalarInterp[0].x * oscale0 + oshift0, 0.5)).r;\n        opacityG1.y = texture2D(otexture, vec2(scalarInterp[0].y * oscale0 + oshift0, 0.5)).r;\n        opacityG1.z = texture2D(otexture, vec2(scalarInterp[0].z * oscale0 + oshift0, 0.5)).r;\n        opacityG2.x = texture2D(otexture, vec2(scalarInterp[1].x * oscale0 + oshift0, 0.5)).r;\n        opacityG2.y = texture2D(otexture, vec2(scalarInterp[1].y * oscale0 + oshift0, 0.5)).r;\n        opacityG2.z = texture2D(otexture, vec2(scalarInterp[1].z * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n        float gradOpacityFactor = 1.0f;\n        if (gradientMag >= 0.0){\n          gradOpacityFactor = computeGradientOpacityFactor(gradientMag, goscale0, goshift0, gomin0, gomax0);\n        }\n        opacityG1.xyz *= gradOpacityFactor;\n        opacityG2.xyz *= gradOpacityFactor;\n    #endif\n\n        vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n        // divide by spacing\n        opacityG.xyz /= vSpacing;\n        opacityG.w = length(opacityG.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(opacityG.xyz);\n        if (length(opacityG.xyz) > 0.0) {\n          return vec4(normalize(opacityG.xyz),opacityG.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n\n      vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2])\n      {\n        vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n        vec3 yvec = vec3(0.0, tstep.y, 0.0);\n        vec3 zvec = vec3(0.0, 0.0, tstep.z);\n        vec3 texPosPVec[3];\n        texPosPVec[0] = pos + xvec;\n        texPosPVec[1] = pos + yvec;\n        texPosPVec[2] = pos + zvec;\n        vec3 texPosNVec[3];\n        texPosNVec[0] = pos - xvec;\n        texPosNVec[1] = pos - yvec;\n        texPosNVec[2] = pos - zvec;\n        vec3 g1, g2;\n\n        scalarInterp[0].x = getTextureValue(texPosPVec[0]).a;\n        scalarInterp[0].y = getTextureValue(texPosPVec[1]).a;\n        scalarInterp[0].z = getTextureValue(texPosPVec[2]).a;\n        scalarInterp[1].x = getTextureValue(texPosNVec[0]).a;\n        scalarInterp[1].y = getTextureValue(texPosNVec[1]).a;\n        scalarInterp[1].z = getTextureValue(texPosNVec[2]).a;\n\n        #ifdef vtkClippingPlanesOn\n          adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n          adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n        #endif\n        vec4 result;\n        result.x = scalarInterp[0].x - scalarInterp[1].x;\n        result.y = scalarInterp[0].y - scalarInterp[1].y;\n        result.z = scalarInterp[0].z - scalarInterp[1].z;\n        // divide by spacing\n        result.xyz /= vSpacing;\n        result.w = length(result.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(result.xyz);\n        if (length(result.xyz) > 0.0) {\n          return vec4(normalize(result.xyz),result.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n  #endif\n\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n#ifdef vtkImageLabelOutlineOn\nvec3 fragCoordToIndexSpace(vec4 fragCoord) {\n  vec4 pcPos = vec4(\n    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n    (fragCoord.z - 0.5) * 2.0,\n    1.0);\n\n  vec4 worldCoord = PCWCMatrix * pcPos;\n  vec4 vertex = (worldCoord/worldCoord.w);\n\n  vec3 index = (vWCtoIDX * vertex).xyz;\n\n  // half voxel fix for labelmapOutline\n  return (index + vec3(0.5)) / vec3(volumeDimensions);\n}\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  // support gradient opacity\n  #ifdef vtkGradientOpacityOn\n    vec4 normal;\n  #endif\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n      normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n//=======================================================================\n// Given a texture value compute the color and opacity\n//\nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n#ifdef vtkImageLabelOutlineOn\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n  if (actualThickness == 0) {\n    return vec4(0, 0, 1, 1);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  if (float(tColor.a) > 0.01) {\n    for (int i = -actualThickness; i <= actualThickness; i++) {\n      for (int j = -actualThickness; j <= actualThickness; j++) {\n        if (i == 0 || j == 0) {\n          continue;\n        }\n\n        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n          gl_FragCoord.y + float(j),\n          gl_FragCoord.z, gl_FragCoord.w);\n\n        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n        vec4 value = getTextureValue(neighborPosIS);\n\n        // If any of my neighbours are not the same value as I\n        // am, this means I am on the border of the segment.\n        // We can break the loops\n        if (any(notEqual(value, centerValue))) {\n          pixelOnBorder = true;\n          break;\n        }\n      }\n\n      if (pixelOnBorder == true) {\n        break;\n      }\n    }\n\n    // If I am on the border, I am displayed at full opacity\n    if (pixelOnBorder == true) {\n      tColor.a = outlineOpacity;\n    }\n  }\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n  //\n  vec4 goFactor = vec4(1.0,1.0,1.0,1.0);\n\n  // compute the normal vectors as needed\n  #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #if !defined(vtkComponent0Proportional)\n        vec4 normal0 = normalMat[0];\n      #endif\n      #if !defined(vtkComponent1Proportional)\n        vec4 normal1 = normalMat[1];\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          vec4 normal2 = normalMat[2];\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            vec4 normal3 = normalMat[3];\n          #endif\n        #endif\n      #endif\n    #else\n      vec4 normalLight;\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp);\n        if (length(normal0)>0.0){\n          #ifdef vtkGradientOpacityOn\n            normalLight = computeDensityNormal(normal0.w, scalarInterp);\n          #else\n            normalLight = computeDensityNormal(scalarInterp);\n          #endif\n          if (length(normalLight) == 0.0){\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n        normalLight = normal0;\n      #endif\n    #endif\n  #endif\n\n  // compute gradient opacity factors as needed\n  #if defined(vtkGradientOpacityOn)\n    #if !defined(vtkComponent0Proportional)\n      goFactor.x =\n        computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      #if !defined(vtkComponent1Proportional)\n        goFactor.y =\n          computeGradientOpacityFactor(normal1.a, goscale1, goshift1, gomin1, gomax1);\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          goFactor.z =\n            computeGradientOpacityFactor(normal2.a, goscale2, goshift2, gomin2, gomax2);\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            goFactor.w =\n              computeGradientOpacityFactor(normal3.a, goscale3, goshift3, gomin3, gomax3);\n          #endif\n        #endif\n      #endif\n    #endif\n  #endif\n\n  // single component is always independent\n  #if vtkNumComponents == 1\n    vec4 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5));\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n    if (tColor.a < EPSILON){\n      return vec4(0.0);\n    }\n  #endif\n\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n    vec4 tColor = mix0*texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0));\n    #if !defined(vtkComponent0Proportional)\n      tColor.a = goFactor.x*mix0*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n      tColor *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix0));\n    #endif\n\n    vec3 tColor1 = mix1*texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n    #if !defined(vtkComponent1Proportional)\n      tColor.a += goFactor.y*mix1*texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n      tColor1 *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix1));\n    #endif\n\n    #if vtkNumComponents >= 3\n      vec3 tColor2 = mix2*texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n      #if !defined(vtkComponent2Proportional)\n        tColor.a += goFactor.z*mix2*texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n      #else\n        float pwfValue = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n        tColor2 *= pwfValue;\n        tColor.a *= mix(pwfValue, 1.0, (1.0 - mix2));\n      #endif\n\n      #if vtkNumComponents >= 4\n        vec3 tColor3 = mix3*texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n        #if !defined(vtkComponent3Proportional)\n          tColor.a += goFactor.w*mix3*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #else\n          float pwfValue = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n          tColor3 *= pwfValue;\n          tColor.a *= mix(pwfValue, 1.0, (1.0 - mix3));\n        #endif\n      #endif\n    #endif\n  #else // then not independent\n\n  #if vtkNumComponents == 2\n    float lum = tValue.r * cscale0 + cshift0;\n    float alpha = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    vec4 tColor = vec4(lum, lum, lum, alpha);\n  #endif\n  #if vtkNumComponents == 3\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n  #endif\n  #if vtkNumComponents == 4\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n  #endif\n  #endif // dependent\n\n  // apply lighting if requested as appropriate\n  #if vtkLightComplexity > 0\n    #if !defined(vtkComponent0Proportional)\n      #if vtkNumComponents == 1\n        #ifdef SurfaceShadowOn\n            #if vtkLightComplexity < 3\n                vec3 tColorS = applyLightingDirectional(posIS, tColor, normalLight);\n            #else\n                vec3 tColorS = applyLightingPositional(posIS, tColor, normalLight, IStoVC(posIS));\n            #endif\n        #endif\n\n        #ifdef VolumeShadowOn\n          vec3 tColorVS = applyShadowRay(tColor.rgb, posIS, rayDirVC);\n          #ifdef SurfaceShadowOn\n            float vol_coef = volumetricScatteringBlending * (1.0 - tColor.a / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n            tColor.rgb = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n          #else\n            tColor.rgb = tColorVS;\n          #endif\n        #else\n            tColor.rgb = tColorS;\n        #endif\n\n      #else\n        applyLighting(tColor.rgb, normal0);\n      #endif\n    #endif\n\n    #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n      #if !defined(vtkComponent1Proportional)\n        applyLighting(tColor1, normal1);\n      #endif\n    #if vtkNumComponents >= 3\n      #if !defined(vtkComponent2Proportional)\n        applyLighting(tColor2, normal2);\n      #endif\n    #if vtkNumComponents >= 4\n      #if !defined(vtkComponent3Proportional)\n        applyLighting(tColor3, normal3);\n      #endif\n    #endif\n    #endif\n    #endif\n  #endif\n\n// perform final independent blend as needed\n#if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n  tColor.rgb += tColor1;\n#if vtkNumComponents >= 3\n  tColor.rgb += tColor2;\n#if vtkNumComponents >= 4\n  tColor.rgb += tColor3;\n#endif\n#endif\n#endif\n\n#endif\nreturn tColor;\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents == 2\n     if (val.r >= min.r && val.r <= max.r &&\n        val.g >= min.g && val.g <= max.g) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 3\n    if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n        all(lessThanEqual(val, ipScalarRangeMax))) {\n      withinRange = true;\n    }\n  #endif\n  return withinRange;\n}\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Fragment;o.getProperty().getInterpolationType()===wg.LINEAR&&(a=cd.substitute(a,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===o.getProperty().getUseLabelOutline()&&(a=cd.substitute(a,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result);const i=t.scalarTexture.getComponents();a=cd.substitute(a,"//VTK::NumComponents",`#define vtkNumComponents ${i}`).result;const s=o.getProperty().getIndependentComponents();if(s){a=cd.substitute(a,"//VTK::IndependentComponentsOn","#define vtkIndependentComponentsOn").result;const e=[];for(let t=0;t<i;t++)o.getProperty().getOpacityMode(t)===Pg.PROPORTIONAL&&e.push(`#define vtkComponent${t}Proportional`);e.length>0&&(a=cd.substitute(a,"//VTK::vtkProportionalComponents",e.join("\n")).result)}const l=t.currentInput.getSpatialExtent(),c=t.currentInput.getSpacing(),u=new Float64Array(3);In(u,(l[1]-l[0])*c[0],(l[3]-l[2])*c[1],(l[5]-l[4])*c[2]);const d=Sn(u)/e.getCurrentSampleDistance(r);a=cd.substitute(a,"//VTK::MaximumSamplesValue",`${Math.ceil(d)}`).result,a=cd.substitute(a,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lastLightComplexity}`).result,t.lastLightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(a=cd.substitute(a,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(a=cd.substitute(a,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0&&(a=cd.substitute(a,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result)),t.gopacity=o.getProperty().getUseGradientOpacity(0);for(let e=1;s&&!t.gopacity&&e<i;++e)o.getProperty().getUseGradientOpacity(e)&&(t.gopacity=!0);t.gopacity&&(a=cd.substitute(a,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(a=cd.substitute(a,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(a=cd.substitute(a,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,a=cd.substitute(a,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),a=cd.substitute(a,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=a,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lastLightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=cd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lastLightComplexity&&(o=cd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=cd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=cd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=cd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=cd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{let a=0;o.getProperty().getShade()&&t.renderable.getBlendMode()===Rg.COMPOSITE_BLEND&&(a=0,t.numberOfLights=0,r.getLights().forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===a&&(a=1)),1===a&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(a=2),a<3&&e.getPositional()&&(a=3)})));let i=!1;t.lastLightComplexity!==a&&(t.lastLightComplexity=a,i=!0);const s=t.scalarTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=!1;const u=[];if(l){for(let e=0;e<s;e++)u.push(o.getProperty().getOpacityMode(e));u.length>0&&(c=!0)}const d=t.currentInput.getSpatialExtent(),p=t.currentInput.getSpacing(),f=new Float64Array(3);In(f,(d[1]-d[0])*p[0],(d[3]-d[2])*p[1],(d[5]-d[4])*p[2]);const g=Sn(f)/e.getCurrentSampleDistance(r),m={interpolationType:o.getProperty().getInterpolationType(),useLabelOutline:o.getProperty().getUseLabelOutline(),numComp:s,usesProportionalComponents:c,iComps:l,maxSamples:g,useGradientOpacity:o.getProperty().getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),proportionalComponents:u};return t.previousState&&t.previousState.interpolationType===m.interpolationType&&t.previousState.useLabelOutline===m.useLabelOutline&&t.previousState.numComp===m.numComp&&t.previousState.usesProportionalComponents===m.usesProportionalComponents&&t.previousState.iComps===m.iComps&&t.previousState.maxSamples===m.maxSamples&&t.previousState.useGradientOpacity===m.useGradientOpacity&&t.previousState.blendMode===m.blendMode&&function(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;++n)if(e[n]!==t[n])return!1;return!0}(t.previousState.proportionalComponents,m.proportionalComponents)?!!(0===n.getProgram()?.getHandle()||i||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||!!t.lastZBufferTexture!=!!t.zBufferTexture||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime())&&(t.lastZBufferTexture=t.zBufferTexture,!0):(t.previousState={...m},!0)},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Dg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*Dn(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(o.getProperty().getUseLabelOutline()){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lastLightComplexity)return;let O=0;const R=[],M=[],D=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],D[0+3*O]=-.5*w[0],D[1+3*O]=-.5*w[1],D[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",D),3===t.lastLightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();o.setUniformi("ctexture",t.colorTexture.getTextureUnit()),o.setUniformi("otexture",t.opacityTexture.getTextureUnit()),o.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),o.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const a=t.scalarTexture.getVolumeInfo(),i=r.getProperty(),s=t.scalarTexture.getComponents(),l=r.getProperty().getIndependentComponents();if(l&&s>=2)for(let e=0;e<s;e++)o.setUniformf(`mix${e}`,r.getProperty().getComponentWeight(e));for(let e=0;e<s;e++){const t=l?e:0,n=a.scale[e],r=i.getScalarOpacity(t).getRange(),s=n/(r[1]-r[0]),c=(a.offset[e]-r[0])/(r[1]-r[0]);o.setUniformf(`oshift${e}`,c),o.setUniformf(`oscale${e}`,s);const u=i.getRGBTransferFunction(t).getRange(),d=(a.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);o.setUniformf(`cshift${e}`,d),o.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(l)for(let e=0;e<s;++e){const t=a.scale[e];if(i.getUseGradientOpacity(e)){const n=i.getGradientOpacityMinimumOpacity(e),r=i.getGradientOpacityMaximumOpacity(e);o.setUniformf(`gomin${e}`,n),o.setUniformf(`gomax${e}`,r);const a=[i.getGradientOpacityMinimumValue(e),i.getGradientOpacityMaximumValue(e)];o.setUniformf(`goscale${e}`,t*(r-n)/(a[1]-a[0])),o.setUniformf(`goshift${e}`,-a[0]*(r-n)/(a[1]-a[0])+n)}else o.setUniformf(`gomin${e}`,1),o.setUniformf(`gomax${e}`,1),o.setUniformf(`goscale${e}`,0),o.setUniformf(`goshift${e}`,1)}else{const e=a.scale[s-1],t=i.getGradientOpacityMinimumOpacity(0),n=i.getGradientOpacityMaximumOpacity(0);o.setUniformf("gomin0",t),o.setUniformf("gomax0",n);const r=[i.getGradientOpacityMinimumValue(0),i.getGradientOpacityMaximumValue(0)];o.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),o.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===r.getProperty().getUseLabelOutline()){const e=r.getProperty().getLabelOutlineOpacity();o.setUniformf("outlineOpacity",e)}t.lastLightComplexity>0&&(o.setUniformf("vAmbient",i.getAmbient()),o.setUniformf("vDiffuse",i.getDiffuse()),o.setUniformf("vSpecular",i.getSpecular()),o.setUniformf("vSpecularPower",i.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*Dn(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===wg.NEAREST?(t.scalarTexture.setMinificationFilter(bd.NEAREST),t.scalarTexture.setMagnificationFilter(bd.NEAREST)):(t.scalarTexture.setMinificationFilter(bd.LINEAR),t.scalarTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null,null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=fd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Eg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;t._scalars!==a&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=a);const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(bd.LINEAR),t.jitterTexture.setMagnificationFilter(bd.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=i.getIndependentComponents()?s:1,c=i.getScalarOpacity(),u=t._openGLRenderWindow.getGraphicsResourceForObject(c);let d=Vg(i,c,l);if(u.vtkObj&&u.hash===d&&t.opacityTextureString===d)t.opacityTexture=u.vtkObj,t.opacityTextureString=u.hash;else{const r=1024,o=2*r*l,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<l;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.releaseGraphicsResources(t._openGLRenderWindow),t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(bd.LINEAR),t.opacityTexture.setMagnificationFilter(bd.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*l,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*l,1,ys.UNSIGNED_CHAR,e)}t.opacityTextureString=d,c&&t._openGLRenderWindow.setGraphicsResourceForObject(c,t.opacityTexture,t.opacityTextureString)}const p=i.getRGBTransferFunction();d=Vg(i,p,l);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===d&&t.colorTextureString===d)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(2*e*l*3),r=new Float32Array(3*e);for(let t=0;t<l;++t){const o=i.getRGBTransferFunction(t),a=o.getRange();o.getTable(a[0],a[1],e,r,1);for(let o=0;o<3*e;++o)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.create2DFromRaw(e,2*l,3,ys.UNSIGNED_CHAR,n),t.colorTextureString=d,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}e.updateLabelOutlineThicknessTexture(r);const g=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(d=`${o.getMTime()}A${a.getMTime()}`,g?.vtkObj&&g?.hash===d&&t.scalarTextureString===d)t.scalarTexture=g.vtkObj,t.scalarTextureString=g.hash;else{const e=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.releaseGraphicsResources(t._openGLRenderWindow),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],a,t.renderable.getPreferSizeOverAccuracy()),t.scalarTextureString=d,a&&t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,t.scalarTextureString)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Es.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Es.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=e=>{const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}}}const Bg={context:null,VBOBuildTime:null,scalarTexture:null,scalarTextureString:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lastLightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0,_scalars:null};const Ng=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bg,n),Zt.extend(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=yd.newInstance(),t.scalarTexture=Nd.newInstance(),t.opacityTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.jitterTexture=Nd.newInstance(),t.jitterTexture.setWrapS(Td.REPEAT),t.jitterTexture.setWrapT(Td.REPEAT),t.labelOutlineThicknessTexture=Nd.newInstance(),t.framebuffer=Hp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Lg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",Ng);const{vtkDebugMacro:_g}=Kt;function Fg(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent();const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():_g("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const kg={};const Gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kg,n),Zt.extend(e,t,n),Fg(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Gg);var Ug="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:zg}=ds;function Wg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Nd.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Hg(e,t){return Wg(e,t,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE)}function jg(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Hg(t._openGLRenderWindow,t.size),t.seedTexture0=Hg(t._openGLRenderWindow,t.size),t.licTexture1=Hg(t._openGLRenderWindow,t.size),t.seedTexture1=Hg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Wg(t._openGLRenderWindow,t.size,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Wg(e,t,Nd.Filter.LINEAR,Nd.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const Kg={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function $g(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kg,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),jg(e,t)}var qg={newInstance:jt.newInstance($g,"vtkLICPingPongBufferManager"),extend:$g};const Xg=0,Yg=1,Zg=2,Qg=3,Jg=1;function em(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Hp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;f&&o[0]===f.getSize()[0]&&o[1]===f.getSize()[1]||(f=Hp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=qg.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const g=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],m=1/o[0],h=1/o[1],v=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;v.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:y}=t;v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",0),y.setUniformf("uMaskThreshold",t.maskThreshold),y.setUniform2f("uNoiseBoundsPt1",...g),y.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),y.setUniformi("texLIC",t.bufs.getLICTextureUnit()),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,y);const{LICIShaderProgram:T}=t;v.readyShaderProgram(T),T.setUniformi("uPassNo",0),T.setUniformf("uStepSize",-d),T.setUniform2f("uNoiseBoundsPt1",...g),T.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:b}=t;if(v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,b),t.enhancedLIC){t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",m),a.setUniformf("uDy",h),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",1),n(y,t.bufs),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformi("uPassNo",1),T.setUniformf("uStepSize",-d),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),b.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,b)}if(t.antiAlias){const e=t.AAHProgram;v.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",m);const a=t.AAVProgram;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",h);for(let i=0;i<t.antiAlias;++i)v.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),v.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const tm={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLineIntegralConvolution2D"),extend:nm};function om(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const am={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:Xg,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:Jg,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function im(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,am,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),om(0,t)}var sm={newInstance:jt.newInstance(im,"vtkSurfaceLICInterface"),extend:im};const{Representation:lm}=ds;function cm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===Jg?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Nd.newInstance({wrapS:Nd.Wrap.REPEAT,wrapT:Nd.Wrap.REPEAT,minificationFilter:Nd.Filter.NEAREST,magnificationFilter:Nd.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.allocateTextures=()=>{const n=Nd.Filter.NEAREST,r=Nd.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Nd.newInstance({wrapS:Nd.Wrap.CLAMP_TO_EDGE,wrapT:Nd.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Nd.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Hp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",lm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=rm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===Zg||u===Qg){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const um={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),sm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkSurfaceLICInterface"),extend:dm};const{vtkErrorMacro:fm}=Kt;function gm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=cd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=cd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=cd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=cd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=cd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(fm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(fm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(fm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=sm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=pm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(fm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const mm={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const hm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mm,n),np.extend(e,t,n),gm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",hm);const{vtkErrorMacro:vm}=Kt;function ym(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||vm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,zu.ARRAY_BUFFER),o&&a.getColorBO().upload(f,zu.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Tm={};const bm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),np.extend(e,t,n),ym(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",bm);const{vtkErrorMacro:xm}=Kt;function Cm(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=cd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||xm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||xm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||xm("Error setting 'radiusMC' in shader 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o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():xm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let 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e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const Vh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Lh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Eh(e,t)}var Bh={newInstance:jt.newInstance(Lh,"vtkCamera"),extend:Lh};function Nh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const _h={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function Fh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_h,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),Nh(e,t)}var kh={newInstance:jt.newInstance(Fh,"vtkLight"),extend:Fh,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Gh}=jt;function Uh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Gh("call displayToView on your view instead"),e.viewToDisplay=()=>Gh("callviewtodisplay on your view instead"),e.getSize=()=>Gh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Gh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkViewport"),extend:Wh};const{vtkDebugMacro:jh,vtkErrorMacro:Kh,vtkWarningMacro:$h}=Kt;function qh(e){return()=>Kh(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function Xh(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(jh("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):Kh("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=qh("allocateTime"),e.updateGeometry=qh("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{jh(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Bh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=kh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return Kh("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return Kh("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),jh("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return jh("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return Kh("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&($h("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return jh("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return Kh("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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Yh={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function Zh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,Yh,n),Hh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),Xh(e,t)}var Qh={newInstance:Et(Zh,"vtkRenderer"),extend:Zh};const Jh=Object.create(null);function ev(e,t){Jh[e]=t}function tv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Jh[e]&&Jh[e](t)}function nv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return tv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))}}const rv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1};function ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),nv(e,t)}var av={newInstance:jt.newInstance(ov,"vtkRenderWindow"),extend:ov,registerViewConstructor:ev,listViewAPIs:function(){return Object.keys(Jh)},newAPISpecificView:tv};const iv={Unknown:0,LeftController:1,RightController:2},sv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var lv={Device:iv,Input:sv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:cv,Input:uv}=lv,{vtkWarningMacro:dv,vtkErrorMacro:pv,normalizeWheel:fv,vtkOnceErrorMacro:gv}=jt,mv={ctrlKey:!1,altKey:!1,shiftKey:!1},hv={"xr-standard":[uv.Trigger,uv.Grip,uv.TrackPad,uv.Thumbstick,uv.A,uv.B]},vv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function yv(e){e.cancelable&&e.preventDefault()}function Tv(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function bv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{pv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>dv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",yv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",yv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&yv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&dv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&yv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:pv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?dv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):pv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;dv(`${e} did not request an 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t.lastGamepadValues[i.index][s].buttons||(t.lastGamepadValues[i.index][s].buttons[r]=!1),t.lastGamepadValues[i.index][s].buttons[r]!==i.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,pressed:i.buttons[r].pressed,device:"left"===n.handedness?cv.LeftController:cv.RightController,input:hv[i.mapping]&&hv[i.mapping][r]?hv[i.mapping][r]:uv.Trigger}),t.lastGamepadValues[i.index][s].buttons[r]=i.buttons[r].pressed),t.lastGamepadValues[i.index][s].buttons[r]&&null!=a&&e.move3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,device:"left"===n.handedness?cv.LeftController:cv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{yv(n);const r={...fv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:pv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);if(2===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Tv(o);e.recognizeGesture("TouchEnd",t);const a={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},vv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void gv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const xv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Cv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xv,n),jt.obj(e,t),t._animationExtendedEnd=0,jt.event(e,t,"RenderEvent"),vv.forEach((n=>jt.event(e,t,n))),jt.get(e,t,["initialized","interactorStyle","lastFrameTime","recentAnimationFrameRate","_view"]),jt.setGet(e,t,["container","lightFollowCamera","enabled","enableRender","recognizeGestures","desiredUpdateRate","stillUpdateRate","picker","preventDefaultOnPointerDown","preventDefaultOnPointerUp","mouseScrollDebounceByPass"]),jt.moveToProtected(e,t,["view"]),bv(e,t)}var Sv={newInstance:jt.newInstance(Cv,"vtkRenderWindowInteractor"),extend:Cv,handledEvents:vv,...lv};const{vtkErrorMacro:Av,VOID:Iv}=jt;function wv(e,t){t.classHierarchy.push("vtkInteractorObserver");const n={...e};function r(){for(;t.subscribedEvents.length;)t.subscribedEvents.pop().unsubscribe()}function o(){Sv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Iv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Av("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Pv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function Ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),wv(e,t)}var Rv={newInstance:jt.newInstance(Ov,"vtkInteractorObserver"),extend:Ov,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},Mv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Dv}=Mv,Ev={Rotate:Dv.IS_ROTATE,Pan:Dv.IS_PAN,Spin:Dv.IS_SPIN,Dolly:Dv.IS_DOLLY,CameraPose:Dv.IS_CAMERA_POSE,WindowLevel:Dv.IS_WINDOW_LEVEL,Slice:Dv.IS_SLICE};function Vv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Ev).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Dv.IS_NONE&&(t.state=Ev[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Ev[n]&&(t.state=Dv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":e.pokedRenderer.resetCamera(),n.render();break;case"w":case"W":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Lv={state:Dv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Bv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lv,n),Rv.extend(e,t,n),Vv(e,t)}var Nv={newInstance:jt.newInstance(Bv,"vtkInteractorStyle"),extend:Bv,...Mv};const{States:_v}=Mv;function Fv(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=n.pokedRenderer;switch(t.state){case _v.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad?!n||n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad||t.state!==_v.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===_v.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const t=e.pokedRenderer.getActiveCamera(),n=t.getPhysicalTranslation(),r=.025*t.getPhysicalScale(),o=t.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);t.setPhysicalTranslation(n[0]+o[0]*r,n[1]+o[1]*r,n[2]+o[2]*r)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case _v.IS_DOLLY:e.endDolly();break;case _v.IS_PAN:e.endPan();break;case _v.IS_SPIN:e.endSpin();break;case _v.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(n.pokedRenderer,n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=n.pokedRenderer.getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(n.pokedRenderer,o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(n.pokedRenderer,o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(n.pokedRenderer,o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&n.pokedRenderer.updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=e.pokedRenderer.getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(n.pokedRenderer,r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const kv={motionFactor:10,zoomFactor:10};function Gv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kv,n),Nv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),Fv(e,t)}var Uv={newInstance:jt.newInstance(Gv,"vtkInteractorStyleTrackballCamera"),extend:Gv};function zv(e){return e}function Wv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Wv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Hv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Wv:zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function jv(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const Kv={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function $v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kv,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),jv(e,t)}var qv={newInstance:jt.newInstance($v,"vtkRenderPass"),extend:$v};const{Representation:Xv}=ds,{vtkErrorMacro:Yv}=jt;function Zv(e){const t=cd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function Qv(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Es.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Es.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",Xv.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Nd.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Nd.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Nd.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||Yv("Error setting vertexDC in copy shader 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CT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ST(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group 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0!==arguments[2]?arguments[2]:{};Object.assign(t,AT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ST(e,t)}var wT={newInstance:Et(IT,"vtkWebGPURenderEncoder"),extend:IT},PT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const OT=["getMappedRange","mapAsync","unmap"];function RT(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<OT.length;n++)e[OT[n]]=function(){return t.handle[OT[n]](...arguments)}}const MT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function DT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,MT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),RT(e,t)}var ET={newInstance:jt.newInstance(DT),extend:DT,...PT};const{Representation:VT}=ds,{PrimitiveTypes:LT}=PT;class BT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function NT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function _T(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=NT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=NT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=NT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=NT(n,o,t),n.ibo[n.iboId++]=a}function FT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let kT;const GT=new Uint32Array(1),UT=new Uint32Array(2),zT=new Uint32Array(3),WT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)GT[0]=t[n+a],kT(GT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)UT[0]=t[n+a],UT[1]=t[n+(a+1)%e],kT(UT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o);for(let a=0;a<e-2;a++)UT[0]=t[n+a],UT[1]=t[n+a+2],kT(UT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n],zT[1]=t[n+a+1],zT[2]=t[n+a+2],kT(zT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n+a],zT[1]=t[n+a+1+a%2],zT[2]=t[n+a+1+(a+1)%2],kT(zT,r,o)}};function HT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case LT.Points:return"points";case LT.Lines:return"lines";case LT.Triangles:case LT.TriangleEdges:return"polys";case LT.TriangleStripEdges:case LT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new BT};let d=null;d=o===VT.POINTS||r===LT.Points?WT.anythingToPoints:o===VT.WIREFRAME||r===LT.Lines?WT[`${l}ToWireframe`]:WT[`${l}ToSurface`],kT=FT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),kT=_T,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const jT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function KT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jT,n),ET.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),HT(e,t)}var $T={newInstance:jt.newInstance(KT),extend:KT,...PT};const{BufferUsage:qT}=PT,{vtkErrorMacro:XT}=Kt,{VtkDataTypes:YT}=Es;function ZT(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function QT(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function JT(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===qT.Index&&(n=$T.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=ET.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===qT.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===qT.PointArray){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=ZT(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===qT.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=QT(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ZT(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===qT.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case YT.UNSIGNED_CHAR:t="uint8";break;case YT.FLOAT:t="float32";break;case YT.UNSIGNED_INT:t="uint32";break;case YT.INT:t="sint32";break;case YT.DOUBLE:t="float32";break;case YT.UNSIGNED_SHORT:t="uint16";break;case YT.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||XT(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:qT.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=ET.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const eb={device:null,fullScreenQuadBuffer:null};function tb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eb,n),yt(e,t),At(e,t,["device"]),JT(e,t)}var nb={newInstance:Et(tb),extend:tb,...PT};function rb(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const ob={device:null,handle:null,label:null,options:null};function ab(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ob,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),rb(e,t)}var ib={newInstance:jt.newInstance(ab),extend:ab};function sb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(t.texture.getFormat());t.bindGroupLayoutEntry.texture.sampleType=r.sampleType},e.createFromTextureHandle=(e,n)=>{t.texture=null,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e,t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(n.format);t.bindGroupLayoutEntry.texture.sampleType=r.sampleType,t.bindGroupTime.modified()},e.getBindGroupEntry=()=>({resource:e.getHandle()}),e.getShaderCode=(e,n)=>{let r="f32";"sint"===t.bindGroupLayoutEntry.texture.sampleType?r="i32":"uint"===t.bindGroupLayoutEntry.texture.sampleType&&(r="u32");let o=`@binding(${e}) @group(${n}) var ${t.label}: texture_${t.options.dimension}<${r}>;`;return"depth"===t.bindGroupLayoutEntry.texture.sampleType&&(o=`@binding(${e}) @group(${n}) var ${t.label}: texture_depth_${t.options.dimension};`),o},e.addSampler=(n,r)=>{const o=ib.newInstance({label:`${t.label}Sampler`});o.create(n,r),e.setSampler(o)},e.getBindGroupTime=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.bindGroupTime),e.getHandle=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.handle)}const lb={texture:null,handle:null,sampler:null,label:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),jt.obj(e,t),t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,texture:{sampleType:"float",viewDimension:"2d"}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","texture"]),jt.setGet(e,t,["bindGroupLayoutEntry","label","sampler"]),sb(e,t)}var ub={newInstance:jt.newInstance(cb),extend:cb};const{BufferUsage:db}=nb;function pb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return t.device.getHandle().queue.copyExternalImageToTexture({source:n.canvas,flipY:n.flip},{texture:t.handle,premultipliedAlpha:!0},[t.width,t.height,t.depth]),void(t.ready=!0);n.jsImageData&&!n.nativeArray&&(n.width=n.jsImageData.width,n.height=n.jsImageData.height,n.depth=1,n.format="rgba8unorm",n.flip=!0,n.nativeArray=n.jsImageData.data);const o=tT(t.format);let a=t.width*o.stride;const i=(e,t,n)=>{const r=e.length/(t*n)*e.BYTES_PER_ELEMENT,a=2===o.elementSize&&"float"===o.sampleType;if(a||r%256){const i=e,s=r/i.BYTES_PER_ELEMENT,l=o.elementSize,c=256*Math.floor((s*l+255)/256),u=c/l,d=jt.newTypedArray(a?"Uint16Array":i.constructor.name,u*t*n);for(let e=0;e<t*n;e++)if(a)for(let t=0;t<s;t++)d[e*u+t]=Ad.toHalf(i[e*s+t]);else d.set(i.subarray(e*s,(e+1)*s),e*u);return[d,c]}return[e,r]};if(n.nativeArray&&(r=n.nativeArray),n.image){const e=document.createElement("canvas");e.width=n.image.width,e.height=n.image.height;const t=e.getContext("2d");t.translate(0,e.height),t.scale(1,-1),t.drawImage(n.image,0,0,n.image.width,n.image.height,0,0,e.width,e.height),r=t.getImageData(0,0,n.image.width,n.image.height).data}const s=t.device.createCommandEncoder();if(3!==e.getDimensionality()){const e=Tu.generateMipmaps(r,t.width,t.height,t.mipLevel);let o=t.width,l=t.height;for(let r=0;r<=t.mipLevel;r++){const c=i(e[r],l,1);a=c[1];const u={dataArray:n.dataArray?n.dataArray:null,nativeArray:c[0],usage:db.Texture},d=t.device.getBufferManager().getBuffer(u);s.copyBufferToTexture({buffer:d.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:l},{texture:t.handle,mipLevel:r},[o,l,1]),o/=2,l/=2}t.device.submitCommandEncoder(s),t.ready=!0}else{const e=i(r,t.height,t.depth);a=e[1];const o={dataArray:n.dataArray?n.dataArray:null,usage:db.Texture};o.nativeArray=e[0];const l=t.device.getBufferManager().getBuffer(o);s.copyBufferToTexture({buffer:l.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:t.height},{texture:t.handle},[t.width,t.height,t.depth]),t.device.submitCommandEncoder(s),t.ready=!0}},e.getScale=()=>{const e=tT(t.format);return 2===e.elementSize&&"float"===e.sampleType?1:255},e.getNumberOfComponents=()=>tT(t.format).numComponents,e.getDimensionality=()=>{let e=0;return t.width>1&&e++,t.height>1&&e++,t.depth>1&&e++,e},e.resizeToMatch=e=>{e.getWidth()===t.width&&e.getHeight()===t.height&&e.getDepth()===t.depth||(t.width=e.getWidth(),t.height=e.getHeight(),t.depth=e.getDepth(),t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.resize=function(e,n){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;e===t.width&&n===t.height&&r===t.depth||(t.width=e,t.height=n,t.depth=r,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.createView=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};r.dimension||(r.dimension=1===t.depth?"2d":"3d");const o=ub.newInstance({label:n});return o.create(e,r),o}}const fb={device:null,handle:null,buffer:null,ready:!1,label:null};function gb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fb,n),jt.obj(e,t),jt.get(e,t,["handle","ready","width","height","depth","format","usage"]),jt.setGet(e,t,["device","label"]),pb(e,t)}var mb={newInstance:jt.newInstance(gb),extend:gb};function hb(e,t){t.classHierarchy.push("vtkWebGPUOpaquePass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.renderEncoder)t.colorTexture.resize(r.getCanvas().width,r.getCanvas().height),t.depthTexture.resize(r.getCanvas().width,r.getCanvas().height);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"opaquePassColor"}),t.colorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const n=t.colorTexture.createView("opaquePassColorTexture");t.renderEncoder.setColorTextureView(0,n),t.depthFormat="depth32float",t.depthTexture=mb.newInstance({label:"opaquePassDepth"}),t.depthTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:t.depthFormat,usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("opaquePassDepthTexture");t.renderEncoder.setDepthTextureView(a)}t.renderEncoder.attachTextureViews(),e.setCurrentOperation("opaquePass"),n.setRenderEncoder(t.renderEncoder),n.traverse(e)},e.getColorTextureView=()=>t.renderEncoder.getColorTextureViews()[0],e.getDepthTextureView=()=>t.renderEncoder.getDepthTextureView(),e.createRenderEncoder=()=>{t.renderEncoder=wT.newInstance({label:"OpaquePass"}),t.renderEncoder.setPipelineHash("op")}}const vb={renderEncoder:null,colorTexture:null,depthTexture:null};function yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vb,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),hb(e,t)}var Tb={newInstance:jt.newInstance(yb,"vtkWebGPUOpaquePass"),extend:yb};function bb(e,t){t.classHierarchy.push("vtkWebGPUOrderIndependentTranslucentPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.translucentRenderEncoder)t.translucentColorTexture.resizeToMatch(t.colorTextureView.getTexture()),t.translucentAccumulateTexture.resizeToMatch(t.colorTextureView.getTexture());else{e.createRenderEncoder(),e.createFinalEncoder(),t.translucentColorTexture=mb.newInstance({label:"translucentPassColor"}),t.translucentColorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const n=t.translucentColorTexture.createView("oitpColorTexture");t.translucentRenderEncoder.setColorTextureView(0,n),t.translucentAccumulateTexture=mb.newInstance({label:"translucentPassAccumulate"}),t.translucentAccumulateTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=xT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n")}t.translucentRenderEncoder.setDepthTextureView(t.depthTextureView),t.translucentRenderEncoder.attachTextureViews(),e.setCurrentOperation("translucentPass"),n.setRenderEncoder(t.translucentRenderEncoder),n.traverse(e),e.finalPass(r,n)},e.finalPass=(e,n)=>{t.translucentFinalEncoder.setColorTextureView(0,t.colorTextureView),t.translucentFinalEncoder.attachTextureViews(),t.translucentFinalEncoder.begin(e.getCommandEncoder()),n.scissorAndViewport(t.translucentFinalEncoder),t.fullScreenQuad.prepareAndDraw(t.translucentFinalEncoder),t.translucentFinalEncoder.end()},e.getTextures=()=>[t.translucentColorTexture,t.translucentAccumulateTexture],e.createRenderEncoder=()=>{t.translucentRenderEncoder=wT.newInstance({label:"translucentRender"});const e=t.translucentRenderEncoder.getDescription();e.colorAttachments=[{view:void 0,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:void 0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = computedColor.a;"]).result,t.setCode(n)})),t.translucentRenderEncoder.setPipelineHash("oitpr"),t.translucentRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!1,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one"},alpha:{srcfactor:"one",dstFactor:"one"}}},{format:"r16float",blend:{color:{srcFactor:"zero",dstFactor:"one-minus-src"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createFinalEncoder=()=>{t.translucentFinalEncoder=wT.newInstance({label:"translucentFinal"}),t.translucentFinalEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t.translucentFinalEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t.translucentFinalEncoder.setPipelineHash("oitpf"),t.translucentFinalEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})}}const xb={colorTextureView:null,depthTextureView:null};function Cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xb,n),qv.extend(e,t,n),jt.setGet(e,t,["colorTextureView","depthTextureView"]),bb(e,t)}var Sb={newInstance:jt.newInstance(Cb,"vtkWebGPUOrderIndependentTranslucentPass"),extend:Cb};const{BufferUsage:Ab}=nb,{vtkErrorMacro:Ib}=jt;function wb(e,t){t.classHierarchy.push("vtkWebGPUUniformBuffer"),e.addEntry=(e,n)=>{t._bufferEntryNames.has(e)?Ib(`entry named ${e} already 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o=t.bufferEntries[r];o.packed||(o.packed=!0,o.offset=e,n.push(o),e+=o.sizeInBytes)}t.bufferEntries=n,t._bufferEntryNames.clear();for(let e=0;e<t.bufferEntries.length;e++)t._bufferEntryNames.set(t.bufferEntries[e].name,e);t.sizeInBytes=e,t.sizeInBytes=r*Math.ceil(t.sizeInBytes/r),t.sortDirty=!1},e.sendIfNeeded=e=>{if(!t.UBO){const n={nativeArray:t.Float32Array,usage:Ab.UniformArray,label:t.label};t.UBO=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),t.sendDirty=!1}t.sendDirty&&(e.getHandle().queue.writeBuffer(t.UBO.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes),t.sendDirty=!1),t.sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];a.lastValue!==r&&(i[a.offset/i.BYTES_PER_ELEMENT]=r,t.sendDirty=!0),a.lastValue=r},e.setArray=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];let s=!1;for(let e=0;e<r.length;e++)a.lastValue&&a.lastValue[e]===r[e]||(i[a.offset/i.BYTES_PER_ELEMENT+e]=r[e],s=!0);s&&(t.sendDirty=!0,a.lastValue=[...r])},e.getBindGroupEntry=()=>({resource:{buffer:t.UBO.getHandle()}}),e.getSendTime=()=>t.sendTime.getMTime(),e.getShaderCode=(n,r)=>{e.sortBufferEntries();const o=[`struct ${t.label}Struct\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];o.push(`  ${n.name}: ${n.type},`)}return o.push(`};\n@binding(${n}) @group(${r}) var<uniform> ${t.label}: ${t.label}Struct;`),o.join("\n")}}const Pb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Ob(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),wb(e,t)}var Rb={newInstance:jt.newInstance(Ob,"vtkWebGPUUniformBuffer"),extend:Ob};const{BufferUsage:Mb}=nb,{vtkErrorMacro:Db}=jt;function Eb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void Db(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void Db(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Vb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function Lb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Eb(e,t)}var Bb={newInstance:jt.newInstance(Lb,"vtkWebGPUStorageBuffer"),extend:Lb};const Nb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",_b=new Float64Array(16),Fb=new Float64Array(16);function kb(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Uy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Rg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Uy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Uy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Uy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Rg.COMPOSITE_BLEND?l=!0:n===Rg.MAXIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.MINIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.AVERAGE_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.ADDITIVE_INTENSITY_BLEND&&(a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Uy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(_b),b(_b,_b,o);const p=n.getMatrix();m(Fb,p),h(Fb,Fb),T(_b,Fb,_b);const f=d.getWorldToIndex();T(_b,f,_b);const v=d.getDimensions();g(Fb),x(Fb,Fb,[1/v[0],1/v[1],1/v[2]]),T(_b,Fb,_b);for(let t=0;t<16;t++)a[16*e+t]=_b[t];h(_b,_b);for(let t=0;t<4;t++)i[16*e+4*t]=_b[4*t],i[16*e+4*t+1]=_b[4*t+1],i[16*e+4*t+2]=_b[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=tT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=ib.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const Gb={volumes:null,rowLength:1024,lastVolumeLength:0};function Ub(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gb,n),xT.extend(e,t,n),t.fragmentShaderTemplate=Nb,t.UBO=Rb.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Bb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Bb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),kb(e,t)}var zb={newInstance:jt.newInstance(Ub,"vtkWebGPUVolumePassFSQ"),extend:Ub};const{Representation:Wb}=ds,{BufferUsage:Hb,PrimitiveTypes:jb}=nb,Kb=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],$b="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function qb(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=zb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=xT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=Rb.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Hb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:jb.Triangles,representation:Wb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Hb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+Kb[e][0],l[n++]=r+Kb[e][1],l[n++]=r+Kb[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=wT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=mb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=mb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=mb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=wT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=wT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=wT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const Xb={colorTextureView:null,depthTextureView:null,volumes:null};function Yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Xb,n),qv.extend(e,t,n),t._mapper=vT.newInstance(),t._mapper.setFragmentShaderTemplate($b),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=gu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),qb(e,t)}var Zb={newInstance:jt.newInstance(Yb,"vtkWebGPUVolumePass"),extend:Yb};function Qb(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Tb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Sb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=Zb.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=wT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=ib.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=xT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=ub.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const Jb={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function ex(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jb,n),qv.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),Qb(e,t)}var tx={newInstance:jt.newInstance(ex,"vtkForwardPass"),extend:ex};const{VtkDataTypes:nx}=Es;function rx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case nx.UNSIGNED_CHAR:e.format+="8unorm";break;case nx.FLOAT:case nx.UNSIGNED_INT:case nx.INT:case nx.DOUBLE:case nx.UNSIGNED_SHORT:case nx.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=mb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const ox={handle:null,device:null};function ax(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ox,n),jt.obj(e,t),jt.setGet(e,t,["device"]),rx(e,t)}var ix={newInstance:jt.newInstance(ax),extend:ax};class sx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function lx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const cx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function ux(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new sx,t.shaderCache=Uy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=nb.newInstance(),t.bufferManager.setDevice(e),t.textureManager=ix.newInstance(),t.textureManager.setDevice(e),t.pipelines={},lx(e,t)}var dx={newInstance:Et(ux,"vtkWebGPUDevice"),extend:ux};function px(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=mb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=wT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const fx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function gx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fx,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),px(e,t)}var mx={newInstance:jt.newInstance(gx,"vtkWebGPUHardwareSelectionPass"),extend:gx};const{SelectionContent:hx,SelectionField:vx}=qp,{FieldAssociations:yx}=ol,{vtkErrorMacro:Tx}=jt;function bx(e){return`${e.propID} ${e.compositeID}`}function xx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Cx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=xx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=xx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Cx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Cx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Cx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Cx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Cx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Sx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Tx("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=ET.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=ET.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Cx(e,[n,t],0,u);if(r){const t=bx(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=qp.newInstance();switch(i.setContentType(hx.INDICES),e){case yx.FIELD_ASSOCIATION_CELLS:i.setFieldType(vx.CELL);break;case yx.FIELD_ASSOCIATION_POINTS:i.setFieldType(vx.POINT);break;default:Tx("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Ax={};function Ix(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ax,n),Gp.extend(e,t,n),t._selectionPass=mx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Sx(e,t)}var wx={newInstance:jt.newInstance(Ix,"vtkWebGPUHardwareSelector"),extend:Ix};const Px=Object.create(null);function Ox(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const Rx={};function Mx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rx,n),t.overrides=Px,tn.extend(e,t,n),Ox(0,t)}var Dx={newInstance:jt.newInstance(Mx,"vtkWebGPUViewNodeFactory"),extend:Mx};const{vtkErrorMacro:Ex}=jt,Vx={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function Lx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Ex("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Ex("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=dx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=qv.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Vx,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=ET.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Ad.fromHalf(r.colorValues[o]),s[n+1]=255*Ad.fromHalf(r.colorValues[o+1]),s[n+2]=255*Ad.fromHalf(r.colorValues[o+2]),s[n+3]=255*Ad.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=wx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Bx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Nx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",xy.extend(e,t,n),t.myFactory=Dx.newInstance(),t.myFactory.registerOverride("vtkRenderWindow",Nx),t.renderPasses[0]=tx.newInstance(),t.selector||(t.selector=wx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),Lx(e,t)}),"vtkWebGPURenderWindow");ev("WebGPU",Nx);const _x=Hv(),Fx={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},kx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Gx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Ux(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Gx(t.container,t.containerStyle||Fx),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=av.newInstance(),t.renderer=Qh.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(_x.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Sv.newInstance(),t.interactor.setInteractorStyle(Uv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Gx(t.controlContainer,t.controlPanelStyle||kx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",(t=>{"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}))},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Wx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Ux(e,t)}var Hx={newInstance:jt.newInstance(Wx),extend:Wx},jx={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:Kx,Scale:$x}=jx,{ScalarMappingTarget:qx}=Pl,{vtkDebugMacro:Xx,vtkErrorMacro:Yx,vtkWarningMacro:Zx}=jt;function Qx(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function Jx(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function eC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];Qx(o,i),Qx(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=Jx(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=Jx(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function tC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return Yx("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return Yx("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===$x.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Do(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===Kx.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===Kx.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===Kx.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===Kx.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return Yx("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(Yx("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return Yx("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>t.indexedLookup&&e.getSize()?e.getSize():t.tableSize?t.tableSize:16777216,e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void Yx("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):Xx("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void Zx("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===qx.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===qx.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===qx.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===qx.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=Kx[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(Yx(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=Kx.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const nC={clamping:!0,colorSpace:Kx.RGB,hSVWrap:!0,scale:$x.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function rC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","colorSpace","discretize","numberOfValues"]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),tC(e,t)}var oC={newInstance:jt.newInstance(rC,"vtkColorTransferFunction"),extend:rC,...jx},aC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:iC,ScaleModes:sC}=aC,{vtkErrorMacro:lC}=jt;function cC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(iC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(iC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(iC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(iC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(sC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(sC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(sC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(sC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===sC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(lC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case iC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case iC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case iC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case sC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case sC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case sC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const uC={orient:!0,orientationMode:iC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:sC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function dC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,uC,n),nc.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),cC(e,t)}var pC={newInstance:jt.newInstance(dC,"vtkGlyph3DMapper"),extend:dC,...aC};const{vtkErrorMacro:fC}=jt;function gC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(fC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return fC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return fC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return fC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const i={x:n,y:r,midpoint:o,sharpness:a};let s;for(t.nodes.push(i),e.sortAndUpdateRange(),s=0;s<t.nodes.length&&t.nodes[s].x!==n;s++);return s<t.nodes.length?s:-1},e.setNodes=n=>{t.nodes!==n&&(t.nodes=n,e.sortAndUpdateRange())},e.sortAndUpdateRange=()=>{t.nodes.sort(((e,t)=>e.x-t.x)),e.updateRange()||e.modified()},e.updateRange=()=>{const n=t.range.slice(),r=t.nodes.length;return r?(t.range[0]=t.nodes[0].x,t.range[1]=t.nodes[r-1].x):(t.range[0]=0,t.range[1]=0),(n[0]!==t.range[0]||n[1]!==t.range[1])&&(e.modified(),!0)},e.removePoint=n=>{let r;for(r=0;r<t.nodes.length&&t.nodes[r].x!==n;r++);if(r>=t.nodes.length)return-1;const o=r;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addSegment=(n,r,o,a)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=o?t.nodes.splice(e,1):e++;e.addPoint(n,r,.5,0),e.addPoint(o,a,.5,0)},e.getValue=t=>{const n=[];return e.getTable(t,t,1,n),n[0]},e.adjustRange=n=>{if(n.length<2)return 0;const r=e.getRange();r[0]<n[0]?e.addPoint(n[0],e.getValue(n[0])):e.addPoint(n[0],e.getValue(r[0])),r[1]>n[1]?e.addPoint(n[1],e.getValue(n[1])):e.addPoint(n[1],e.getValue(r[1])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return e.sortAndUpdateRange(),1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{const e=t.nodes.length;if(e<2)return-1;let n=t.nodes[1].x-t.nodes[0].x;for(let r=0;r<e-1;r++){const e=t.nodes[r+1].x-t.nodes[r].x;e<n&&(n=e)}return n},e.getTable=function(e,n,r,o){let a,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=0;const l=t.nodes.length;let c=0;0!==l&&(c=t.nodes[l-1].y);let u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const mC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function hC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),gC(e,t)}var vC={newInstance:jt.newInstance(hC,"vtkPiecewiseFunction"),extend:hC};const{InterpolationType:yC,OpacityMode:TC}=Og,{vtkErrorMacro:bC}=jt;function xC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(bC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=vC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=oC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=vC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return bC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(bC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(yC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(yC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(yC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(yC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const CC={independentComponents:!0,interpolationType:yC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function SC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,CC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:TC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),xC(e,t)}var AC={newInstance:jt.newInstance(SC,"vtkVolumeProperty"),extend:SC,...Og};const{vtkDebugMacro:IC}=jt;function wC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=AC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){IC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const PC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function OC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),wC(e,t)}var RC={newInstance:jt.newInstance(OC,"vtkVolume"),extend:OC};const{BlendMode:MC,FilterMode:DC}=Mg,EC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=vC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function VC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(MC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(MC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(MC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(MC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(MC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(MC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(MC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(DC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(DC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(DC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(DC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Do(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const LC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:MC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:DC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function BC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LC,n),Bs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),VC(e,t)}var NC={newInstance:jt.newInstance(BC,"vtkVolumeMapper"),extend:BC,...EC};const{InterpolationType:_C}=eg,{vtkErrorMacro:FC}=jt;function kC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return FC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(FC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(_C.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(_C.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(_C,t.interpolationType)}const GC={independentComponents:!1,interpolationType:_C.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function UC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,GC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),kC(e,t)}var zC={newInstance:jt.newInstance(UC,"vtkImageProperty"),extend:UC};const{vtkDebugMacro:WC}=jt;function HC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=zC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){WC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const jC={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function KC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),HC(e,t)}var $C={newInstance:jt.newInstance(KC,"vtkImageSlice"),extend:KC};function qC(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const XC={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var YC=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,XC,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),qC(e,t)};function ZC(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:QC,otherStaticMethods:JC}=Ul,{SlicingMode:eS}=sg;function tS(e,t){function n(){let n;switch(t.slicingMode){case eS.X:n=0;break;case eS.Y:n=1;break;case eS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case eS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case eS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case eS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case eS.I:l=Ma(a[0],i[0],i[1]);break;case eS.J:l=Ma(a[1],i[2],i[3]);break;case eS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case eS.I:case eS.J:case eS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case eS.X:e.setSlice(r[0]);break;case eS.Y:e.setSlice(r[1]);break;case eS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(eS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(eS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(eS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(eS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(eS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(eS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case eS.X:n[0]=1;break;case eS.Y:n[1]=1;break;case eS.Z:n[2]=1;break;case eS.I:ea(r,[1,0,0],n);break;case eS.J:ea(r,[0,1,0],n);break;case eS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==eS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case eS.I:r[0]=a,r[1]=a;break;case eS.J:r[2]=a,r[3]=a;break;case eS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case eS.I:a[0]=s-r,a[1]=s+r;break;case eS.J:a[2]=s-r,a[3]=s+r;break;case eS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const nS={slicingMode:eS.NONE,closestIJKAxis:{ijkMode:eS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function rS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nS,n),YC(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),Ul.implementCoincidentTopologyMethods(e,t),tS(e,t)}var oS={newInstance:jt.newInstance(rS,"vtkImageMapper"),extend:rS,...QC,...JC,...sg};const{vtkErrorMacro:aS}=jt;function iS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function sS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void aS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=gu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=mc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Es.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Es.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Es.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),iS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),iS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),iS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),iS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const lS={outputPointsPrecision:js.DEFAULT};function cS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),sS(e,t)}var uS={newInstance:jt.newInstance(cS,"vtkAppendPolyData"),extend:cS};function dS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Nf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=gu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const pS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function fS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),dS(e,t)}var gS={newInstance:jt.newInstance(fS,"vtkConeSource"),extend:fS};function mS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Es.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Es.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Nf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=gu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const hS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function vS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),mS(e,t)}var yS={newInstance:jt.newInstance(vS,"vtkCylinderSource"),extend:vS};function TS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=yS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Bf().rotateZ(-90).apply(a).apply(i);const s=gS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Nf().translate(1-.5*t.tipLength,0,0).apply(c);const u=uS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Nf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Nf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Nf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const bS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function xS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),TS(e,t)}var CS={newInstance:jt.newInstance(xS,"vtkArrowSource"),extend:xS};function SS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Bf().translate(...n).apply(e.getPoints().getData())}function AS(e,t){const n=e.getPoints().getBounds(),r=[0,0,0];r[t]=-n[2*t],Bf().translate(...r).apply(e.getPoints().getData())}function IS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Es.newInstance({name:"color",numberOfComponents:3,values:a}))}function wS(e,t){t.classHierarchy.push("vtkAxesActor");const n=nc.newInstance();e.setMapper(n),e.update=()=>{const e=CS.newInstance({direction:[1,0,0],...t.config}).getOutputData();t.config.recenter?SS(e):AS(e,0),IS(e,...t.xAxisColor);const r=CS.newInstance({direction:[0,1,0],...t.config}).getOutputData();t.config.recenter?SS(r):AS(r,1),IS(r,...t.yAxisColor);const o=CS.newInstance({direction:[0,0,1],...t.config}).getOutputData();t.config.recenter?SS(o):AS(o,2),IS(o,...t.zAxisColor);const a=uS.newInstance();a.setInputData(e),a.addInputData(r),a.addInputData(o),n.setInputConnection(a.getOutputPort())},e.update();const r=jt.debounce(e.update,0),{setConfig:o,setXAxisColor:a,setYAxisColor:i,setZAxisColor:s}=e;e.setConfig=e=>!!o(e)&&(r(),!0),e.setXAxisColor=e=>!!a(e)&&(r(),!0),e.setYAxisColor=e=>!!i(e)&&(r(),!0),e.setZAxisColor=e=>!!s(e)&&(r(),!0)}const PS={config:{recenter:!0,tipResolution:60,tipRadius:.1,tipLength:.2,shaftResolution:60,shaftRadius:.03,invert:!1},xAxisColor:[255,0,0],yAxisColor:[255,255,0],zAxisColor:[0,128,0]};function OS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PS,n),hs.extend(e,t,n),jt.setGet(e,t,["config"]),jt.setGetArray(e,t,["xAxisColor","yAxisColor","zAxisColor"],3,255),wS(e,t)}var RS={newInstance:jt.newInstance(OS,"vtkAxesActor"),extend:OS};const MS="resetcamera",DS="orientation",ES={MODE_RESET_CAMERA:MS,MODE_ORIENTATION:DS,MODE_SAME:"same"};function 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i,s,l;a&&(t.mode===DS?(r[0]=a[0]-a[3],r[1]=a[1]-a[4],r[2]=a[2]-a[5],qo(r),o.setPosition(t.focalPoint[0]+t.distance*r[0],t.focalPoint[1]+t.distance*r[1],t.focalPoint[2]+t.distance*r[2]),o.setFocalPoint(t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),o.setViewUp(a[6],a[7],a[8])):(o.setPosition(a[0],a[1],a[2]),o.setFocalPoint(a[3],a[4],a[5]),o.setViewUp(a[6],a[7],a[8])),t.mode===MS&&t.dstRenderer.resetCamera())},e.delete=Vt((()=>e.setSrcRenderer(null)),e.delete),a()}const LS={mode:DS,focalPoint:[0,0,0],distance:6.8,active:!0};function BS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LS,n),yt(e,t),At(e,t,["mode","active","srcRenderer","dstRenderer","distance"]),Pt(e,t,["focalPoint"],3,0),VS(e,t)}var NS={newInstance:Et(BS,"vtkCameraSynchronizer"),extend:BS,SynchronizationMode:ES};const _S={},FS={CameraSync:class{constructor(e,t){this.ctx=e,this.behavior=NS.newInstance(this.getProperties(t)),this.behavior.update()}getProperties(e){let{actorBounds:t,srcRenderer:n,dstRenderer:r}=e;return{distance:3.4*Math.max(t[1]-t[0],t[3]-t[2],t[5]-t[4]),focalPoint:[.5*(t[0]+t[1]),.5*(t[2]+t[3]),.5*(t[4]+t[5])],mode:NS.SynchronizationMode.MODE_ORIENTATION,srcRenderer:this.ctx.getInstance(n),dstRenderer:this.ctx.getInstance(r)}}update(e){this.behavior.set(this.getProperties(e)),this.behavior.update()}delete(){this.behavior.delete()}}};var kS=function(e,t,n){const r=e.get("synchronizedViewId").synchronizedViewId;if(!t.behaviors||!r)return;_S[r]||(_S[r]={});const o=_S[r];if(t.behaviors.autoOrientation){const t=e.getRenderers();if(!o.autoOrientationAxes&&2===t.length){let e=null,n=null;for(let r=0;r<t.length;r++){const o=t[r];o.getInteractive()?e=o:n=o}e&&n&&(o.autoOrientationAxes=NS.newInstance({srcRenderer:e,dstRenderer:n}))}o.autoOrientationAxes&&2!==t.length&&(o.autoOrientationAxes.delete(),delete o.autoOrientationAxes)}const a=Object.keys(t.behaviors),i=Object.keys(o);for(let e=0;e<a.length;e++){const r=a[e];if(o[r])o[r].update(t.behaviors[r]);else{const e=t.behaviors[r];FS[e.type]?o[r]=new FS[e.type](n,e):console.log("No mapping for",e)}}for(let e=0;e<i.length;e++){const n=a[e];t.behaviors[n]||(o[n].delete(),delete o[n])}};const GS={},US=/instance:\${([^}]+)}/,zS={},WS=[],HS={},jS={vtkPoints:mc,vtkCellArray:cc,vtkDataArray:Es};function KS(e){return e.map((e=>US.exec(e))).filter((e=>e)).map((e=>e[1]))}function $S(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return e.dependencies&&e.dependencies.forEach((e=>{t.push(e.id),$S(e,t)})),t}function qS(e,t,n,r){if(!t)return Promise.reject(new Error("No instance provided."));const o=GS[e];return 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";
+var base64Str = 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v[0]=e,v[1]=t,v[2]=n,v[3]=r,v[4]=o,v[5]=a,v[6]=s,v[7]=l,v[8]=c,v[9]=u,v[10]=d,v[11]=p,v[12]=f,v[13]=g,v[14]=m,v[15]=h,v}function f(e,t,n,r,o,a,i,s,l,c,u,d,p,f,g,m,h){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e[9]=u,e[10]=d,e[11]=p,e[12]=f,e[13]=g,e[14]=m,e[15]=h,e}function g(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function m(e,t){if(e===t){var n=t[1],r=t[2],o=t[3],a=t[6],i=t[7],s=t[11];e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=n,e[6]=t[9],e[7]=t[13],e[8]=r,e[9]=a,e[11]=t[14],e[12]=o,e[13]=i,e[14]=s}else e[0]=t[0],e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=t[1],e[5]=t[5],e[6]=t[9],e[7]=t[13],e[8]=t[2],e[9]=t[6],e[10]=t[10],e[11]=t[14],e[12]=t[3],e[13]=t[7],e[14]=t[11],e[15]=t[15];return e}function h(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15],y=n*s-r*i,T=n*l-o*i,b=n*c-a*i,x=r*l-o*s,C=r*c-a*s,S=o*c-a*l,A=u*m-d*g,I=u*h-p*g,w=u*v-f*g,P=d*h-p*m,O=d*v-f*m,R=p*v-f*h,M=y*R-T*O+b*P+x*w-C*I+S*A;return M?(M=1/M,e[0]=(s*R-l*O+c*P)*M,e[1]=(o*O-r*R-a*P)*M,e[2]=(m*S-h*C+v*x)*M,e[3]=(p*C-d*S-f*x)*M,e[4]=(l*w-i*R-c*I)*M,e[5]=(n*R-o*w+a*I)*M,e[6]=(h*b-g*S-v*T)*M,e[7]=(u*S-p*b+f*T)*M,e[8]=(i*O-s*w+c*A)*M,e[9]=(r*w-n*O-a*A)*M,e[10]=(g*C-m*b+v*y)*M,e[11]=(d*b-u*C-f*y)*M,e[12]=(s*I-i*P-l*A)*M,e[13]=(n*P-r*I+o*A)*M,e[14]=(m*T-g*x-h*y)*M,e[15]=(u*x-d*T+p*y)*M,e):null}function v(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15];return e[0]=s*(p*v-f*h)-d*(l*v-c*h)+m*(l*f-c*p),e[1]=-(r*(p*v-f*h)-d*(o*v-a*h)+m*(o*f-a*p)),e[2]=r*(l*v-c*h)-s*(o*v-a*h)+m*(o*c-a*l),e[3]=-(r*(l*f-c*p)-s*(o*f-a*p)+d*(o*c-a*l)),e[4]=-(i*(p*v-f*h)-u*(l*v-c*h)+g*(l*f-c*p)),e[5]=n*(p*v-f*h)-u*(o*v-a*h)+g*(o*f-a*p),e[6]=-(n*(l*v-c*h)-i*(o*v-a*h)+g*(o*c-a*l)),e[7]=n*(l*f-c*p)-i*(o*f-a*p)+u*(o*c-a*l),e[8]=i*(d*v-f*m)-u*(s*v-c*m)+g*(s*f-c*d),e[9]=-(n*(d*v-f*m)-u*(r*v-a*m)+g*(r*f-a*d)),e[10]=n*(s*v-c*m)-i*(r*v-a*m)+g*(r*c-a*s),e[11]=-(n*(s*f-c*d)-i*(r*f-a*d)+u*(r*c-a*s)),e[12]=-(i*(d*h-p*m)-u*(s*h-l*m)+g*(s*p-l*d)),e[13]=n*(d*h-p*m)-u*(r*h-o*m)+g*(r*p-o*d),e[14]=-(n*(s*h-l*m)-i*(r*h-o*m)+g*(r*l-o*s)),e[15]=n*(s*p-l*d)-i*(r*p-o*d)+u*(r*l-o*s),e}function y(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8],u=e[9],d=e[10],p=e[11],f=e[12],g=e[13],m=e[14],h=e[15];return(t*i-n*a)*(d*h-p*m)-(t*s-r*a)*(u*h-p*g)+(t*l-o*a)*(u*m-d*g)+(n*s-r*i)*(c*h-p*f)-(n*l-o*i)*(c*m-d*f)+(r*l-o*s)*(c*g-u*f)}function T(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return e[0]===t[0]&&e[1]===t[1]&&e[2]===t[2]&&e[3]===t[3]&&e[4]===t[4]&&e[5]===t[5]&&e[6]===t[6]&&e[7]===t[7]&&e[8]===t[8]&&e[9]===t[9]&&e[10]===t[10]&&e[11]===t[11]&&e[12]===t[12]&&e[13]===t[13]&&e[14]===t[14]&&e[15]===t[15]}function re(e,t){var n=e[0],r=e[1],o=e[2],i=e[3],s=e[4],l=e[5],c=e[6],u=e[7],d=e[8],p=e[9],f=e[10],g=e[11],m=e[12],h=e[13],v=e[14],y=e[15],T=t[0],b=t[1],x=t[2],C=t[3],S=t[4],A=t[5],I=t[6],w=t[7],P=t[8],O=t[9],R=t[10],M=t[11],E=t[12],D=t[13],V=t[14],L=t[15];return 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i!=Float32Array&&(e[1]=0,e[2]=0,e[3]=0,e[5]=0,e[6]=0,e[7]=0),e[0]=1,e[4]=1,e[8]=1,e}function se(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[4],e[4]=t[5],e[5]=t[6],e[6]=t[8],e[7]=t[9],e[8]=t[10],e}function le(e){var t=new i(9);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t}function ce(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function ue(e,t,n,r,o,a,s,l,c){var u=new i(9);return u[0]=e,u[1]=t,u[2]=n,u[3]=r,u[4]=o,u[5]=a,u[6]=s,u[7]=l,u[8]=c,u}function de(e,t,n,r,o,a,i,s,l,c){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e}function pe(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function fe(e,t){if(e===t){var n=t[1],r=t[2],o=t[5];e[1]=t[3],e[2]=t[6],e[3]=n,e[5]=t[7],e[6]=r,e[7]=o}else e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8];return e}function ge(e,t){var 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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t.size>n*r&&(t.size=n*r,e.dataChange()),!0}t.classHierarchy.push("vtkDataArray"),e.dataChange=()=>{t.ranges=null,e.modified()},e.resize=r=>{n(r);const o=r*e.getNumberOfComponents();return t.size!==o&&(t.size=o,e.dataChange(),!0)},e.initialize=()=>{e.resize(0)},e.getElementComponentSize=()=>t.values.BYTES_PER_ELEMENT,e.getComponent=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return t.values[e*t.numberOfComponents+n]},e.setComponent=(n,r,o)=>{o!==t.values[n*t.numberOfComponents+r]&&(t.values[n*t.numberOfComponents+r]=o,e.dataChange())},e.getValue=n=>{const r=n/t.numberOfComponents,o=n%t.numberOfComponents;return e.getComponent(r,o)},e.setValue=(n,r)=>{const o=n/t.numberOfComponents,a=n%t.numberOfComponents;e.setComponent(o,a,r)},e.getData=()=>t.size===t.values.length?t.values:t.values.subarray(0,t.size),e.getRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,r=n;r<0&&(r=1===t.numberOfComponents?0:t.numberOfComponents);let 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0!==arguments[0]?arguments[0]:1;return e*t.numberOfComponents},e.getNumberOfComponents=()=>t.numberOfComponents,e.getNumberOfValues=()=>t.size,e.getNumberOfTuples=()=>t.size/t.numberOfComponents,e.getDataType=()=>t.dataType,e.newClone=()=>Es({empty:!0,name:t.name,dataType:t.dataType,numberOfComponents:t.numberOfComponents}),e.getName=()=>(t.name||(e.modified(),t.name=`vtkDataArray${e.getMTime()}`),t.name),e.setData=(n,r)=>{t.values=n,t.size=n.length,t.dataType=ws(n),r&&(t.numberOfComponents=r),t.size%t.numberOfComponents!=0&&(t.numberOfComponents=1),e.dataChange()},e.getState=()=>{if(t.deleted)return null;const n={...t,vtkClass:e.getClassName()};n.values=Array.from(n.values),delete n.buffer,Object.keys(n).forEach((e=>{n[e]||delete n[e]}));const r={};return Object.keys(n).sort().forEach((e=>{r[e]=n[e]})),r.mtime&&delete r.mtime,r},e.deepCopy=n=>{const r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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u=a>-1?a:r;c<=u*l&&(c=(u+1)*l),t=Ds.newInstance({name:s.getName(),dataType:s.getDataType(),numberOfComponents:l,values:jt.newTypedArray(s.getDataType(),c),size:0}),t.interpolateTuple(u,s,r,s,o,i),e.addArray(t),n.getAttributes(s).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.copyFieldOn=e=>{t.copyFieldFlags[e]=!0},e.copyFieldOff=e=>{t.copyFieldFlags[e]=!1},e.copyAllOn=()=>{t.doCopyAllOn&&!t.doCopyAllOff||(t.doCopyAllOn=!0,t.doCopyAllOff=!1,e.modified())},e.copyAllOff=()=>{!t.doCopyAllOn&&t.doCopyAllOff||(t.doCopyAllOn=!1,t.doCopyAllOff=!0,e.modified())},e.clearFieldFlags=()=>{t.copyFieldFlags={}},e.deepCopy=e=>{t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. The attribute must be a vtkDataArray.`),-1;if(n&&!e.checkNumberOfComponents(n,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1;let i=t[`active${a}`];if(i>=0&&i<t.arrays.length){if(t.arrays[i]===n)return i;e.removeArrayByIndex(i)}return n?(i=e.addArray(n),t[`active${a}`]=i):t[`active${a}`]=-1,e.modified(),t[`active${a}`]},e.getAttributes=t=>n.filter((n=>e[`get${n}`]()===t)),e.setActiveAttributeByName=(t,n)=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e.setActiveAttributeByIndex=(n,o)=>{const a=r(o);if(n>=0&&n<t.arrays.length){if("PEDIGREEIDS"!==a.toUpperCase()){const t=e.getArrayByIndex(n);if(!t.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. Only vtkDataArray subclasses can be set as active attributes.`),-1;if(!e.checkNumberOfComponents(t,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1}return t[`active${a}`]=n,e.modified(),n}return-1===n&&(t[`active${a}`]=n,e.modified()),-1},e.getActiveAttribute=t=>{const n=r(t);return e[`get${n}`]()},e.removeAllArrays=()=>{n.forEach((e=>{t[`active${e}`]=-1})),o.removeAllArrays()},e.removeArrayByIndex=e=>(-1!==e&&n.forEach((n=>{e===t[`active${n}`]?t[`active${n}`]=-1:e<t[`active${n}`]&&(t[`active${n}`]-=1)})),o.removeArrayByIndex(e)),n.forEach((n=>{const r=`active${n}`;e[`get${n}`]=()=>e.getArrayByIndex(t[r]),e[`set${n}`]=t=>e.setAttribute(t,n),e[`setActive${n}`]=t=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e[`copy${n}Off`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!1},e[`copy${n}On`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!0}})),e.initializeAttributeCopyFlags=()=>{t.copyAttributeFlags=[],Object.keys(qs).filter((e=>"ALLCOPY"!==e)).forEach((e=>{t.copyAttributeFlags[qs[e]]=Object.keys($s).filter((e=>"NUM_ATTRIBUTES"!==e)).reduce(((e,t)=>(e[$s[t]]=!0,e)),[])})),t.copyAttributeFlags[qs.COPYTUPLE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.INTERPOLATE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.COPYTUPLE][$s.PEDIGREEIDS]=!1},e.initialize=jt.chain(e.initialize,e.initializeAttributeCopyFlags),t.dataArrays&&Object.keys(t.dataArrays).length&&Object.keys(t.dataArrays).forEach((n=>{t.dataArrays[n].ref||"vtkDataArray"!==t.dataArrays[n].type||e.addArray(Ds.newInstance(t.dataArrays[n]))}));const a=e.shallowCopy;e.shallowCopy=(e,n)=>{a(e,n),t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.shallowCopy(e,n),{data:t}}))},e.initializeAttributeCopyFlags()}const Zs={activeScalars:-1,activeVectors:-1,activeTensors:-1,activeNormals:-1,activeTCoords:-1,activeGlobalIds:-1,activePedigreeIds:-1};function Qs(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zs,n),Hs.extend(e,t,n),jt.setGet(e,t,["activeScalars","activeNormals","activeTCoords","activeVectors","activeTensors","activeGlobalIds","activePedigreeIds"]),t.arrays||(t.arrays={}),Ys(e,t)}var Js={newInstance:jt.newInstance(Qs,"vtkDataSetAttributes"),extend:Qs,...Ks};const el=["pointData","cellData","fieldData"];function tl(e,t){t.classHierarchy.push("vtkDataSet"),el.forEach((e=>{t[e]?t[e]=We(t[e]):t[e]=Js.newInstance()}));const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),el.forEach((n=>{t[n]=Js.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))}}const nl={};function rl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nl,n),jt.obj(e,t),jt.setGet(e,t,el),tl(e,t)}var ol={newInstance:jt.newInstance(rl,"vtkDataSet"),extend:rl,FieldDataTypes:{UNIFORM:0,DATA_OBJECT_FIELD:0,COORDINATE:1,POINT_DATA:1,POINT:2,POINT_FIELD_DATA:2,CELL:3,CELL_FIELD_DATA:3,VERTEX:4,VERTEX_FIELD_DATA:4,EDGE:5,EDGE_FIELD_DATA:5,ROW:6,ROW_DATA:6},FieldAssociations:{FIELD_ASSOCIATION_POINTS:0,FIELD_ASSOCIATION_CELLS:1,FIELD_ASSOCIATION_NONE:2,FIELD_ASSOCIATION_POINTS_THEN_CELLS:3,FIELD_ASSOCIATION_VERTICES:4,FIELD_ASSOCIATION_EDGES:5,FIELD_ASSOCIATION_ROWS:6,NUMBER_OF_ASSOCIATIONS:7}};const al={UNCHANGED:0,SINGLE_POINT:1,X_LINE:2,Y_LINE:3,Z_LINE:4,XY_PLANE:5,YZ_PLANE:6,XZ_PLANE:7,XYZ_GRID:8,EMPTY:9};var il={StructuredType:al};const{StructuredType:sl}=il;var ll={getDataDescriptionFromExtent:function(e){let t=0;for(let n=0;n<3;++n)e[2*n]<e[2*n+1]&&t++;return e[0]>e[1]||e[2]>e[3]||e[4]>e[5]?sl.EMPTY:3===t?sl.XYZ_GRID:2===t?e[0]===e[1]?sl.YZ_PLANE:e[2]===e[3]?sl.XZ_PLANE:sl.XY_PLANE:1===t?e[0]<e[1]?sl.X_LINE:e[2]<e[3]?sl.Y_LINE:sl.Z_LINE:sl.SINGLE_POINT},...il};const{vtkErrorMacro:cl}=jt;function ul(e,t){t.classHierarchy.push("vtkImageData"),e.setExtent=function(){if(t.deleted)return cl("instance deleted - cannot call any method"),!1;for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];const a=1===r.length?r[0]:r;if(6!==a.length)return!1;const i=t.extent.some(((e,t)=>e!==a[t]));return i&&(t.extent=a.slice(),t.dataDescription=ll.getDataDescriptionFromExtent(t.extent),e.modified()),i},e.setDimensions=function(){let n,r,o;if(t.deleted)cl("instance deleted - cannot call any method");else{if(1===arguments.length){const e=arguments.length<=0?void 0:arguments[0];n=e[0],r=e[1],o=e[2]}else{if(3!==arguments.length)return void cl("Bad dimension specification");n=arguments.length<=0?void 0:arguments[0],r=arguments.length<=1?void 0:arguments[1],o=arguments.length<=2?void 0:arguments[2]}e.setExtent(0,n-1,0,r-1,0,o-1)}},e.getDimensions=()=>[t.extent[1]-t.extent[0]+1,t.extent[3]-t.extent[2]+1,t.extent[5]-t.extent[4]+1],e.getNumberOfCells=()=>{const t=e.getDimensions();let n=1;for(let e=0;e<3;e++){if(0===t[e])return 0;t[e]>1&&(n*=t[e]-1)}return n},e.getNumberOfPoints=()=>{const t=e.getDimensions();return t[0]*t[1]*t[2]},e.getPoint=n=>{const r=e.getDimensions();if(0===r[0]||0===r[1]||0===r[2])return cl("Requesting a point from an empty image."),null;const o=new Float64Array(3);switch(t.dataDescription){case al.EMPTY:return null;case al.SINGLE_POINT:break;case al.X_LINE:o[0]=n;break;case al.Y_LINE:o[1]=n;break;case al.Z_LINE:o[2]=n;break;case al.XY_PLANE:o[0]=n%r[0],o[1]=n/r[0];break;case al.YZ_PLANE:o[1]=n%r[1],o[2]=n/r[1];break;case al.XZ_PLANE:o[0]=n%r[0],o[2]=n/r[0];break;case al.XYZ_GRID:o[0]=n%r[0],o[1]=n/r[0]%r[1],o[2]=n/(r[0]*r[1]);break;default:cl("Invalid dataDescription")}const a=[0,0,0];return e.indexToWorld(o,a),a},e.getBounds=()=>e.extentToBounds(e.getSpatialExtent()),e.extentToBounds=e=>ki.transformBounds(e,t.indexToWorld),e.getSpatialExtent=()=>ki.inflate([...t.extent],.5),e.computeTransforms=()=>{w(t.indexToWorld,t.origin),t.indexToWorld[0]=t.direction[0],t.indexToWorld[1]=t.direction[1],t.indexToWorld[2]=t.direction[2],t.indexToWorld[4]=t.direction[3],t.indexToWorld[5]=t.direction[4],t.indexToWorld[6]=t.direction[5],t.indexToWorld[8]=t.direction[6],t.indexToWorld[9]=t.direction[7],t.indexToWorld[10]=t.direction[8],x(t.indexToWorld,t.indexToWorld,t.spacing),h(t.worldToIndex,t.indexToWorld)},e.indexToWorld=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.indexToWorld),n},e.indexToWorldVec3=e.indexToWorld,e.worldToIndex=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.worldToIndex),n},e.worldToIndexVec3=e.worldToIndex,e.indexToWorldBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.indexToWorld,n)},e.worldToIndexBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.worldToIndex,n)},e.onModified(e.computeTransforms),e.computeTransforms(),e.getCenter=()=>ki.getCenter(e.getBounds()),e.computeHistogram=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=[0,0,0,0,0,0];e.worldToIndexBounds(t,r);const o=[0,0,0],a=[0,0,0];ki.computeCornerPoints(r,o,a),ua(o,o),ua(a,a);const i=e.getDimensions();Ea(o,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],o),Ea(a,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],a);const s=i[0],l=i[0]*i[1],c=e.getPointData().getScalars().getData();let u=-1/0,d=1/0,p=0,f=0,g=0;for(let e=o[2];e<=a[2];e++)for(let t=o[1];t<=a[1];t++){let i=o[0]+t*s+e*l;for(let s=o[0];s<=a[0];s++){if(!n||n([s,t,e],r)){const e=c[i];e>u&&(u=e),e<d&&(d=e),p+=e*e,f+=e,g+=1}++i}}const m=g>0?f/g:0,h=g?Math.abs(p/g-m*m):0,v=Math.sqrt(h);return{minimum:d,maximum:u,average:m,variance:h,sigma:v,count:g}},e.computeIncrements=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;const n=[];let r=t;for(let t=0;t<3;++t)n[t]=r,r*=e[2*t+1]-e[2*t]+1;return n},e.computeOffsetIndex=t=>{let[n,r,o]=t;const a=e.getExtent(),i=e.getPointData().getScalars().getNumberOfComponents(),s=e.computeIncrements(a,i);return Math.floor((Math.round(n)-a[0])*s[0]+(Math.round(r)-a[2])*s[1]+(Math.round(o)-a[4])*s[2])},e.getOffsetIndexFromWorld=t=>{const n=e.getExtent(),r=e.worldToIndex(t);for(let e=0;e<3;++e)if(r[e]<n[2*e]||r[e]>n[2*e+1])return cl(`GetScalarPointer: Pixel ${r} is not in memory. Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. Current Scalar numberOfComponents: ${r}`),NaN;const o=e.getOffsetIndexFromWorld(t);return Number.isNaN(o)?o:e.getPointData().getScalars().getComponent(o,n)}}const dl={direction:null,indexToWorld:null,worldToIndex:null,spacing:[1,1,1],origin:[0,0,0],extent:[0,-1,0,-1,0,-1],dataDescription:al.EMPTY};function pl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dl,n),ol.extend(e,t,n),t.direction?Array.isArray(t.direction)&&(t.direction=new Float64Array(t.direction.slice(0,9))):t.direction=pe(new Float64Array(9)),t.indexToWorld=new Float64Array(16),t.worldToIndex=new Float64Array(16),jt.get(e,t,["indexToWorld","worldToIndex"]),jt.setGetArray(e,t,["origin","spacing"],3),jt.setGetArray(e,t,["direction"],9),jt.getArray(e,t,["extent"],6),ul(e,t)}var fl={newInstance:jt.newInstance(pl,"vtkImageData"),extend:pl};const gl={LUMINANCE:1,LUMINANCE_ALPHA:2,RGB:3,RGBA:4};var ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const g=t.numberOfColorsInRange+3,m=t.numberOfColorsInRange+2,h=r?[Math.min(Math.ceil(g/n**0),n),Math.min(Math.ceil(g/n**1),n),Math.min(Math.ceil(g/n**2),n)]:[m,2,1],v=h[0]*h[1]*h[2],y=new Float64Array(v);y.fill(NaN);const T=t.numberOfColorsInRange,b=T+2,x=[0,0,0],C=a[0],S=a[1]-a[0];for(let e=0;e<b;++e){const t=C+S*(e-1)/(T-1);y[(c=h,(l=x)[0]+c[0]*(l[1]+c[1]*l[2]))]=i?10**t:t,ec(x,h)}const A=Ds.newInstance({numberOfComponents:1,values:y}),I=t.lookupTable.mapScalars(A,t.colorMode,0);t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setDimensions(h),t.colorTextureMap.getPointData().setScalars(I),t.lookupTable.setAlpha(s)}var l,c;const u=t.lookupTable.getVectorMode()===Zl.MAGNITUDE&&n.getNumberOfComponents()>1?-1:t.lookupTable.getVectorComponent();t.colorCoordinates=function(e,t,n,r,o,a,i){const s=new Array(arguments.length);for(let e=0;e<arguments.length;++e){const t=arguments[e];s[e]=t.getMTime?.()??t}const l=s.join("/"),c=nc.get(e);if(c&&c.stringHash===l)return c.textureCoordinates;const 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fc={newInstance:jt.newInstance(pc,"vtkCellArray"),extend:pc,...cc};const{vtkErrorMacro:gc}=jt,mc=[1,-1,1,-1,1,-1];function hc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return gc(`getBounds called on an array with components of\n        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n},e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.deepCopy=e=>{e.initialize(t.points,t.pointsIds)},e.getCellDimension=()=>{},e.intersectWithLine=(e,t,n,r,o,a,i)=>{},e.evaluatePosition=(e,t,n,r,o,a)=>{jt.vtkErrorMacro("vtkCell.evaluatePosition is not implemented.")}}const xc={bounds:[-1,-1,-1,-1,-1,-1],pointsIds:[]};function Cc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xc,n),jt.obj(e,t),t.points||(t.points=Tc.newInstance()),jt.get(e,t,["points","pointsIds"]),bc(e,t)}var Sc={newInstance:jt.newInstance(Cc,"vtkCell"),extend:Cc};function Ac(e,t){t.classHierarchy.push("vtkCellLinks"),e.buildLinks=n=>{const r=n.getPoints().getNumberOfPoints(),o=n.getNumberOfCells(),a=new Uint32Array(r);if(n.isA("vtkPolyData")){for(let t=0;t<o;++t){const{cellPointIds:r}=n.getCellPoints(t);r.forEach((t=>{e.incrementLinkCount(t)}))}e.allocateLinks(r),t.maxId=r-1;for(let 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let n=t;for(t>=e.array.length&&(n+=e.array.length);n>e.array.length;)e.array.push({ncells:0,cells:null});e.array.length=n}(t,t.maxId+1)},e.reset=()=>{t.maxId=-1},e.deepCopy=e=>{t.array=[...e.array],t.extend=e.extend,t.maxId=e.maxId},e.incrementLinkCount=e=>{++t.array[e].ncells},e.allocateLinks=e=>{for(let n=0;n<e;++n)t.array[n].cells=new Array(t.array[n].ncells)},e.insertCellReference=(e,n,r)=>{t.array[e].cells[n]=r}}const Ic={array:null,maxId:0,extend:0};function wc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ic,n),jt.obj(e,t),Ac(e,t)}var Pc={newInstance:jt.newInstance(wc,"vtkCellLinks"),extend:wc};const Oc=0,Rc=1,Mc=2,Ec=3,Dc=4,Vc=5,Lc=6,Bc=7,Nc=9,_c=21,Fc=41,kc=42,Gc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],Uc={getClassNameFromTypeId:function(e){return e<Gc.length?Gc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Gc.findIndex(e)},isLinear:function(e){return e<_c||e===Fc||e===kc},hasSubCells:function(e){return e===Lc||e===Dc||e===Mc}};function zc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Oc},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let r=0;r<n;++r)if(t===e.getCellType(r))return!0;return!1},e.insertNextType=t=>e.insertNextCell(t,-1),e.getCellType=e=>t.typeArray[e],e.reset=()=>{t.maxId=-1},e.deepCopy=n=>{e.allocate(n.getSize(),n.getExtend()),t.typeArray.set(n.getTypeArray()),t.locationArray.set(n.getLocationArray()),t.maxId=n.getMaxId()}}const Wc={size:0,maxId:-1,extend:1e3};function Hc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wc,n),jt.obj(e,t),jt.get(e,t,["size","maxId","extend"]),jt.getArray(e,t,["typeArray","locationArray"]),zc(e,t)}var jc={newInstance:jt.newInstance(Hc,"vtkCellTypes"),extend:Hc,...Uc},Kc={IntersectionState:{NO_INTERSECTION:0,YES_INTERSECTION:1,ON_LINE:2}};const{IntersectionState:$c}=Kc;function qc(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;const o={t:Number.MIN_VALUE,distance:0},a=[];let i;a[0]=n[0]-t[0],a[1]=n[1]-t[1],a[2]=n[2]-t[2];const s=a[0]*(e[0]-t[0])+a[1]*(e[1]-t[1])+a[2]*(e[2]-t[2]),l=jo(a,a);let c=1e-5*s;return 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u=[],d=[],p=Xc(e,n,l,c,u,d);var f;if(i.t=u[0],i.betweenPoints=(f=i.t)>=0&&f<=1,a[0]=d[0],p===$c.YES_INTERSECTION){for(let t=0;t<3;t++)o[t]=l[t]+a[0]*(c[t]-l[t]),s[t]=e[t]+i.t*(n[t]-e[t]);if(Yo(o,s)<=r*r)return i.intersect=1,i}else{let t;if(i.t<0)return t=qc(e,l,c,o),t.distance<=r*r?(i.t=0,i.intersect=1,i.betweenPoints=!0,i):i;if(i.t>1)return t=qc(n,l,c,o),t.distance<=r*r?(i.t=1,i.intersect=1,i.betweenPoints=!0,i):i;if(a[0]<0)return a[0]=0,t=qc(l,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i;if(a[0]>1)return a[0]=1,t=qc(c,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i}return i},e.evaluateLocation=(e,n,r)=>{const o=[],a=[];t.points.getPoint(0,o),t.points.getPoint(1,a);for(let t=0;t<3;t++)n[t]=o[t]+e[0]*(a[t]-o[t]);r[0]=1-e[0],r[1]=e[0]},e.evaluateOrientation=(e,n,r)=>!!t.orientations&&(function(e,t,n,r){var o,i,s,l,c,u=t[0],d=t[1],p=t[2],f=t[3],g=n[0],m=n[1],h=n[2],v=n[3];(i=u*g+d*m+p*h+f*v)<0&&(i=-i,g=-g,m=-m,h=-h,v=-v),1-i>a?(o=Math.acos(i),s=Math.sin(o),l=Math.sin((1-r)*o)/s,c=Math.sin(r*o)/s):(l=1-r,c=r),e[0]=l*u+c*g,e[1]=l*d+c*m,e[2]=l*p+c*h,e[3]=l*f+c*v}(n,t.orientations[0],t.orientations[1],e[0]),r[0]=1-e[0],r[1]=e[0],!0)}const Qc={orientations:null};function Jc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Qc,n),Sc.extend(e,t,n),jt.setGet(e,t,["orientations"]),Zc(e,t)}var eu={newInstance:jt.newInstance(Jc,"vtkLine"),extend:Jc,...Yc,...Kc};function tu(e,t){t.classHierarchy.push("vtkPointSet"),t.points?t.points=We(t.points):t.points=Tc.newInstance(),e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.getBounds=()=>t.points.getBounds(),e.computeBounds=()=>{e.getBounds()};const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),t.points=Tc.newInstance(),t.points.shallowCopy(e.getPoints())}}const nu={};function ru(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nu,n),ol.extend(e,t,n),jt.setGet(e,t,["points"]),tu(e,t)}var ou={newInstance:jt.newInstance(ru,"vtkPointSet"),extend:ru};function au(e,t,n,r){const o=n[0]-t[0],a=n[1]-t[1],i=n[2]-t[2],s=e[0]-t[0],l=e[1]-t[1],c=e[2]-t[2];r[0]=a*c-i*l,r[1]=i*s-o*c,r[2]=o*l-a*s}function iu(e,t,n,r){au(e,t,n,r);const o=Math.sqrt(r[0]*r[0]+r[1]*r[1]+r[2]*r[2]);0!==o&&(r[0]/=o,r[1]/=o,r[2]/=o)}const su={computeNormalDirection:au,computeNormal:iu,intersectWithTriangle:function(e,t,n,r,o,a){let i=arguments.length>6&&void 0!==arguments[6]?arguments[6]:1e-6,s=!1;const l=[],c=[],u=[],d=[],p=[];iu(e,t,n,d),iu(r,o,a,p);const f=-jo(d,e),g=-jo(p,r),m=[jo(p,e)+g,jo(p,t)+g,jo(p,n)+g];if(m[0]*m[1]>i&&m[0]*m[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible buffer.",!1),e.bind=()=>!!o&&(t.context.bindBuffer(n(r),o),!0),e.release=()=>!!o&&(t.context.bindBuffer(n(r),null),!0),e.releaseGraphicsResources=()=>{null!==o&&(t.context.bindBuffer(n(r),null),t.context.deleteBuffer(o),o=null,t.allocatedGPUMemoryInBytes=0)},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getError=()=>i}const Zu={objectType:Xu.ARRAY_BUFFER,context:null,allocatedGPUMemoryInBytes:0};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),jt.obj(e,t),jt.get(e,t,["_openGLRenderWindow","allocatedGPUMemoryInBytes"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu,...qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkOpenGLCellArrayBufferObject"),e.setType($u.ARRAY_BUFFER),e.createVBO=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * 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vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=mg(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.oglObject?.getHandle()&&m?.hash===g)t.colorTexture=m.oglObject;else{t.colorTexture=Ud.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(n*p*3);if(c===og.NEAREST?(t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST)):(t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR)),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader 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Image;n.crossOrigin&&(o.crossOrigin=n.crossOrigin),o.onload=()=>e(o),o.onerror=r,o.src=t}))}};zm("http",(e=>kh));const{vtkDebugMacro:Gh}=jt;function Uh(e,t){t.classHierarchy.push("vtkCamera");const n=new Float64Array(3),r=new Float64Array([0,0,-1]),o=new Float64Array([0,1,0]),a=g(new Float64Array(16)),i=g(new Float64Array(16)),s=new Float64Array(3),l=new Float64Array(3),c=new Float64Array(3),u=g(new Float64Array(16)),p=g(new Float64Array(16)),f=new Float64Array(3),v=new Float64Array(3);function y(){t.viewPlaneNormal[0]=-t.directionOfProjection[0],t.viewPlaneNormal[1]=-t.directionOfProjection[1],t.viewPlaneNormal[2]=-t.directionOfProjection[2]}e.orthogonalizeViewUp=()=>{const n=e.getViewMatrix();t.viewUp[0]=n[4],t.viewUp[1]=n[5],t.viewUp[2]=n[6],e.modified()},e.setPosition=(n,r,o)=>{n===t.position[0]&&r===t.position[1]&&o===t.position[2]||(t.position[0]=n,t.position[1]=r,t.position[2]=o,e.computeDistance(),e.modified())},e.setFocalPoint=(n,r,o)=>{n===t.focalPoint[0]&&r===t.focalPoint[1]&&o===t.focalPoint[2]||(t.focalPoint[0]=n,t.focalPoint[1]=r,t.focalPoint[2]=o,e.computeDistance(),e.modified())},e.setDistance=n=>{if(t.distance===n)return;t.distance=n,t.distance<1e-20&&(t.distance=1e-20,Gh("Distance is set to minimum."));const r=t.directionOfProjection;t.focalPoint[0]=t.position[0]+r[0]*t.distance,t.focalPoint[1]=t.position[1]+r[1]*t.distance,t.focalPoint[2]=t.position[2]+r[2]*t.distance,e.modified()},e.computeDistance=()=>{const e=t.focalPoint[0]-t.position[0],n=t.focalPoint[1]-t.position[1],r=t.focalPoint[2]-t.position[2];if(t.distance=Math.sqrt(e*e+n*n+r*r),t.distance<1e-20){t.distance=1e-20,Gh("Distance is set to minimum.");const e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let 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Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId 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pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an 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t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const 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o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const Mv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Ev(e,t){let n=arguments.length>2&&void 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let 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n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = 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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],D=r[2],E=Math.hypot(R,M,D);return E<a?null:(R*=E=1/E,M*=E,D*=E,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+D*o,C=D*R*s-M*o,S=R*M*s-D*o,A=M*M*s+i,I=D*M*s+R*o,w=R*D*s+M*o,P=M*D*s-R*o,O=D*D*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),E(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,D=(1-(p+v))*C,E=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=D,e[6]=E,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+D*I+L*w),e[14]=n[2]+w-(R*A+E*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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Qi(e,t){t.classHierarchy.push("vtkProp3D"),e.addPosition=n=>{t.position=t.position.map(((e,t)=>e+n[t])),e.modified()},e.getOrientationWXYZ=()=>{const e=Ui();N(e,t.rotation);const n=new Float64Array(3),r=function(e,t){var n=2*Math.acos(t[3]),r=Math.sin(n/2);return r>a?(e[0]=t[0]/r,e[1]=t[1]/r,e[2]=t[2]/r):(e[0]=1,e[1]=0,e[2]=0),n}(n,e);return[Ro(r),n[0],n[1],n[2]]},e.rotateX=n=>{0!==n&&(S(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateY=n=>{0!==n&&(A(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateZ=n=>{0!==n&&(I(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateWXYZ=(n,r,o,a)=>{if(0===n||0===r&&0===o&&0===a)return;const i=Oo(n),s=Ui();zi(s,[r,o,a],i);const l=new 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Es,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&t.getDataType()===Tl.UNSIGNED_CHAR||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Es.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Es.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Es.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function Dl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var El={newInstance:jt.newInstance(Dl,"vtkLookupTable"),extend:Dl};let Vl=1,Ll=0;const Bl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function Nl(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;Ll=e}var _l={getResolveCoincidentTopologyAsString:function(){return Bl[Ll]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Vl},getResolveCoincidentTopology:function(){return Ll},setResolveCoincidentTopology:Nl,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){Vl=e},setResolveCoincidentTopologyToDefault:function(){Nl(0)},setResolveCoincidentTopologyToOff:function(){Nl(0)},setResolveCoincidentTopologyToPolygonOffset:function(){Nl(1)}};function Fl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=(e,r)=>{t[n.key]={factor:e,offset:r}}}))}const kl=["Polygon","Line","Point"],Gl={};Fl(Gl,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},kl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var Ul={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(_l).forEach((t=>{e[t]=_l[t]})),Object.keys(Gl).forEach((t=>{e[t]=Gl[t]})),Fl(e,t.topologyOffset,kl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Gl.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Gl,otherStaticMethods:_l,CATEGORIES:kl};const zl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Wl={PassTypes:zl};const{FieldAssociations:Hl}=ol,{staticOffsetAPI:jl,otherStaticMethods:Kl}=Ul,{ColorMode:$l,ScalarMode:ql,GetArray:Xl}=hl,{VectorMode:Yl}=ml,{VtkDataTypes:Zl}=Es;function Ql(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function Jl(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString($l,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(ql,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===ql.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===ql.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===ql.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===ql.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_FIELD_DATA){const t=e.getFieldData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==a){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(n))e.mapScalarsToTexture(o,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},e.scalarToTextureCoordinate=(e,t,n)=>{let r=.5,o=1;return _a(e)||(o=.49,r=(e-t)*n,r>1e3?r=1e3:r<-1e3&&(r=-1e3)),{texCoordS:r,texCoordT:o}},e.createColorTextureCoordinates=(t,n,r,o,a,i,s,l,c)=>{const u=(i[1]-i[0])/l,d=[];d[0]=i[0]-u,d[1]=i[1]+u;const p=1/(d[1]-d[0]),f=n.getData(),g=t.getData();let m=0,h=0;if(a<0||a>=o)for(let t=0;t<r;++t){let t=0;for(let e=0;e<o;++e)t+=g[m]*g[m],m++;let n=Math.sqrt(t);c&&(n=El.applyLogScale(n,s,i));const r=e.scalarToTextureCoordinate(n,d[0],p);f[h]=r.texCoordS,f[h+1]=r.texCoordT,h+=2}else{m+=a;for(let t=0;t<r;++t){let t=g[m];c&&(t=El.applyLogScale(t,s,i));const n=e.scalarToTextureCoordinate(t,d[0],p);f[h]=n.texCoordS,f[h+1]=n.texCoordT,h+=2,m+=o}}},e.mapScalarsToTexture=(n,r)=>{const o=t.lookupTable.getRange(),a=t.lookupTable.usingLogScale();a&&El.getLogRange(o,o);const i=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==r){t.lookupTable.setAlpha(r),t.colorTextureMap=null,t.lookupTable.build();let e=t.lookupTable.getNumberOfAvailableColors();e>4094&&(e=4094),e<64&&(e=64),e+=2;const n=(o[1]-o[0])/(e-2),s=new Float64Array(2*e);for(let t=0;t<e;++t)s[t]=o[0]+t*n-n/2,a&&(s[t]=10**s[t]);for(let t=0;t<e;++t)s[t+e]=NaN;t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setExtent(0,e-1,0,1,0,0);const l=Es.newInstance({numberOfComponents:1,values:s});t.colorTextureMap.getPointData().setScalars(t.lookupTable.mapScalars(l,t.colorMode,0)),t.lookupTable.setAlpha(i)}if(!t.colorCoordinates||e.getMTime()>t.colorCoordinates.getMTime()||e.getInputData(0).getMTime()>t.colorCoordinates.getMTime()||t.lookupTable.getMTime()>t.colorCoordinates.getMTime()){t.colorCoordinates=null;const r=n.getNumberOfComponents(),i=n.getNumberOfTuples();t.colorCoordinates=Es.newInstance({numberOfComponents:2,values:new Float32Array(2*i)});let s=t.lookupTable.getVectorComponent();t.lookupTable.getVectorMode()===Yl.MAGNITUDE&&n.getNumberOfComponents()>1&&(s=-1),e.createColorTextureCoordinates(n,t.colorCoordinates,i,r,s,o,t.lookupTable.getRange(),t.colorTextureMap.getPointData().getScalars().getNumberOfTuples()/2-2,a)}},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=n=>{if(!t.interpolateScalarsBeforeMapping)return!1;if(t.lookupTable&&t.lookupTable.getIndexedLookup())return!1;const r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName),o=r.scalars;return!(!o||r.cellFlag||t.colorMode===$l.DEFAULT&&o.getDataType()===Zl.UNSIGNED_CHAR||t.colorMode===$l.DIRECT_SCALARS)},e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Ql("AcquireInvertibleLookupTable"),e.valueToColor=Ql("ValueToColor"),e.colorToValue=Ql("ColorToValue"),e.useInvertibleColorFor=Ql("UseInvertibleColorFor"),e.clearInvertibleColor=Ql("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(zl.ID_LOW24),o=e.getRawPixelBuffer(zl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===Hl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===Hl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===zl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(zl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===zl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(zl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const ec={colorMapColors:null,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function tc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ec,n),Fs(e,t,n),jt.get(e,t,["colorCoordinates","colorMapColors","colorTextureMap","selectionWebGLIdsToVTKIds"]),jt.setGet(e,t,["colorByArrayName","arrayAccessMode","colorMode","fieldDataTupleId","interpolateScalarsBeforeMapping","lookupTable","populateSelectionSettings","renderTime","scalarMode","scalarVisibility","static","useLookupTableScalarRange","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),Ul.implementCoincidentTopologyMethods(e,t),Jl(e,t)}var nc={newInstance:jt.newInstance(tc,"vtkMapper"),extend:tc,...jl,...Kl,...hl};function rc(e){let t=0;return e.filter(((e,n)=>n===t&&(t+=e+1,!0)))}function oc(e){let t=0;for(let n=0;n<e.length;)n+=e[n]+1,t++;return t}const ac={extractCellSizes:rc,getNumberOfCells:oc};function ic(e,t){t.classHierarchy.push("vtkCellArray");const n={...e};e.getNumberOfCells=n=>void 0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=oc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=rc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function sc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function lc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Es.extend(e,t,sc(n)),ic(e,t)}var cc={newInstance:jt.newInstance(lc,"vtkCellArray"),extend:lc,...ac};const{vtkErrorMacro:uc}=jt,dc=[1,-1,1,-1,1,-1];function pc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return uc(`getBounds called on an array with components of\n        ${e.getNumberOfComponents()}`),dc;const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);return t.bounds[2]=r[0],t.bounds[3]=r[1],t.bounds[4]=0,t.bounds[5]=0,t.bounds},e.computeBounds=e.getBounds,e.setNumberOfComponents(t.numberOfComponents<2?3:t.numberOfComponents)}const fc={empty:!0,numberOfComponents:3,dataType:ys.FLOAT,bounds:[1,-1,1,-1,1,-1]};function gc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fc,n),Es.extend(e,t,n),pc(e,t)}var mc={newInstance:jt.newInstance(gc,"vtkPoints"),extend:gc};function hc(e,t){t.classHierarchy.push("vtkCell"),e.initialize=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n){t.pointsIds=n;let r=t.points.getData();r.length!==3*t.pointsIds.length&&(r=jt.newTypedArray(e.getDataType(),3*t.pointsIds.length));const o=e.getData();t.pointsIds.forEach(((e,t)=>{let n=3*e,a=3*t;r[a]=o[n],r[++a]=o[++n],r[++a]=o[++n]})),t.points.setData(r)}else{t.points=e,t.pointsIds=new 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let 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Ac=0,Ic=1,wc=2,Pc=3,Oc=4,Rc=5,Mc=6,Dc=7,Ec=9,Vc=21,Lc=41,Bc=42,Nc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],_c={getClassNameFromTypeId:function(e){return e<Nc.length?Nc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Nc.findIndex(e)},isLinear:function(e){return e<Vc||e===Lc||e===Bc},hasSubCells:function(e){return e===Mc||e===Oc||e===wc}};function Fc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Ac},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let 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e=P[2];P[2]=P[D],P[D]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function ou(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(nu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=Yc.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),tu(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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su={newInstance:jt.newInstance(iu,"vtkTriangle"),extend:iu,...ru};const lu=["verts","lines","polys","strips"],{vtkWarningMacro:cu}=jt,uu={[Pc]:Yc,[Oc]:Yc,[Rc]:su};function du(e,t){t.classHierarchy.push("vtkPolyData"),lu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=cc.newInstance()})),e.getNumberOfCells=()=>lu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),lu.forEach((n=>{t[n]=cc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?wc:Ic,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Oc:Pc,1===t&&cu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. 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o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||uu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const pu={};function fu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pu,n),eu.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),du(e,t)}var gu={newInstance:jt.newInstance(fu,"vtkPolyData"),extend:fu};function mu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const hu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function vu(e,t){let n=arguments.length>2&&void 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Tu={newInstance:jt.newInstance(vu,"vtkTexture"),extend:vu,...yu};const bu=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],xu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],Cu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Su=[0,1,0,1,0,1,0,1,2,2,2,2],Au=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Iu=new Float64Array(3),wu=new Float64Array(3),Pu=new Float64Array(3),Ou=new Float64Array(3),Ru=new Float64Array(3),Mu=new Float64Array(3),Du=new Float64Array(16);function Eu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function Vu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],Vn(Pu,Iu,r),Pu[0]+=.1,Vn(wu,Pu,o),Pn(Ru,wu,Iu),Pu[0]-=.1,Pu[1]+=.1,Vn(wu,Pu,o),Pn(Mu,wu,Iu);for(let e=0;e<3;e++)Ru[e]/=.05*u[0],Mu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],a[0]<-.5?On(Pu,Ru,a[0]*i-l.width):a[0]>.5?On(Pu,Ru,a[0]*i):On(Pu,Ru,a[0]*i-l.width/2),wn(Iu,Iu,Pu),On(Pu,Mu,a[1]*i-l.height/2),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Pu,Ru,l.width),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Pu,Mu,l.height),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Pu,Ru,l.width),Pn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Du,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Iu[0]=f[3*u],Iu[1]=f[3*u+1],Iu[2]=f[3*u+2],Vn(Pu,Iu,n),Iu[0]=f[3*u+3],Iu[1]=f[3*u+4],Iu[2]=f[3*u+5],Vn(Ou,Iu,n),Pn(Pu,Pu,Ou);const r=[Pu[0],Pu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const Lu=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Vu(e,t)}),"vtkCubeAxesActorHelper");function Bu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Iu[s]=t.dataBounds[a]-.1*r*bu[a][s],Iu[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Iu[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Pu,Iu,e),Iu[s]=t.dataBounds[a],Vn(Ou,Iu,e),Pn(Pu,Ou,Pu),Mn(Pu,Pu),i=Pu[2]>o,t.camera.getParallelProjection()||(Mn(Ou,Ou),i=Dn(Ou,Pu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Au[t][0]].length+2*r[Au[t][1]].length,a+=r[Au[t][0]].length+r[Au[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let 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e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[xu[e][t]]++;const a=[],i=[];for(let e=0;e<3;e++){const n=go().domain([t.dataBounds[2*e],t.dataBounds[2*e+1]]);a[e]=n.ticks(5);const r=n.tickFormat(5);i[e]=a[e].map(r)}e.updatePolyData(r,n,a),e.updateTextData(r,n,a,i),(o||t.forceUpdate)&&e.updateTextureAtlas(i)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Eu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Eu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Eu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Nu(e){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:12,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},...e}}function _u(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nu(n)),hs.extend(e,t,n),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Tu.newInstance(),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=nc.newInstance(),t.polyData=gu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=gu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Bu(e,t)}var Fu={newInstance:jt.newInstance(_u,"vtkCubeAxesActor"),extend:_u,newCubeAxesActorHelper:Lu};function ku(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Gu={};const Uu=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gu,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Fu.newCubeAxesActorHelper(),ku(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Uu);const zu={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var Wu={ObjectType:zu};const{ObjectType:Hu}=Wu;function ju(e,t){function n(e){switch(e){case Hu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Hu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Hu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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L=a.vertexOffset;f=function(e){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;)C(A[e],A,e+1),e+=A[e]+1,T++;return t.elementCount=w,e.upload(O,zu.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,zu.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):Xu("Wrong type for coordScale, expected vec3 or null"):Xu("Wrong type for coordShift, expected vec3 or null")}}const Zu={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),qu.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ed(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const nd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),td(e,t)}var od={newInstance:jt.newInstance(rd,"vtkShader"),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ad(`Links failed: ${t.error}`),0):(ad(t.error),0):(ad(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.fragmentShader.getError()),0):(ad(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ad(`Error linking 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No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . 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No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ad("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ad("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const sd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=od.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=od.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=od.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShaderProgram"),extend:ld,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function ud(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==zu.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const dd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function pd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),ud(e,t)}var fd={newInstance:jt.newInstance(pd,"vtkOpenGLVertexArrayObject"),extend:pd};const gd={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function md(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===gd.Points?t.context.POINTS:e===rs.WIREFRAME||n===gd.Lines||n===gd.TrisEdges||n===gd.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===gd.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===gd.Points?2:t.primitiveType===gd.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const hd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=cd.newInstance(),t.VAO=fd.newInstance(),t.CABO=Ju.newInstance(),md(e,t)}var yd={newInstance:jt.newInstance(vd),extend:vd,primTypes:gd};const Td={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},bd={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var xd={Wrap:Td,Filter:bd};const Cd=new Float32Array(1),Sd=new Int32Array(Cd.buffer);var Ad={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Cd[0]=e;const t=Sd[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Id,Filter:wd}=xd,{VtkDataTypes:Pd}=Es,{vtkDebugMacro:Od,vtkErrorMacro:Rd,vtkWarningMacro:Md}=Kt,{toHalf:Dd}=Ad;function Ed(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Pd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Pd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Dd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Ad.toHalf(Ad.fromHalf(e[u][l+s+t])*h*(1-v)+Ad.fromHalf(e[u][l+p+t])*h*v+Ad.fromHalf(e[u][c+s+t])*m*(1-v)+Ad.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Pd.UNSIGNED_SHORT&&e!==Pd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent()),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(wd.LINEAR),e.setMagnificationFilter(wd.LINEAR)):(e.setMinificationFilter(wd.NEAREST),e.setMagnificationFilter(wd.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Id.REPEAT),e.setWrapS(Id.REPEAT),e.setWrapT(Id.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Md("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Od(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Md("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Od("Unsupported internal texture type!"),Od(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Pd.SHORT:case t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Pd.FLOAT:case Pd.VOID:default:return t.context.FLOAT}switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Pd.FLOAT:case Pd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case wd.NEAREST:return t.context.NEAREST;case wd.LINEAR:return t.context.LINEAR;case wd.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case wd.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case wd.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case wd.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Id.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Id.REPEAT:return t.context.REPEAT;case Id.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Pd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Rd("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Pd.UNSIGNED_CHAR),e.getInternalFormat(Pd.UNSIGNED_CHAR,4),e.getFormat(Pd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Pd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Pd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Es.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Es.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Pd.FLOAT||t.useHalfFloat&&(u===Pd.SHORT||u===Pd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Pd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Pd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Pd.UNSIGNED_CHAR;if(u===Pd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Pd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Pd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Pd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Pd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Vd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Id.CLAMP_TO_EDGE,wrapT:Id.CLAMP_TO_EDGE,wrapR:Id.CLAMP_TO_EDGE,minificationFilter:wd.NEAREST,magnificationFilter:wd.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function Ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),Ed(e,t)}const Bd=Et(Ld,"vtkOpenGLTexture");var Nd={newInstance:Bd,extend:Ld,...xd};rn("vtkTexture",Bd);var _d="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Fd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",kd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=cd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},Gd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=cd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Ud}=ol,{primTypes:zd}=yd,{Representation:Wd,Shading:Hd}=ds,{ScalarMode:jd}=nc,{Filter:Kd,Wrap:$d}=Nd,{vtkErrorMacro:qd}=Kt,Xd={type:"StartEvent"},Yd={type:"EndEvent"},{CoordinateSystem:Zd}=Zi;function Qd(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=zd.Start;e<zd.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=cd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?t.renderable.getInterpolateScalarsBeforeMapping()&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=cd.substitute(i,"//VTK::Color::Impl",c).result):i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=cd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=cd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=cd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:qd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=cd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=cd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=cd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=cd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=cd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=cd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=cd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()||o.getEdgeVisibility()&&o.getRepresentation()===Wd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===zd.Points||o.getRepresentation()===Wd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===zd.Lines||o.getRepresentation()===Wd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==zd.Tris&&e!==zd.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==zd.TrisEdges&&e!==zd.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=cd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==zl.ID_LOW24&&t.lastSelectionState!==zl.ID_HIGH24||(a=cd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=cd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=cd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=cd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case zl.ID_LOW24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case zl.ID_HIGH24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=cd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Hd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||qd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||qd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||qd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===Zd.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():zl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===Wd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===zl.ID_LOW24||t.lastSelectionState===zl.ID_HIGH24);for(let i=zd.Start;i<zd.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===zd.TrisEdges||i===zd.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(Xd),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(Yd),!t.currentInput)return void qd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==jd.USE_CELL_DATA&&i!==jd.USE_CELL_FIELD_DATA&&i!==jd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===jd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Hd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();if(t.openGLActor.getActiveTextures()||(c=null),t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),t.internalColorTexture||(t.internalColorTexture=Nd.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Kd.NEAREST),e.setMagnificationFilter(Kd.NEAREST),e.setWrapS($d.CLAMP_TO_EDGE),e.setWrapT($d.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const u=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==u){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],d=r.getProperty().getEdgeVisibility()&&l===Wd.SURFACE;for(let e=zd.Start;e<zd.End;e++)e!==zd.TrisEdges&&e!==zd.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):d?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,Wd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Jd={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:zl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ep(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jd,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=zd,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=zd.Start;e<zd.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),Qd(e,t)}const tp=Et(ep,"vtkOpenGLPolyDataMapper");var np={newInstance:tp,extend:ep};rn("vtkMapper",tp);const{ColorMode:rp,ScalarMode:op,GetArray:ap}=hl;function ip(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString(rp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(op,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===op.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===op.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===op.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===op.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_FIELD_DATA){const t=e.getFieldData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const sp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function lp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),ip(e,t)}var cp={newInstance:jt.newInstance(lp,"vtkMapper2D"),extend:lp};const up=1,{primTypes:dp}=yd,{ScalarMode:pp}=cp,{vtkErrorMacro:fp}=Kt,gp={type:"StartEvent"},mp={type:"EndEvent"};function hp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=dp.Start;e<dp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(gp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(mp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==pp.USE_CELL_DATA&&a!==pp.USE_CELL_FIELD_DATA&&a!==pp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===pp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=mc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[dp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[dp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[dp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[dp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=dp.Start;a<dp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=cd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=cd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=cd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=cd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=cd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=cd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=cd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=cd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=cd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||fp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||fp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||fp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===up?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const vp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const yp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vp,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=dp,t.tmpMat4=g(new Float64Array(16));for(let e=dp.Start;e<dp.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),hp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",yp);const{VectorMode:Tp}=Pl;function bp(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function xp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Cp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Sp(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;bp(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,bp(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{bp(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Tp.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Es.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Ap=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=nc.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=gu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Tu.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Sp(e,t)}),"vtkScalarBarActorHelper");function Ip(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(xp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Cp())}}function wp(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Pp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wp(n)),t.autoLayout||(t.autoLayout=xp(0,t)),t.generateTicks||(t.generateTicks=Cp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Ip(e,t)}var Op={newInstance:jt.newInstance(Pp,"vtkScalarBarActor"),extend:Pp,newScalarBarActorHelper:Ap};function Rp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Mp={};const Dp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Mp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Op.newScalarBarActorHelper(),Rp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Dp);const{vtkErrorMacro:Ep}=Kt;function Vp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Es.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ep("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||Ep("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const Lp={context:null};const Bp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lp,n),Zt.extend(e,t,n),t.openGLTexture=Nd.newInstance(),t.tris=yd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Vp(e,t)}));rn("vtkSkybox",Bp);const{FieldAssociations:Np}=ol;function _p(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Fp={fieldAssociation:Np.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function kp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),_p(e,t)}var Gp={newInstance:jt.newInstance(kp,"vtkHardwareSelector"),extend:kp};function Up(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>{const e=[0,0];return null!==t.glFramebuffer&&(e[0]=t.glFramebuffer.width,e[1]=t.glFramebuffer.height),e},e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Nd.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(bd.LINEAR),r.setMagnificationFilter(bd.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const zp={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function Wp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zp,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Up(e,t)}var Hp={newInstance:Et(Wp,"vtkFramebuffer"),extend:Wp};function jp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Kp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function $p(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),jp(e,t)}var qp={newInstance:jt.newInstance($p,"vtkSelectionNode"),extend:$p,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:Xp}=Wl,{SelectionContent:Yp,SelectionField:Zp}=qp,{FieldAssociations:Qp}=ol,{vtkErrorMacro:Jp}=jt;function ef(e){return`${e.propID} ${e.compositeID}`}function tf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function nf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function rf(e,t){let n=t;return n<<=24,n|=e,n}function of(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=nf(n[0],n[1],e.pixBuffer[Xp.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=nf(n[0],n[1],e.pixBuffer[Xp.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[Xp.ID_LOW24]&&0===tf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area))return a;const s=nf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area),l=nf(n[0],n[1],e.pixBuffer[Xp.ID_HIGH24],e.area);return a.attributeID=rf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=of(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=of(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=of(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function af(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=qp.newInstance();switch(l.setContentType(Yp.INDICES),e){case Qp.FIELD_ASSOCIATION_CELLS:l.setFieldType(Zp.CELL);break;case Qp.FIELD_ASSOCIATION_POINTS:l.setFieldType(Zp.POINT);break;default:Jp("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function sf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=of(e,[n,t],0,u);if(r&&r.valid){const t=ef(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return af(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function lf(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e[0]!==n[0]||e[1]!==n[1]?(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer()):t.framebuffer.bind()}else t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return sf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return Jp("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=Xp.MIN_KNOWN_PASS;t.currentPass<=Xp.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===Xp.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===Xp.ID_HIGH24){if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===Xp.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=nf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===Xp.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===Xp.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(Xp,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=nf(e[0],e[1],t.pixBuffer[Xp.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=nf(e[0],e[1],t.pixBuffer[Xp.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[Xp.ID_LOW24]&&0===tf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area))return a;const s=nf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area),l=nf(e[0],e[1],t.pixBuffer[Xp.ID_HIGH24],t.area);return a.attributeID=rf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=ef(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return af(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const cf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cf,n),Gp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),lf(e,t)}var df={newInstance:jt.newInstance(uf,"vtkOpenGLHardwareSelector"),extend:uf,...Wl};const{vtkErrorMacro:pf}=Kt,{Representation:ff}=ds,{ObjectType:gf}=qu,{PassTypes:mf}=df,hf={type:"StartEvent"},vf={type:"EndEvent"};function yf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(hf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(vf),!t.currentInput)return void pf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=cd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=cd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=cd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=cd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=cd.substitute(o,"//VTK::Color::Impl",s).result,o=cd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=cd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=cd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=cd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=cd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===ff.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===mf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||pf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||pf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=qu.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=qu.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=qu.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=qu.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,gf.ARRAY_BUFFER),t.normalBuffer.upload(n,gf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),gf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,gf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Tf={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const bf=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tf,n),np.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),yf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",bf);const{vtkErrorMacro:xf}=jt;class Cf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Sf(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void xf("Invalid or missing input");const r=gu.newInstance();r.shallowCopy(n);const o=new Cf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(cc.newInstance({values:c,name:"faces"})),t[0]=r}}const Af={};function If(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),jt.obj(e,t),jt.algo(e,t,1,1),Sf(e,t)}var wf={newInstance:jt.newInstance(If,"vtkClosedPolyLineToSurfaceFilter"),extend:If};const{vtkErrorMacro:Pf}=Kt;function Of(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Pf("Invalid or missing input");if(!t.cutFunction)return void Pf("Missing cut function");const o=gu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const 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o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Rf={cutFunction:null,cutScalars:null,cutValue:0};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Of(e,t)}var Df={newInstance:Et(Mf,"vtkCutter"),extend:Mf};const Ef=e=>e,Vf=1e-6;class Lf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:Ef}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=Dn(n,r);return a>=1||(En(this.tmp,n,r),Sn(this.tmp)<Vf&&(En(this.tmp,[1,0,0],e),Sn(this.tmp)<Vf&&En(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Bf=function(){return new Lf(!0)},Nf=function(){return new Lf(!1)};const _f=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Ff=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function kf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=gu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Es.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Es.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Bf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Nf().translate(...t.center).apply(o),t.matrix){Nf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Nf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const Gf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=cc.newInstance({values:Uint16Array.from(Ff)}),t._lineCells=cc.newInstance({values:Uint16Array.from(_f)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),kf(e,t)}var zf={newInstance:jt.newInstance(Uf,"vtkCubeSource"),extend:Uf};const{vtkErrorMacro:Wf}=jt;function Hf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void Wf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):Wf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const jf={};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=zf.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkImageDataOutlineFilter"),extend:Kf};let qf;function Xf(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>qf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const Yf={preMultiplyFlag:!1,matrix:[...vo]};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),Xf(e,t)}qf=jt.newInstance(Zf,"vtkTransform");var Qf={newInstance:qf,extend:Zf};const Jf={NEAREST:0,LINEAR:1};var eg={InterpolationType:Jf};const{vtkErrorMacro:tg}=Kt;function ng(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function rg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function og(e,t){t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const n=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):tg("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Jf.NEAREST?(t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.openGLTexture.setMinificationFilter(bd.LINEAR),t.openGLTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime(),e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(!r)return;const o=r.getPointData()?.getScalars();if(!o)return;t._scalars!==o&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=o);const a=o.getNumberOfComponents();let i=`${r.getMTime()}A${o.getMTime()}`;const s=t._openGLRenderWindow.getGraphicsResourceForObject(o);if(s?.vtkObj&&s?.hash===i&&t.openGLTextureString===i)t.openGLTexture=s.vtkObj,t.openGLTextureString=s.hash;else{t.openGLTexture||(t.openGLTexture=Nd.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const e=r.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],o),t.openGLTextureString=i,o&&t._openGLRenderWindow.setGraphicsResourceForObject(o,t.openGLTexture,t.openGLTextureString)}const l=n.getProperty(),c=l.getIndependentComponents(),u=c?a:1,d=c?2*u:1,p=l.getRGBTransferFunction();i=ng(l,p,u);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===i&&t.colorTextureString===i)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(e*d*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),p){const r=new Float32Array(3*e);for(let t=0;t<u;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),c)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=i,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}const g=l.getPiecewiseFunction();i=ng(l,g,u);const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);if(m?.vtkObj&&m?.hash===i&&t.pwfTextureString===i)t.pwfTexture=m.vtkObj,t.pwfTextureString=m.hash;else{const e=1024,n=e*d,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),g){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<u;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=i,g&&t._openGLRenderWindow.setGraphicsResourceForObject(g,t.pwfTexture,t.pwfTextureString)}const h=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==h){const e=Es.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Es.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:tg("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=h,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",rg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",rg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=cd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:tg("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=cd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:tg("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=cd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=cd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=cd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`));const t=ue(n?.getDirection());ge(t,t);const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getDimensions(),l=[0,i[0]-1,0,i[1]-1,0,i[2]-1];let c=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[c+a]=r[a],e[c+o[0]]=l[2*o[0]+n],e[c+o[1]]=l[2*o[1]+t],c+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const u=new Uint16Array(8);u[0]=3,u[1]=0,u[2]=1,u[3]=3,u[4]=3,u[5]=0,u[6]=3,u[7]=2;const d=s.getNormal();za.normalize(d);const p=new Float32Array(12);for(let e=0;e<4;++e)p[3*e]=d[0],p[3*e+1]=d[1],p[3*e+2]=d[2];t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(u,1);const f=Es.newInstance({numberOfComponents:3,values:p,name:"Normals"});t.resliceGeom.getPointData().setNormals(f)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=gu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Es.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else tg("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)}}const ag={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,openGLTextureString:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1,_scalars:null};const ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ag,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=$f.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=gu.newInstance(),t.cutter=Df.newInstance(),t.lineToSurfaceFilter=wf.newInstance(),t.transform=Qf.newInstance(),bt(e,t,["openGLTexture"]),og(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",ig);var sg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:lg}=Kt,{SlicingMode:cg}=sg;function ug(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function dg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function pg(e,t){t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(n.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=cd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=cd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:lg("Unsupported number of independent coordinates.")}}if(i=cd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=cd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=cd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=cd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:lg("Unsupported number of independent coordinates.")}i=cd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=cd.substitute(i,"//VTK::TCoord::Impl",[...dg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); \n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (actualThickness == 0) {\n                    gl_FragData[0] = vec4(0.0, 0.0, 1.0, 1.0);\n                    return;\n                  }\n                  if (opacityToUse > 0.01) {\n                    for (int i = -actualThickness; i <= actualThickness; i++) {\n                      for (int j = -actualThickness; j <= actualThickness; j++) {\n                        if (i == 0 || j == 0) {\n                          continue;\n                        }\n                        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                          gl_FragCoord.y + float(j),\n                          gl_FragCoord.z, gl_FragCoord.w);\n                        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                        float value = texture2D(texture1, neighborPosIS.xy).r;\n                        if (value != centerValue) {\n                          pixelOnBorder = true;\n                          break;\n                        }\n                      }\n                      if (pixelOnBorder == true) {\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      gl_FragData[0] = vec4(tColor, outlineOpacity);\n                    }\n                    else {\n                      gl_FragData[0] = vec4(tColor, opacityToUse);\n                    }\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||lg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||lg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):lg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=ug(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.vtkObj&&m?.hash===g&&t.colorTextureString===g)t.colorTexture=m.vtkObj,t.colorTextureString=m.hash;else{const e=1024,n=new Uint8ClampedArray(e*p*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST)):(t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR)),f){const r=new Float32Array(3*e);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),u)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,p,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=g,f&&t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,t.colorTextureString)}const h=l.getPiecewiseFunction(),v=ug(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.vtkObj&&y?.hash===v&&t.pwfTextureString===v)t.pwfTexture=y.vtkObj,t.pwfTextureString=y.hash;else{const e=1024,n=e*p,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),h){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<d;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),u)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,p,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=v,h&&t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,t.pwfTextureString)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===cg.I&&(S=x-C[0]),T===cg.J&&(S=x-C[2]),T!==cg.K&&T!==cg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Nd.newInstance({resizable:!0}),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===Jf.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.NEAREST)):t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(bd.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(bd.LINEAR)),t.openGLTexture.setWrapS(Td.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Td.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[cg.X,cg.Y,cg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===cg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===cg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===cg.K||T===cg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):lg("Reformat slicing not yet supported.");const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);m?.vtkObj?(t.openGLTexture=m.vtkObj,t.VBOBuildString=m.hash):(t._scalars!==g&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=g),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t._openGLRenderWindow.setGraphicsResourceForObject(g,t.openGLTexture,t.VBOBuildString)),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const h=Es.newInstance({numberOfComponents:3,values:r});h.setName("points");const v=Es.newInstance({numberOfComponents:2,values:l});v.setName("tcoords");const y=new Uint16Array(8);y[0]=3,y[1]=0,y[2]=1,y[3]=3,y[4]=3,y[5]=0,y[6]=3,y[7]=2;const C=Es.newInstance({numberOfComponents:1,values:y});t.tris.getCABO().createVBO(C,"polys",rs.SURFACE,{points:h,tcoords:v,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=e=>{t.labelOutlineThicknessTexture||(t.labelOutlineThicknessTexture=Nd.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]}}const fg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,_scalars:null};const gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fg,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),pg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",gg);const{vtkErrorMacro:mg}=jt;function hg(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function vg(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===Jf.NEAREST?(t.volumeTexture.setMinificationFilter(bd.NEAREST),t.volumeTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.volumeTexture.setMinificationFilter(bd.LINEAR),t.volumeTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=hg(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=hg(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Es.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Es.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Es.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Es.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getCenterlineTangentDirections(),r=new Float32Array(3*i),o=new Float32Array(3*i);for(let t=0,a=0;t<n;++t){const n=3*t;for(let t=0;t<4;++t)r[a+0]=e[n+0],r[a+1]=e[n+1],r[a+2]=e[n+2],o[a+0]=e[n+3],o[a+1]=e[n+4],o[a+2]=e[n+5],a+=3}const a=Es.newInstance({numberOfComponents:3,values:r,name:"centerlineTopDirection"}),s=Es.newInstance({numberOfComponents:3,values:o,name:"centerlineBotDirection"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const s=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],l=t.renderable.getUseUniformOrientation();l?s.push("out vec3 centerlineDirVSOutput;","uniform vec3 centerlineDirection;"):s.push("out vec3 centerlineTopDirVSOutput;","out vec3 centerlineBotDirVSOutput;","out float centerlineAngleVSOutput;","attribute vec3 centerlineTopDirection;","attribute vec3 centerlineBotDirection;"),a=cd.substitute(a,"//VTK::Color::Dec",s).result;const c=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];l?c.push("centerlineDirVSOutput = centerlineDirection;"):c.push("vec3 sumVec = centerlineTopDirection + centerlineBotDirection;","float sumLen2 = dot(sumVec, sumVec);","float diffLen2 = 4.0 - sumLen2;","if (diffLen2 < 0.001) {","  // vectors are too close to each other, use lerp","  centerlineAngleVSOutput = -1.0; // use negative angle as a flag for lerp","  centerlineTopDirVSOutput = centerlineTopDirection;","  centerlineBotDirVSOutput = centerlineBotDirection;","} else if (sumLen2 == 0.0) {","  // vector are opposite to each other, don't make a choice for the user","  // use slerp without direction, it will display the centerline color on each row of pixel","  centerlineAngleVSOutput = 0.0;","  centerlineTopDirVSOutput = vec3(0.0);","  centerlineBotDirVSOutput = vec3(0.0);","} else {","  // use slerp","  centerlineAngleVSOutput = 2.0 * atan(sqrt(diffLen2/sumLen2));","  float sinAngle = sin(centerlineAngleVSOutput);","  centerlineTopDirVSOutput = centerlineTopDirection / sinAngle;","  centerlineBotDirVSOutput = centerlineBotDirection / sinAngle;","}"),a=cd.substitute(a,"//VTK::Color::Impl",c).result;const u=t.volumeTexture.getComponents(),d=o.getProperty().getIndependentComponents();let p=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];l?p.push("in vec3 centerlineDirVSOutput;"):p.push("in vec3 centerlineTopDirVSOutput;","in vec3 centerlineBotDirVSOutput;","in float centerlineAngleVSOutput;");const f=t.renderable.getCenterPoint();if(f&&p.push("uniform vec3 globalCenterPoint;"),d){for(let e=1;e<u;e++)p=p.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(u){case 1:p=p.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:p=p.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:mg("Unsupported number of independent coordinates.")}}i=cd.substitute(i,"//VTK::TCoord::Dec",p).result;let g=[];if(l?g.push("vec3 interpolatedCenterlineDir = centerlineDirVSOutput;"):g.push("vec3 interpolatedCenterlineDir;","if (centerlineAngleVSOutput < 0.0) {","  // Lerp","  interpolatedCenterlineDir = quadOffsetVSOutput.y * centerlineTopDirVSOutput + (1.0 - quadOffsetVSOutput.y) * centerlineBotDirVSOutput;","} else {","  // Slerp","  float topInterpolationAngle = quadOffsetVSOutput.y * centerlineAngleVSOutput;","  float botInterpolationAngle = centerlineAngleVSOutput - topInterpolationAngle;","  interpolatedCenterlineDir = sin(topInterpolationAngle) * centerlineTopDirVSOutput + sin(botInterpolationAngle) * centerlineBotDirVSOutput;","}","// Slerp should give a normalized vector but when sin(angle) is small, rounding error occurs","// Normalize for both lerp and slerp","interpolatedCenterlineDir = normalize(interpolatedCenterlineDir);"),f?g.push("float baseOffset = dot(interpolatedCenterlineDir, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):g.push("float horizontalOffset = quadOffsetVSOutput.x;"),g.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * interpolatedCenterlineDir;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(volumeTexture, volumePosTC);"),d){const e=["r","g","b","a"];for(let t=0;t<u;++t)g=g.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(u){case 1:g=g.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:g=g.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:mg("Unsupported number of independent coordinates.")}}else switch(u){case 1:g=g.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:g=g.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",g).result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||mg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&n.getProgram().isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),e.name,e.offset,n.getCABO().getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&mg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const a=t.volumeTexture.getTextureUnit();if(n.getProgram().setUniformi("volumeTexture",a),n.getProgram().setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4f("backgroundColor",...t.renderable.getBackgroundColor()),n.getProgram().isUniformUsed("centerlineDirection")){const e=t.renderable.getUniformDirection();n.getProgram().setUniform3fArray("centerlineDirection",e)}if(n.getProgram().isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();n.getProgram().setUniform3fArray("globalCenterPoint",e)}const i=t.currentImageDataInput,s=i.getWorldToIndex(),l=P(new Float32Array(16),Rn([],i.getDimensions())),c=oe(l,l,s);if(n.getProgram().setUniformMatrix("MCTCMatrix",c),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const yg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Tg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),kd(e,t,n),jt.algo(e,t,2,0),t.tris=yd.newInstance(),t.volumeTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.pwfTexture=Nd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),vg(e,t)}),"vtkOpenGLImageCPRMapper");function bg(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Tg);const xg={context:null,keyMatrixTime:null,keyMatrices:null};const Cg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),bg(e,t)}),"vtkOpenGLImageSlice");function Sg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Cg);const Ag={};const Ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ag,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Sg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Ig);const wg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Pg={FRACTIONAL:0,PROPORTIONAL:1};var Og={InterpolationType:wg,OpacityMode:Pg};const Rg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5};var Mg={BlendMode:Rg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Dg,vtkErrorMacro:Eg}=Kt;function Vg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function Lg(e,t){t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(n,r)=>{if(n){t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const n=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera()),e.renderPiece(r,n)}},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define vtkIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n#ifdef vtkImageLabelOutlineOn\n\nuniform float outlineOpacity;\nuniform float vpWidth;\nuniform float vpHeight;\nuniform float vpOffsetX;\nuniform float vpOffsetY;\nuniform mat4 PCWCMatrix;\nuniform mat4 vWCtoIDX;\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents >= 3\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#endif\n#if vtkNumComponents >= 4\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef vtkIndependentComponentsOn\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.a = tmp.g;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n#if defined(vtkGradientOpacityOn)\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n#else\n  return 1.0;\n#endif\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n#ifdef vtkClippingPlanesOn\n  void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n  {\n    vec3 g1VC[3];\n    for (int i = 0; i < 3; ++i)\n    {\n      g1VC[i] = IStoVC(texPos[i]);\n    }\n    vec3 posVC = IStoVC(pos);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      for (int j = 0; j < 3; ++j)\n      {\n        if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n        {\n          g1[j] = 0.0;\n        }\n      }\n    }\n  }\n#endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    #ifdef vtkGradientOpacityOn\n      vec4 computeDensityNormal(float gradientMag, vec3 scalarInterp[2])\n      {\n    #else\n      //if gradient opacity not on but using density gradient\n      vec4 computeDensityNormal(vec3 scalarInterp[2])\n      {\n    #endif\n        vec3 opacityG1, opacityG2;\n        opacityG1.x = texture2D(otexture, vec2(scalarInterp[0].x * oscale0 + oshift0, 0.5)).r;\n        opacityG1.y = texture2D(otexture, vec2(scalarInterp[0].y * oscale0 + oshift0, 0.5)).r;\n        opacityG1.z = texture2D(otexture, vec2(scalarInterp[0].z * oscale0 + oshift0, 0.5)).r;\n        opacityG2.x = texture2D(otexture, vec2(scalarInterp[1].x * oscale0 + oshift0, 0.5)).r;\n        opacityG2.y = texture2D(otexture, vec2(scalarInterp[1].y * oscale0 + oshift0, 0.5)).r;\n        opacityG2.z = texture2D(otexture, vec2(scalarInterp[1].z * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n        float gradOpacityFactor = 1.0f;\n        if (gradientMag >= 0.0){\n          gradOpacityFactor = computeGradientOpacityFactor(gradientMag, goscale0, goshift0, gomin0, gomax0);\n        }\n        opacityG1.xyz *= gradOpacityFactor;\n        opacityG2.xyz *= gradOpacityFactor;\n    #endif\n\n        vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n        // divide by spacing\n        opacityG.xyz /= vSpacing;\n        opacityG.w = length(opacityG.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(opacityG.xyz);\n        if (length(opacityG.xyz) > 0.0) {\n          return vec4(normalize(opacityG.xyz),opacityG.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n\n      vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2])\n      {\n        vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n        vec3 yvec = vec3(0.0, tstep.y, 0.0);\n        vec3 zvec = vec3(0.0, 0.0, tstep.z);\n        vec3 texPosPVec[3];\n        texPosPVec[0] = pos + xvec;\n        texPosPVec[1] = pos + yvec;\n        texPosPVec[2] = pos + zvec;\n        vec3 texPosNVec[3];\n        texPosNVec[0] = pos - xvec;\n        texPosNVec[1] = pos - yvec;\n        texPosNVec[2] = pos - zvec;\n        vec3 g1, g2;\n\n        scalarInterp[0].x = getTextureValue(texPosPVec[0]).a;\n        scalarInterp[0].y = getTextureValue(texPosPVec[1]).a;\n        scalarInterp[0].z = getTextureValue(texPosPVec[2]).a;\n        scalarInterp[1].x = getTextureValue(texPosNVec[0]).a;\n        scalarInterp[1].y = getTextureValue(texPosNVec[1]).a;\n        scalarInterp[1].z = getTextureValue(texPosNVec[2]).a;\n\n        #ifdef vtkClippingPlanesOn\n          adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n          adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n        #endif\n        vec4 result;\n        result.x = scalarInterp[0].x - scalarInterp[1].x;\n        result.y = scalarInterp[0].y - scalarInterp[1].y;\n        result.z = scalarInterp[0].z - scalarInterp[1].z;\n        // divide by spacing\n        result.xyz /= vSpacing;\n        result.w = length(result.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(result.xyz);\n        if (length(result.xyz) > 0.0) {\n          return vec4(normalize(result.xyz),result.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n  #endif\n\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n#ifdef vtkImageLabelOutlineOn\nvec3 fragCoordToIndexSpace(vec4 fragCoord) {\n  vec4 pcPos = vec4(\n    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n    (fragCoord.z - 0.5) * 2.0,\n    1.0);\n\n  vec4 worldCoord = PCWCMatrix * pcPos;\n  vec4 vertex = (worldCoord/worldCoord.w);\n\n  vec3 index = (vWCtoIDX * vertex).xyz;\n\n  // half voxel fix for labelmapOutline\n  return (index + vec3(0.5)) / vec3(volumeDimensions);\n}\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  // support gradient opacity\n  #ifdef vtkGradientOpacityOn\n    vec4 normal;\n  #endif\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n      normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n//=======================================================================\n// Given a texture value compute the color and opacity\n//\nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n#ifdef vtkImageLabelOutlineOn\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n  if (actualThickness == 0) {\n    return vec4(0, 0, 1, 1);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  if (float(tColor.a) > 0.01) {\n    for (int i = -actualThickness; i <= actualThickness; i++) {\n      for (int j = -actualThickness; j <= actualThickness; j++) {\n        if (i == 0 || j == 0) {\n          continue;\n        }\n\n        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n          gl_FragCoord.y + float(j),\n          gl_FragCoord.z, gl_FragCoord.w);\n\n        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n        vec4 value = getTextureValue(neighborPosIS);\n\n        // If any of my neighbours are not the same value as I\n        // am, this means I am on the border of the segment.\n        // We can break the loops\n        if (any(notEqual(value, centerValue))) {\n          pixelOnBorder = true;\n          break;\n        }\n      }\n\n      if (pixelOnBorder == true) {\n        break;\n      }\n    }\n\n    // If I am on the border, I am displayed at full opacity\n    if (pixelOnBorder == true) {\n      tColor.a = outlineOpacity;\n    }\n  }\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n  //\n  vec4 goFactor = vec4(1.0,1.0,1.0,1.0);\n\n  // compute the normal vectors as needed\n  #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #if !defined(vtkComponent0Proportional)\n        vec4 normal0 = normalMat[0];\n      #endif\n      #if !defined(vtkComponent1Proportional)\n        vec4 normal1 = normalMat[1];\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          vec4 normal2 = normalMat[2];\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            vec4 normal3 = normalMat[3];\n          #endif\n        #endif\n      #endif\n    #else\n      vec4 normalLight;\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp);\n        if (length(normal0)>0.0){\n          #ifdef vtkGradientOpacityOn\n            normalLight = computeDensityNormal(normal0.w, scalarInterp);\n          #else\n            normalLight = computeDensityNormal(scalarInterp);\n          #endif\n          if (length(normalLight) == 0.0){\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n        normalLight = normal0;\n      #endif\n    #endif\n  #endif\n\n  // compute gradient opacity factors as needed\n  #if defined(vtkGradientOpacityOn)\n    #if !defined(vtkComponent0Proportional)\n      goFactor.x =\n        computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      #if !defined(vtkComponent1Proportional)\n        goFactor.y =\n          computeGradientOpacityFactor(normal1.a, goscale1, goshift1, gomin1, gomax1);\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          goFactor.z =\n            computeGradientOpacityFactor(normal2.a, goscale2, goshift2, gomin2, gomax2);\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            goFactor.w =\n              computeGradientOpacityFactor(normal3.a, goscale3, goshift3, gomin3, gomax3);\n          #endif\n        #endif\n      #endif\n    #endif\n  #endif\n\n  // single component is always independent\n  #if vtkNumComponents == 1\n    vec4 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5));\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n    if (tColor.a < EPSILON){\n      return vec4(0.0);\n    }\n  #endif\n\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n    vec4 tColor = mix0*texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0));\n    #if !defined(vtkComponent0Proportional)\n      tColor.a = goFactor.x*mix0*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n      tColor *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix0));\n    #endif\n\n    vec3 tColor1 = mix1*texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n    #if !defined(vtkComponent1Proportional)\n      tColor.a += goFactor.y*mix1*texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n      tColor1 *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix1));\n    #endif\n\n    #if vtkNumComponents >= 3\n      vec3 tColor2 = mix2*texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n      #if !defined(vtkComponent2Proportional)\n        tColor.a += goFactor.z*mix2*texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n      #else\n        float pwfValue = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n        tColor2 *= pwfValue;\n        tColor.a *= mix(pwfValue, 1.0, (1.0 - mix2));\n      #endif\n\n      #if vtkNumComponents >= 4\n        vec3 tColor3 = mix3*texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n        #if !defined(vtkComponent3Proportional)\n          tColor.a += goFactor.w*mix3*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #else\n          float pwfValue = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n          tColor3 *= pwfValue;\n          tColor.a *= mix(pwfValue, 1.0, (1.0 - mix3));\n        #endif\n      #endif\n    #endif\n  #else // then not independent\n\n  #if vtkNumComponents == 2\n    float lum = tValue.r * cscale0 + cshift0;\n    float alpha = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    vec4 tColor = vec4(lum, lum, lum, alpha);\n  #endif\n  #if vtkNumComponents == 3\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n  #endif\n  #if vtkNumComponents == 4\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n  #endif\n  #endif // dependent\n\n  // apply lighting if requested as appropriate\n  #if vtkLightComplexity > 0\n    #if !defined(vtkComponent0Proportional)\n      #if vtkNumComponents == 1\n        #ifdef SurfaceShadowOn\n            #if vtkLightComplexity < 3\n                vec3 tColorS = applyLightingDirectional(posIS, tColor, normalLight);\n            #else\n                vec3 tColorS = applyLightingPositional(posIS, tColor, normalLight, IStoVC(posIS));\n            #endif\n        #endif\n\n        #ifdef VolumeShadowOn\n          vec3 tColorVS = applyShadowRay(tColor.rgb, posIS, rayDirVC);\n          #ifdef SurfaceShadowOn\n            float vol_coef = volumetricScatteringBlending * (1.0 - tColor.a / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n            tColor.rgb = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n          #else\n            tColor.rgb = tColorVS;\n          #endif\n        #else\n            tColor.rgb = tColorS;\n        #endif\n\n      #else\n        applyLighting(tColor.rgb, normal0);\n      #endif\n    #endif\n\n    #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n      #if !defined(vtkComponent1Proportional)\n        applyLighting(tColor1, normal1);\n      #endif\n    #if vtkNumComponents >= 3\n      #if !defined(vtkComponent2Proportional)\n        applyLighting(tColor2, normal2);\n      #endif\n    #if vtkNumComponents >= 4\n      #if !defined(vtkComponent3Proportional)\n        applyLighting(tColor3, normal3);\n      #endif\n    #endif\n    #endif\n    #endif\n  #endif\n\n// perform final independent blend as needed\n#if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n  tColor.rgb += tColor1;\n#if vtkNumComponents >= 3\n  tColor.rgb += tColor2;\n#if vtkNumComponents >= 4\n  tColor.rgb += tColor3;\n#endif\n#endif\n#endif\n\n#endif\nreturn tColor;\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents == 2\n     if (val.r >= min.r && val.r <= max.r &&\n        val.g >= min.g && val.g <= max.g) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 3\n    if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n        all(lessThanEqual(val, ipScalarRangeMax))) {\n      withinRange = true;\n    }\n  #endif\n  return withinRange;\n}\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Fragment;o.getProperty().getInterpolationType()===wg.LINEAR&&(a=cd.substitute(a,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===o.getProperty().getUseLabelOutline()&&(a=cd.substitute(a,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result);const i=t.scalarTexture.getComponents();a=cd.substitute(a,"//VTK::NumComponents",`#define vtkNumComponents ${i}`).result;const s=o.getProperty().getIndependentComponents();if(s){a=cd.substitute(a,"//VTK::IndependentComponentsOn","#define vtkIndependentComponentsOn").result;const e=[];for(let t=0;t<i;t++)o.getProperty().getOpacityMode(t)===Pg.PROPORTIONAL&&e.push(`#define vtkComponent${t}Proportional`);e.length>0&&(a=cd.substitute(a,"//VTK::vtkProportionalComponents",e.join("\n")).result)}const l=t.currentInput.getSpatialExtent(),c=t.currentInput.getSpacing(),u=new Float64Array(3);In(u,(l[1]-l[0])*c[0],(l[3]-l[2])*c[1],(l[5]-l[4])*c[2]);const d=Sn(u)/e.getCurrentSampleDistance(r);a=cd.substitute(a,"//VTK::MaximumSamplesValue",`${Math.ceil(d)}`).result,a=cd.substitute(a,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lastLightComplexity}`).result,t.lastLightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(a=cd.substitute(a,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(a=cd.substitute(a,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0&&(a=cd.substitute(a,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result)),t.gopacity=o.getProperty().getUseGradientOpacity(0);for(let e=1;s&&!t.gopacity&&e<i;++e)o.getProperty().getUseGradientOpacity(e)&&(t.gopacity=!0);t.gopacity&&(a=cd.substitute(a,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(a=cd.substitute(a,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(a=cd.substitute(a,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,a=cd.substitute(a,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),a=cd.substitute(a,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=a,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lastLightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=cd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lastLightComplexity&&(o=cd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=cd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=cd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=cd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=cd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{let a=0;o.getProperty().getShade()&&t.renderable.getBlendMode()===Rg.COMPOSITE_BLEND&&(a=0,t.numberOfLights=0,r.getLights().forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===a&&(a=1)),1===a&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(a=2),a<3&&e.getPositional()&&(a=3)})));let i=!1;t.lastLightComplexity!==a&&(t.lastLightComplexity=a,i=!0);const s=t.scalarTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=!1;const u=[];if(l){for(let e=0;e<s;e++)u.push(o.getProperty().getOpacityMode(e));u.length>0&&(c=!0)}const d=t.currentInput.getSpatialExtent(),p=t.currentInput.getSpacing(),f=new Float64Array(3);In(f,(d[1]-d[0])*p[0],(d[3]-d[2])*p[1],(d[5]-d[4])*p[2]);const g=Sn(f)/e.getCurrentSampleDistance(r),m={interpolationType:o.getProperty().getInterpolationType(),useLabelOutline:o.getProperty().getUseLabelOutline(),numComp:s,usesProportionalComponents:c,iComps:l,maxSamples:g,useGradientOpacity:o.getProperty().getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),proportionalComponents:u};return t.previousState&&t.previousState.interpolationType===m.interpolationType&&t.previousState.useLabelOutline===m.useLabelOutline&&t.previousState.numComp===m.numComp&&t.previousState.usesProportionalComponents===m.usesProportionalComponents&&t.previousState.iComps===m.iComps&&t.previousState.maxSamples===m.maxSamples&&t.previousState.useGradientOpacity===m.useGradientOpacity&&t.previousState.blendMode===m.blendMode&&function(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;++n)if(e[n]!==t[n])return!1;return!0}(t.previousState.proportionalComponents,m.proportionalComponents)?!!(0===n.getProgram()?.getHandle()||i||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||!!t.lastZBufferTexture!=!!t.zBufferTexture||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime())&&(t.lastZBufferTexture=t.zBufferTexture,!0):(t.previousState={...m},!0)},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Dg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*Dn(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(o.getProperty().getUseLabelOutline()){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lastLightComplexity)return;let O=0;const R=[],M=[],D=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],D[0+3*O]=-.5*w[0],D[1+3*O]=-.5*w[1],D[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",D),3===t.lastLightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();o.setUniformi("ctexture",t.colorTexture.getTextureUnit()),o.setUniformi("otexture",t.opacityTexture.getTextureUnit()),o.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),o.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const a=t.scalarTexture.getVolumeInfo(),i=r.getProperty(),s=t.scalarTexture.getComponents(),l=r.getProperty().getIndependentComponents();if(l&&s>=2)for(let e=0;e<s;e++)o.setUniformf(`mix${e}`,r.getProperty().getComponentWeight(e));for(let e=0;e<s;e++){const t=l?e:0,n=a.scale[e],r=i.getScalarOpacity(t).getRange(),s=n/(r[1]-r[0]),c=(a.offset[e]-r[0])/(r[1]-r[0]);o.setUniformf(`oshift${e}`,c),o.setUniformf(`oscale${e}`,s);const u=i.getRGBTransferFunction(t).getRange(),d=(a.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);o.setUniformf(`cshift${e}`,d),o.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(l)for(let e=0;e<s;++e){const t=a.scale[e];if(i.getUseGradientOpacity(e)){const n=i.getGradientOpacityMinimumOpacity(e),r=i.getGradientOpacityMaximumOpacity(e);o.setUniformf(`gomin${e}`,n),o.setUniformf(`gomax${e}`,r);const a=[i.getGradientOpacityMinimumValue(e),i.getGradientOpacityMaximumValue(e)];o.setUniformf(`goscale${e}`,t*(r-n)/(a[1]-a[0])),o.setUniformf(`goshift${e}`,-a[0]*(r-n)/(a[1]-a[0])+n)}else o.setUniformf(`gomin${e}`,1),o.setUniformf(`gomax${e}`,1),o.setUniformf(`goscale${e}`,0),o.setUniformf(`goshift${e}`,1)}else{const e=a.scale[s-1],t=i.getGradientOpacityMinimumOpacity(0),n=i.getGradientOpacityMaximumOpacity(0);o.setUniformf("gomin0",t),o.setUniformf("gomax0",n);const r=[i.getGradientOpacityMinimumValue(0),i.getGradientOpacityMaximumValue(0)];o.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),o.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===r.getProperty().getUseLabelOutline()){const e=r.getProperty().getLabelOutlineOpacity();o.setUniformf("outlineOpacity",e)}t.lastLightComplexity>0&&(o.setUniformf("vAmbient",i.getAmbient()),o.setUniformf("vDiffuse",i.getDiffuse()),o.setUniformf("vSpecular",i.getSpecular()),o.setUniformf("vSpecularPower",i.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*Dn(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===wg.NEAREST?(t.scalarTexture.setMinificationFilter(bd.NEAREST),t.scalarTexture.setMagnificationFilter(bd.NEAREST)):(t.scalarTexture.setMinificationFilter(bd.LINEAR),t.scalarTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null,null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=fd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Eg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;t._scalars!==a&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=a);const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(bd.LINEAR),t.jitterTexture.setMagnificationFilter(bd.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=i.getIndependentComponents()?s:1,c=i.getScalarOpacity(),u=t._openGLRenderWindow.getGraphicsResourceForObject(c);let d=Vg(i,c,l);if(u.vtkObj&&u.hash===d&&t.opacityTextureString===d)t.opacityTexture=u.vtkObj,t.opacityTextureString=u.hash;else{const r=1024,o=2*r*l,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<l;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.releaseGraphicsResources(t._openGLRenderWindow),t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(bd.LINEAR),t.opacityTexture.setMagnificationFilter(bd.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*l,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*l,1,ys.UNSIGNED_CHAR,e)}t.opacityTextureString=d,c&&t._openGLRenderWindow.setGraphicsResourceForObject(c,t.opacityTexture,t.opacityTextureString)}const p=i.getRGBTransferFunction();d=Vg(i,p,l);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===d&&t.colorTextureString===d)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(2*e*l*3),r=new Float32Array(3*e);for(let t=0;t<l;++t){const o=i.getRGBTransferFunction(t),a=o.getRange();o.getTable(a[0],a[1],e,r,1);for(let o=0;o<3*e;++o)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.create2DFromRaw(e,2*l,3,ys.UNSIGNED_CHAR,n),t.colorTextureString=d,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}e.updateLabelOutlineThicknessTexture(r);const g=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(d=`${o.getMTime()}A${a.getMTime()}`,g?.vtkObj&&g?.hash===d&&t.scalarTextureString===d)t.scalarTexture=g.vtkObj,t.scalarTextureString=g.hash;else{const e=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.releaseGraphicsResources(t._openGLRenderWindow),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],a,t.renderable.getPreferSizeOverAccuracy()),t.scalarTextureString=d,a&&t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,t.scalarTextureString)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Es.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Es.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=e=>{const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}}}const Bg={context:null,VBOBuildTime:null,scalarTexture:null,scalarTextureString:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lastLightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0,_scalars:null};const Ng=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bg,n),Zt.extend(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=yd.newInstance(),t.scalarTexture=Nd.newInstance(),t.opacityTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.jitterTexture=Nd.newInstance(),t.jitterTexture.setWrapS(Td.REPEAT),t.jitterTexture.setWrapT(Td.REPEAT),t.labelOutlineThicknessTexture=Nd.newInstance(),t.framebuffer=Hp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Lg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",Ng);const{vtkDebugMacro:_g}=Kt;function Fg(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent();const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():_g("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const kg={};const Gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kg,n),Zt.extend(e,t,n),Fg(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Gg);var Ug="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:zg}=ds;function Wg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Nd.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Hg(e,t){return Wg(e,t,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE)}function jg(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Hg(t._openGLRenderWindow,t.size),t.seedTexture0=Hg(t._openGLRenderWindow,t.size),t.licTexture1=Hg(t._openGLRenderWindow,t.size),t.seedTexture1=Hg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Wg(t._openGLRenderWindow,t.size,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Wg(e,t,Nd.Filter.LINEAR,Nd.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const Kg={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function $g(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kg,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),jg(e,t)}var qg={newInstance:jt.newInstance($g,"vtkLICPingPongBufferManager"),extend:$g};const Xg=0,Yg=1,Zg=2,Qg=3,Jg=1;function em(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Hp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;f&&o[0]===f.getSize()[0]&&o[1]===f.getSize()[1]||(f=Hp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=qg.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const g=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],m=1/o[0],h=1/o[1],v=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;v.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:y}=t;v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",0),y.setUniformf("uMaskThreshold",t.maskThreshold),y.setUniform2f("uNoiseBoundsPt1",...g),y.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),y.setUniformi("texLIC",t.bufs.getLICTextureUnit()),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,y);const{LICIShaderProgram:T}=t;v.readyShaderProgram(T),T.setUniformi("uPassNo",0),T.setUniformf("uStepSize",-d),T.setUniform2f("uNoiseBoundsPt1",...g),T.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:b}=t;if(v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,b),t.enhancedLIC){t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",m),a.setUniformf("uDy",h),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",1),n(y,t.bufs),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformi("uPassNo",1),T.setUniformf("uStepSize",-d),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),b.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,b)}if(t.antiAlias){const e=t.AAHProgram;v.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",m);const a=t.AAVProgram;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",h);for(let i=0;i<t.antiAlias;++i)v.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),v.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const tm={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLineIntegralConvolution2D"),extend:nm};function om(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const am={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:Xg,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:Jg,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function im(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,am,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),om(0,t)}var sm={newInstance:jt.newInstance(im,"vtkSurfaceLICInterface"),extend:im};const{Representation:lm}=ds;function cm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===Jg?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Nd.newInstance({wrapS:Nd.Wrap.REPEAT,wrapT:Nd.Wrap.REPEAT,minificationFilter:Nd.Filter.NEAREST,magnificationFilter:Nd.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.allocateTextures=()=>{const n=Nd.Filter.NEAREST,r=Nd.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Nd.newInstance({wrapS:Nd.Wrap.CLAMP_TO_EDGE,wrapT:Nd.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Nd.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Hp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",lm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=rm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===Zg||u===Qg){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const um={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),sm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkSurfaceLICInterface"),extend:dm};const{vtkErrorMacro:fm}=Kt;function gm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=cd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=cd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=cd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=cd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=cd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(fm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(fm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(fm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=sm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=pm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(fm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const mm={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const hm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mm,n),np.extend(e,t,n),gm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",hm);const{vtkErrorMacro:vm}=Kt;function ym(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||vm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,zu.ARRAY_BUFFER),o&&a.getColorBO().upload(f,zu.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Tm={};const bm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),np.extend(e,t,n),ym(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",bm);const{vtkErrorMacro:xm}=Kt;function Cm(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=cd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||xm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||xm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||xm("Error setting 'radiusMC' in shader 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o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():xm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let 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e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const Vh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Lh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Eh(e,t)}var Bh={newInstance:jt.newInstance(Lh,"vtkCamera"),extend:Lh};function Nh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const _h={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function Fh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_h,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),Nh(e,t)}var kh={newInstance:jt.newInstance(Fh,"vtkLight"),extend:Fh,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Gh}=jt;function Uh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Gh("call displayToView on your view instead"),e.viewToDisplay=()=>Gh("callviewtodisplay on your view instead"),e.getSize=()=>Gh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Gh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkViewport"),extend:Wh};const{vtkDebugMacro:jh,vtkErrorMacro:Kh,vtkWarningMacro:$h}=Kt;function qh(e){return()=>Kh(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function Xh(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(jh("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):Kh("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=qh("allocateTime"),e.updateGeometry=qh("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{jh(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Bh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=kh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return Kh("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return Kh("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),jh("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return jh("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return Kh("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&($h("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return jh("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return Kh("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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Yh={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function Zh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,Yh,n),Hh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),Xh(e,t)}var Qh={newInstance:Et(Zh,"vtkRenderer"),extend:Zh};const Jh=Object.create(null);function ev(e,t){Jh[e]=t}function tv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Jh[e]&&Jh[e](t)}function nv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return tv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))}}const rv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1};function ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),nv(e,t)}var av={newInstance:jt.newInstance(ov,"vtkRenderWindow"),extend:ov,registerViewConstructor:ev,listViewAPIs:function(){return Object.keys(Jh)},newAPISpecificView:tv};const iv={Unknown:0,LeftController:1,RightController:2},sv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var lv={Device:iv,Input:sv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:cv,Input:uv}=lv,{vtkWarningMacro:dv,vtkErrorMacro:pv,normalizeWheel:fv,vtkOnceErrorMacro:gv}=jt,mv={ctrlKey:!1,altKey:!1,shiftKey:!1},hv={"xr-standard":[uv.Trigger,uv.Grip,uv.TrackPad,uv.Thumbstick,uv.A,uv.B]},vv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function yv(e){e.cancelable&&e.preventDefault()}function Tv(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function bv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{pv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>dv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",yv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",yv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&yv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&dv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&yv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:pv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?dv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):pv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;dv(`${e} did not request an 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t.lastGamepadValues[i.index][s].buttons||(t.lastGamepadValues[i.index][s].buttons[r]=!1),t.lastGamepadValues[i.index][s].buttons[r]!==i.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,pressed:i.buttons[r].pressed,device:"left"===n.handedness?cv.LeftController:cv.RightController,input:hv[i.mapping]&&hv[i.mapping][r]?hv[i.mapping][r]:uv.Trigger}),t.lastGamepadValues[i.index][s].buttons[r]=i.buttons[r].pressed),t.lastGamepadValues[i.index][s].buttons[r]&&null!=a&&e.move3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,device:"left"===n.handedness?cv.LeftController:cv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{yv(n);const r={...fv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:pv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);if(2===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Tv(o);e.recognizeGesture("TouchEnd",t);const a={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},vv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void gv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const xv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Cv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xv,n),jt.obj(e,t),t._animationExtendedEnd=0,jt.event(e,t,"RenderEvent"),vv.forEach((n=>jt.event(e,t,n))),jt.get(e,t,["initialized","interactorStyle","lastFrameTime","recentAnimationFrameRate","_view"]),jt.setGet(e,t,["container","lightFollowCamera","enabled","enableRender","recognizeGestures","desiredUpdateRate","stillUpdateRate","picker","preventDefaultOnPointerDown","preventDefaultOnPointerUp","mouseScrollDebounceByPass"]),jt.moveToProtected(e,t,["view"]),bv(e,t)}var Sv={newInstance:jt.newInstance(Cv,"vtkRenderWindowInteractor"),extend:Cv,handledEvents:vv,...lv};const{vtkErrorMacro:Av,VOID:Iv}=jt;function wv(e,t){t.classHierarchy.push("vtkInteractorObserver");const n={...e};function r(){for(;t.subscribedEvents.length;)t.subscribedEvents.pop().unsubscribe()}function o(){Sv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Iv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Av("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Pv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function Ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),wv(e,t)}var Rv={newInstance:jt.newInstance(Ov,"vtkInteractorObserver"),extend:Ov,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},Mv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Dv}=Mv,Ev={Rotate:Dv.IS_ROTATE,Pan:Dv.IS_PAN,Spin:Dv.IS_SPIN,Dolly:Dv.IS_DOLLY,CameraPose:Dv.IS_CAMERA_POSE,WindowLevel:Dv.IS_WINDOW_LEVEL,Slice:Dv.IS_SLICE};function Vv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Ev).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Dv.IS_NONE&&(t.state=Ev[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Ev[n]&&(t.state=Dv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":e.pokedRenderer.resetCamera(),n.render();break;case"w":case"W":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Lv={state:Dv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Bv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lv,n),Rv.extend(e,t,n),Vv(e,t)}var Nv={newInstance:jt.newInstance(Bv,"vtkInteractorStyle"),extend:Bv,...Mv};const{States:_v}=Mv;function Fv(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=n.pokedRenderer;switch(t.state){case _v.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad?!n||n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad||t.state!==_v.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===_v.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const t=e.pokedRenderer.getActiveCamera(),n=t.getPhysicalTranslation(),r=.025*t.getPhysicalScale(),o=t.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);t.setPhysicalTranslation(n[0]+o[0]*r,n[1]+o[1]*r,n[2]+o[2]*r)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case _v.IS_DOLLY:e.endDolly();break;case _v.IS_PAN:e.endPan();break;case _v.IS_SPIN:e.endSpin();break;case _v.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(n.pokedRenderer,n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=n.pokedRenderer.getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(n.pokedRenderer,o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(n.pokedRenderer,o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(n.pokedRenderer,o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&n.pokedRenderer.updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=e.pokedRenderer.getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(n.pokedRenderer,r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const kv={motionFactor:10,zoomFactor:10};function Gv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kv,n),Nv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),Fv(e,t)}var Uv={newInstance:jt.newInstance(Gv,"vtkInteractorStyleTrackballCamera"),extend:Gv};function zv(e){return e}function Wv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Wv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Hv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Wv:zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function jv(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const Kv={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function $v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kv,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),jv(e,t)}var qv={newInstance:jt.newInstance($v,"vtkRenderPass"),extend:$v};const{Representation:Xv}=ds,{vtkErrorMacro:Yv}=jt;function Zv(e){const t=cd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function Qv(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Es.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Es.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",Xv.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Nd.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Nd.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Nd.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||Yv("Error setting vertexDC in copy shader 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CT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ST(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group 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0!==arguments[2]?arguments[2]:{};Object.assign(t,AT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ST(e,t)}var wT={newInstance:Et(IT,"vtkWebGPURenderEncoder"),extend:IT},PT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const OT=["getMappedRange","mapAsync","unmap"];function RT(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<OT.length;n++)e[OT[n]]=function(){return t.handle[OT[n]](...arguments)}}const MT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function DT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,MT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),RT(e,t)}var ET={newInstance:jt.newInstance(DT),extend:DT,...PT};const{Representation:VT}=ds,{PrimitiveTypes:LT}=PT;class BT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function NT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function _T(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=NT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=NT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=NT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=NT(n,o,t),n.ibo[n.iboId++]=a}function FT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let kT;const GT=new Uint32Array(1),UT=new Uint32Array(2),zT=new Uint32Array(3),WT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)GT[0]=t[n+a],kT(GT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)UT[0]=t[n+a],UT[1]=t[n+(a+1)%e],kT(UT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o);for(let a=0;a<e-2;a++)UT[0]=t[n+a],UT[1]=t[n+a+2],kT(UT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n],zT[1]=t[n+a+1],zT[2]=t[n+a+2],kT(zT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n+a],zT[1]=t[n+a+1+a%2],zT[2]=t[n+a+1+(a+1)%2],kT(zT,r,o)}};function HT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case LT.Points:return"points";case LT.Lines:return"lines";case LT.Triangles:case LT.TriangleEdges:return"polys";case LT.TriangleStripEdges:case LT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new BT};let d=null;d=o===VT.POINTS||r===LT.Points?WT.anythingToPoints:o===VT.WIREFRAME||r===LT.Lines?WT[`${l}ToWireframe`]:WT[`${l}ToSurface`],kT=FT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),kT=_T,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const jT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function KT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jT,n),ET.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),HT(e,t)}var $T={newInstance:jt.newInstance(KT),extend:KT,...PT};const{BufferUsage:qT}=PT,{vtkErrorMacro:XT}=Kt,{VtkDataTypes:YT}=Es;function ZT(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function QT(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function JT(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===qT.Index&&(n=$T.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=ET.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===qT.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===qT.PointArray){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=ZT(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===qT.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=QT(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ZT(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===qT.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case YT.UNSIGNED_CHAR:t="uint8";break;case YT.FLOAT:t="float32";break;case YT.UNSIGNED_INT:t="uint32";break;case YT.INT:t="sint32";break;case YT.DOUBLE:t="float32";break;case YT.UNSIGNED_SHORT:t="uint16";break;case YT.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||XT(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:qT.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=ET.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const eb={device:null,fullScreenQuadBuffer:null};function tb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eb,n),yt(e,t),At(e,t,["device"]),JT(e,t)}var nb={newInstance:Et(tb),extend:tb,...PT};function rb(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const ob={device:null,handle:null,label:null,options:null};function ab(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ob,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),rb(e,t)}var ib={newInstance:jt.newInstance(ab),extend:ab};function sb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(t.texture.getFormat());t.bindGroupLayoutEntry.texture.sampleType=r.sampleType},e.createFromTextureHandle=(e,n)=>{t.texture=null,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e,t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(n.format);t.bindGroupLayoutEntry.texture.sampleType=r.sampleType,t.bindGroupTime.modified()},e.getBindGroupEntry=()=>({resource:e.getHandle()}),e.getShaderCode=(e,n)=>{let r="f32";"sint"===t.bindGroupLayoutEntry.texture.sampleType?r="i32":"uint"===t.bindGroupLayoutEntry.texture.sampleType&&(r="u32");let o=`@binding(${e}) @group(${n}) var ${t.label}: texture_${t.options.dimension}<${r}>;`;return"depth"===t.bindGroupLayoutEntry.texture.sampleType&&(o=`@binding(${e}) @group(${n}) var ${t.label}: texture_depth_${t.options.dimension};`),o},e.addSampler=(n,r)=>{const o=ib.newInstance({label:`${t.label}Sampler`});o.create(n,r),e.setSampler(o)},e.getBindGroupTime=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.bindGroupTime),e.getHandle=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.handle)}const lb={texture:null,handle:null,sampler:null,label:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),jt.obj(e,t),t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,texture:{sampleType:"float",viewDimension:"2d"}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","texture"]),jt.setGet(e,t,["bindGroupLayoutEntry","label","sampler"]),sb(e,t)}var ub={newInstance:jt.newInstance(cb),extend:cb};const{BufferUsage:db}=nb;function pb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return t.device.getHandle().queue.copyExternalImageToTexture({source:n.canvas,flipY:n.flip},{texture:t.handle,premultipliedAlpha:!0},[t.width,t.height,t.depth]),void(t.ready=!0);n.jsImageData&&!n.nativeArray&&(n.width=n.jsImageData.width,n.height=n.jsImageData.height,n.depth=1,n.format="rgba8unorm",n.flip=!0,n.nativeArray=n.jsImageData.data);const o=tT(t.format);let a=t.width*o.stride;const i=(e,t,n)=>{const r=e.length/(t*n)*e.BYTES_PER_ELEMENT,a=2===o.elementSize&&"float"===o.sampleType;if(a||r%256){const i=e,s=r/i.BYTES_PER_ELEMENT,l=o.elementSize,c=256*Math.floor((s*l+255)/256),u=c/l,d=jt.newTypedArray(a?"Uint16Array":i.constructor.name,u*t*n);for(let e=0;e<t*n;e++)if(a)for(let t=0;t<s;t++)d[e*u+t]=Ad.toHalf(i[e*s+t]);else d.set(i.subarray(e*s,(e+1)*s),e*u);return[d,c]}return[e,r]};if(n.nativeArray&&(r=n.nativeArray),n.image){const e=document.createElement("canvas");e.width=n.image.width,e.height=n.image.height;const t=e.getContext("2d");t.translate(0,e.height),t.scale(1,-1),t.drawImage(n.image,0,0,n.image.width,n.image.height,0,0,e.width,e.height),r=t.getImageData(0,0,n.image.width,n.image.height).data}const s=t.device.createCommandEncoder();if(3!==e.getDimensionality()){const e=Tu.generateMipmaps(r,t.width,t.height,t.mipLevel);let o=t.width,l=t.height;for(let r=0;r<=t.mipLevel;r++){const c=i(e[r],l,1);a=c[1];const u={dataArray:n.dataArray?n.dataArray:null,nativeArray:c[0],usage:db.Texture},d=t.device.getBufferManager().getBuffer(u);s.copyBufferToTexture({buffer:d.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:l},{texture:t.handle,mipLevel:r},[o,l,1]),o/=2,l/=2}t.device.submitCommandEncoder(s),t.ready=!0}else{const e=i(r,t.height,t.depth);a=e[1];const o={dataArray:n.dataArray?n.dataArray:null,usage:db.Texture};o.nativeArray=e[0];const l=t.device.getBufferManager().getBuffer(o);s.copyBufferToTexture({buffer:l.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:t.height},{texture:t.handle},[t.width,t.height,t.depth]),t.device.submitCommandEncoder(s),t.ready=!0}},e.getScale=()=>{const e=tT(t.format);return 2===e.elementSize&&"float"===e.sampleType?1:255},e.getNumberOfComponents=()=>tT(t.format).numComponents,e.getDimensionality=()=>{let e=0;return t.width>1&&e++,t.height>1&&e++,t.depth>1&&e++,e},e.resizeToMatch=e=>{e.getWidth()===t.width&&e.getHeight()===t.height&&e.getDepth()===t.depth||(t.width=e.getWidth(),t.height=e.getHeight(),t.depth=e.getDepth(),t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.resize=function(e,n){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;e===t.width&&n===t.height&&r===t.depth||(t.width=e,t.height=n,t.depth=r,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.createView=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};r.dimension||(r.dimension=1===t.depth?"2d":"3d");const o=ub.newInstance({label:n});return o.create(e,r),o}}const fb={device:null,handle:null,buffer:null,ready:!1,label:null};function gb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fb,n),jt.obj(e,t),jt.get(e,t,["handle","ready","width","height","depth","format","usage"]),jt.setGet(e,t,["device","label"]),pb(e,t)}var mb={newInstance:jt.newInstance(gb),extend:gb};function hb(e,t){t.classHierarchy.push("vtkWebGPUOpaquePass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.renderEncoder)t.colorTexture.resize(r.getCanvas().width,r.getCanvas().height),t.depthTexture.resize(r.getCanvas().width,r.getCanvas().height);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"opaquePassColor"}),t.colorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const n=t.colorTexture.createView("opaquePassColorTexture");t.renderEncoder.setColorTextureView(0,n),t.depthFormat="depth32float",t.depthTexture=mb.newInstance({label:"opaquePassDepth"}),t.depthTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:t.depthFormat,usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("opaquePassDepthTexture");t.renderEncoder.setDepthTextureView(a)}t.renderEncoder.attachTextureViews(),e.setCurrentOperation("opaquePass"),n.setRenderEncoder(t.renderEncoder),n.traverse(e)},e.getColorTextureView=()=>t.renderEncoder.getColorTextureViews()[0],e.getDepthTextureView=()=>t.renderEncoder.getDepthTextureView(),e.createRenderEncoder=()=>{t.renderEncoder=wT.newInstance({label:"OpaquePass"}),t.renderEncoder.setPipelineHash("op")}}const vb={renderEncoder:null,colorTexture:null,depthTexture:null};function yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vb,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),hb(e,t)}var Tb={newInstance:jt.newInstance(yb,"vtkWebGPUOpaquePass"),extend:yb};function bb(e,t){t.classHierarchy.push("vtkWebGPUOrderIndependentTranslucentPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.translucentRenderEncoder)t.translucentColorTexture.resizeToMatch(t.colorTextureView.getTexture()),t.translucentAccumulateTexture.resizeToMatch(t.colorTextureView.getTexture());else{e.createRenderEncoder(),e.createFinalEncoder(),t.translucentColorTexture=mb.newInstance({label:"translucentPassColor"}),t.translucentColorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const n=t.translucentColorTexture.createView("oitpColorTexture");t.translucentRenderEncoder.setColorTextureView(0,n),t.translucentAccumulateTexture=mb.newInstance({label:"translucentPassAccumulate"}),t.translucentAccumulateTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=xT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n")}t.translucentRenderEncoder.setDepthTextureView(t.depthTextureView),t.translucentRenderEncoder.attachTextureViews(),e.setCurrentOperation("translucentPass"),n.setRenderEncoder(t.translucentRenderEncoder),n.traverse(e),e.finalPass(r,n)},e.finalPass=(e,n)=>{t.translucentFinalEncoder.setColorTextureView(0,t.colorTextureView),t.translucentFinalEncoder.attachTextureViews(),t.translucentFinalEncoder.begin(e.getCommandEncoder()),n.scissorAndViewport(t.translucentFinalEncoder),t.fullScreenQuad.prepareAndDraw(t.translucentFinalEncoder),t.translucentFinalEncoder.end()},e.getTextures=()=>[t.translucentColorTexture,t.translucentAccumulateTexture],e.createRenderEncoder=()=>{t.translucentRenderEncoder=wT.newInstance({label:"translucentRender"});const e=t.translucentRenderEncoder.getDescription();e.colorAttachments=[{view:void 0,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:void 0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = computedColor.a;"]).result,t.setCode(n)})),t.translucentRenderEncoder.setPipelineHash("oitpr"),t.translucentRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!1,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one"},alpha:{srcfactor:"one",dstFactor:"one"}}},{format:"r16float",blend:{color:{srcFactor:"zero",dstFactor:"one-minus-src"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createFinalEncoder=()=>{t.translucentFinalEncoder=wT.newInstance({label:"translucentFinal"}),t.translucentFinalEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t.translucentFinalEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t.translucentFinalEncoder.setPipelineHash("oitpf"),t.translucentFinalEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})}}const xb={colorTextureView:null,depthTextureView:null};function Cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xb,n),qv.extend(e,t,n),jt.setGet(e,t,["colorTextureView","depthTextureView"]),bb(e,t)}var Sb={newInstance:jt.newInstance(Cb,"vtkWebGPUOrderIndependentTranslucentPass"),extend:Cb};const{BufferUsage:Ab}=nb,{vtkErrorMacro:Ib}=jt;function wb(e,t){t.classHierarchy.push("vtkWebGPUUniformBuffer"),e.addEntry=(e,n)=>{t._bufferEntryNames.has(e)?Ib(`entry named ${e} already 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o=t.bufferEntries[r];o.packed||(o.packed=!0,o.offset=e,n.push(o),e+=o.sizeInBytes)}t.bufferEntries=n,t._bufferEntryNames.clear();for(let e=0;e<t.bufferEntries.length;e++)t._bufferEntryNames.set(t.bufferEntries[e].name,e);t.sizeInBytes=e,t.sizeInBytes=r*Math.ceil(t.sizeInBytes/r),t.sortDirty=!1},e.sendIfNeeded=e=>{if(!t.UBO){const n={nativeArray:t.Float32Array,usage:Ab.UniformArray,label:t.label};t.UBO=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),t.sendDirty=!1}t.sendDirty&&(e.getHandle().queue.writeBuffer(t.UBO.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes),t.sendDirty=!1),t.sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];a.lastValue!==r&&(i[a.offset/i.BYTES_PER_ELEMENT]=r,t.sendDirty=!0),a.lastValue=r},e.setArray=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];let s=!1;for(let e=0;e<r.length;e++)a.lastValue&&a.lastValue[e]===r[e]||(i[a.offset/i.BYTES_PER_ELEMENT+e]=r[e],s=!0);s&&(t.sendDirty=!0,a.lastValue=[...r])},e.getBindGroupEntry=()=>({resource:{buffer:t.UBO.getHandle()}}),e.getSendTime=()=>t.sendTime.getMTime(),e.getShaderCode=(n,r)=>{e.sortBufferEntries();const o=[`struct ${t.label}Struct\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];o.push(`  ${n.name}: ${n.type},`)}return o.push(`};\n@binding(${n}) @group(${r}) var<uniform> ${t.label}: ${t.label}Struct;`),o.join("\n")}}const Pb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Ob(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),wb(e,t)}var Rb={newInstance:jt.newInstance(Ob,"vtkWebGPUUniformBuffer"),extend:Ob};const{BufferUsage:Mb}=nb,{vtkErrorMacro:Db}=jt;function Eb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void Db(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void Db(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Vb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function Lb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Eb(e,t)}var Bb={newInstance:jt.newInstance(Lb,"vtkWebGPUStorageBuffer"),extend:Lb};const Nb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",_b=new Float64Array(16),Fb=new Float64Array(16);function kb(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Uy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Rg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Uy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Uy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Uy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Rg.COMPOSITE_BLEND?l=!0:n===Rg.MAXIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.MINIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.AVERAGE_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.ADDITIVE_INTENSITY_BLEND&&(a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Uy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(_b),b(_b,_b,o);const p=n.getMatrix();m(Fb,p),h(Fb,Fb),T(_b,Fb,_b);const f=d.getWorldToIndex();T(_b,f,_b);const v=d.getDimensions();g(Fb),x(Fb,Fb,[1/v[0],1/v[1],1/v[2]]),T(_b,Fb,_b);for(let t=0;t<16;t++)a[16*e+t]=_b[t];h(_b,_b);for(let t=0;t<4;t++)i[16*e+4*t]=_b[4*t],i[16*e+4*t+1]=_b[4*t+1],i[16*e+4*t+2]=_b[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=tT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=ib.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const Gb={volumes:null,rowLength:1024,lastVolumeLength:0};function Ub(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gb,n),xT.extend(e,t,n),t.fragmentShaderTemplate=Nb,t.UBO=Rb.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Bb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Bb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),kb(e,t)}var zb={newInstance:jt.newInstance(Ub,"vtkWebGPUVolumePassFSQ"),extend:Ub};const{Representation:Wb}=ds,{BufferUsage:Hb,PrimitiveTypes:jb}=nb,Kb=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],$b="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function qb(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=zb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=xT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=Rb.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Hb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:jb.Triangles,representation:Wb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Hb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+Kb[e][0],l[n++]=r+Kb[e][1],l[n++]=r+Kb[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=wT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=mb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=mb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=mb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=wT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=wT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=wT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const Xb={colorTextureView:null,depthTextureView:null,volumes:null};function Yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Xb,n),qv.extend(e,t,n),t._mapper=vT.newInstance(),t._mapper.setFragmentShaderTemplate($b),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=gu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),qb(e,t)}var Zb={newInstance:jt.newInstance(Yb,"vtkWebGPUVolumePass"),extend:Yb};function Qb(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Tb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Sb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=Zb.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=wT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=ib.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=xT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=ub.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const Jb={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function ex(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jb,n),qv.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),Qb(e,t)}var tx={newInstance:jt.newInstance(ex,"vtkForwardPass"),extend:ex};const{VtkDataTypes:nx}=Es;function rx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case nx.UNSIGNED_CHAR:e.format+="8unorm";break;case nx.FLOAT:case nx.UNSIGNED_INT:case nx.INT:case nx.DOUBLE:case nx.UNSIGNED_SHORT:case nx.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=mb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const ox={handle:null,device:null};function ax(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ox,n),jt.obj(e,t),jt.setGet(e,t,["device"]),rx(e,t)}var ix={newInstance:jt.newInstance(ax),extend:ax};class sx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function lx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const cx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function ux(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new sx,t.shaderCache=Uy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=nb.newInstance(),t.bufferManager.setDevice(e),t.textureManager=ix.newInstance(),t.textureManager.setDevice(e),t.pipelines={},lx(e,t)}var dx={newInstance:Et(ux,"vtkWebGPUDevice"),extend:ux};function px(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=mb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=wT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const fx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function gx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fx,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),px(e,t)}var mx={newInstance:jt.newInstance(gx,"vtkWebGPUHardwareSelectionPass"),extend:gx};const{SelectionContent:hx,SelectionField:vx}=qp,{FieldAssociations:yx}=ol,{vtkErrorMacro:Tx}=jt;function bx(e){return`${e.propID} ${e.compositeID}`}function xx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Cx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=xx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=xx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Cx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Cx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Cx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Cx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Cx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Sx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Tx("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=ET.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=ET.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Cx(e,[n,t],0,u);if(r){const t=bx(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=qp.newInstance();switch(i.setContentType(hx.INDICES),e){case yx.FIELD_ASSOCIATION_CELLS:i.setFieldType(vx.CELL);break;case yx.FIELD_ASSOCIATION_POINTS:i.setFieldType(vx.POINT);break;default:Tx("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Ax={};function Ix(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ax,n),Gp.extend(e,t,n),t._selectionPass=mx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Sx(e,t)}var wx={newInstance:jt.newInstance(Ix,"vtkWebGPUHardwareSelector"),extend:Ix};const Px=Object.create(null);function Ox(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const Rx={};function Mx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rx,n),t.overrides=Px,tn.extend(e,t,n),Ox(0,t)}var Dx={newInstance:jt.newInstance(Mx,"vtkWebGPUViewNodeFactory"),extend:Mx};const{vtkErrorMacro:Ex}=jt,Vx={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function Lx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Ex("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Ex("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=dx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=qv.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Vx,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=ET.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Ad.fromHalf(r.colorValues[o]),s[n+1]=255*Ad.fromHalf(r.colorValues[o+1]),s[n+2]=255*Ad.fromHalf(r.colorValues[o+2]),s[n+3]=255*Ad.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=wx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Bx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Nx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",xy.extend(e,t,n),t.myFactory=Dx.newInstance(),t.myFactory.registerOverride("vtkRenderWindow",Nx),t.renderPasses[0]=tx.newInstance(),t.selector||(t.selector=wx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),Lx(e,t)}),"vtkWebGPURenderWindow");ev("WebGPU",Nx);const _x=Hv(),Fx={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},kx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Gx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Ux(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Gx(t.container,t.containerStyle||Fx),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=av.newInstance(),t.renderer=Qh.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(_x.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Sv.newInstance(),t.interactor.setInteractorStyle(Uv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Gx(t.controlContainer,t.controlPanelStyle||kx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",(t=>{"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}))},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Wx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Ux(e,t)}var Hx={newInstance:jt.newInstance(Wx),extend:Wx},jx={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:Kx,Scale:$x}=jx,{ScalarMappingTarget:qx}=Pl,{vtkDebugMacro:Xx,vtkErrorMacro:Yx,vtkWarningMacro:Zx}=jt;function Qx(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function Jx(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function eC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];Qx(o,i),Qx(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=Jx(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=Jx(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function tC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return Yx("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return Yx("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===$x.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Do(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===Kx.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===Kx.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===Kx.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===Kx.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return Yx("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(Yx("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return Yx("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>t.indexedLookup&&e.getSize()?e.getSize():t.tableSize?t.tableSize:16777216,e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void Yx("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):Xx("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void Zx("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===qx.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===qx.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===qx.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===qx.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=Kx[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(Yx(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=Kx.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const nC={clamping:!0,colorSpace:Kx.RGB,hSVWrap:!0,scale:$x.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function rC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","colorSpace","discretize","numberOfValues"]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),tC(e,t)}var oC={newInstance:jt.newInstance(rC,"vtkColorTransferFunction"),extend:rC,...jx},aC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:iC,ScaleModes:sC}=aC,{vtkErrorMacro:lC}=jt;function cC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(iC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(iC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(iC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(iC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(sC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(sC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(sC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(sC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===sC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(lC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case iC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case iC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case iC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case sC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case sC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case sC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const uC={orient:!0,orientationMode:iC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:sC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function dC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,uC,n),nc.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),cC(e,t)}var pC={newInstance:jt.newInstance(dC,"vtkGlyph3DMapper"),extend:dC,...aC};const{vtkErrorMacro:fC}=jt;function gC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(fC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return fC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return fC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return fC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const i={x:n,y:r,midpoint:o,sharpness:a};let s;for(t.nodes.push(i),e.sortAndUpdateRange(),s=0;s<t.nodes.length&&t.nodes[s].x!==n;s++);return s<t.nodes.length?s:-1},e.setNodes=n=>{t.nodes!==n&&(t.nodes=n,e.sortAndUpdateRange())},e.sortAndUpdateRange=()=>{t.nodes.sort(((e,t)=>e.x-t.x)),e.updateRange()||e.modified()},e.updateRange=()=>{const n=t.range.slice(),r=t.nodes.length;return r?(t.range[0]=t.nodes[0].x,t.range[1]=t.nodes[r-1].x):(t.range[0]=0,t.range[1]=0),(n[0]!==t.range[0]||n[1]!==t.range[1])&&(e.modified(),!0)},e.removePoint=n=>{let r;for(r=0;r<t.nodes.length&&t.nodes[r].x!==n;r++);if(r>=t.nodes.length)return-1;const o=r;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addSegment=(n,r,o,a)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=o?t.nodes.splice(e,1):e++;e.addPoint(n,r,.5,0),e.addPoint(o,a,.5,0)},e.getValue=t=>{const n=[];return e.getTable(t,t,1,n),n[0]},e.adjustRange=n=>{if(n.length<2)return 0;const r=e.getRange();r[0]<n[0]?e.addPoint(n[0],e.getValue(n[0])):e.addPoint(n[0],e.getValue(r[0])),r[1]>n[1]?e.addPoint(n[1],e.getValue(n[1])):e.addPoint(n[1],e.getValue(r[1])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return e.sortAndUpdateRange(),1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{const e=t.nodes.length;if(e<2)return-1;let n=t.nodes[1].x-t.nodes[0].x;for(let r=0;r<e-1;r++){const e=t.nodes[r+1].x-t.nodes[r].x;e<n&&(n=e)}return n},e.getTable=function(e,n,r,o){let a,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=0;const l=t.nodes.length;let c=0;0!==l&&(c=t.nodes[l-1].y);let u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const mC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function hC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),gC(e,t)}var vC={newInstance:jt.newInstance(hC,"vtkPiecewiseFunction"),extend:hC};const{InterpolationType:yC,OpacityMode:TC}=Og,{vtkErrorMacro:bC}=jt;function xC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(bC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=vC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=oC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=vC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return bC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(bC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(yC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(yC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(yC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(yC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const CC={independentComponents:!0,interpolationType:yC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function SC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,CC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:TC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),xC(e,t)}var AC={newInstance:jt.newInstance(SC,"vtkVolumeProperty"),extend:SC,...Og};const{vtkDebugMacro:IC}=jt;function wC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=AC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){IC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const PC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function OC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),wC(e,t)}var RC={newInstance:jt.newInstance(OC,"vtkVolume"),extend:OC};const{BlendMode:MC,FilterMode:DC}=Mg,EC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=vC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function VC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(MC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(MC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(MC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(MC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(MC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(MC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(MC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(DC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(DC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(DC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(DC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Do(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const LC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:MC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:DC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function BC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LC,n),Bs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),VC(e,t)}var NC={newInstance:jt.newInstance(BC,"vtkVolumeMapper"),extend:BC,...EC};const{InterpolationType:_C}=eg,{vtkErrorMacro:FC}=jt;function kC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return FC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(FC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(_C.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(_C.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(_C,t.interpolationType)}const GC={independentComponents:!1,interpolationType:_C.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function UC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,GC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),kC(e,t)}var zC={newInstance:jt.newInstance(UC,"vtkImageProperty"),extend:UC};const{vtkDebugMacro:WC}=jt;function HC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=zC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){WC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const jC={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function KC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),HC(e,t)}var $C={newInstance:jt.newInstance(KC,"vtkImageSlice"),extend:KC};function qC(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const XC={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var YC=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,XC,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),qC(e,t)};function ZC(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:QC,otherStaticMethods:JC}=Ul,{SlicingMode:eS}=sg;function tS(e,t){function n(){let n;switch(t.slicingMode){case eS.X:n=0;break;case eS.Y:n=1;break;case eS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case eS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case eS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case eS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case eS.I:l=Ma(a[0],i[0],i[1]);break;case eS.J:l=Ma(a[1],i[2],i[3]);break;case eS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case eS.I:case eS.J:case eS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case eS.X:e.setSlice(r[0]);break;case eS.Y:e.setSlice(r[1]);break;case eS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(eS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(eS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(eS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(eS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(eS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(eS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case eS.X:n[0]=1;break;case eS.Y:n[1]=1;break;case eS.Z:n[2]=1;break;case eS.I:ea(r,[1,0,0],n);break;case eS.J:ea(r,[0,1,0],n);break;case eS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==eS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case eS.I:r[0]=a,r[1]=a;break;case eS.J:r[2]=a,r[3]=a;break;case eS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case eS.I:a[0]=s-r,a[1]=s+r;break;case eS.J:a[2]=s-r,a[3]=s+r;break;case eS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const nS={slicingMode:eS.NONE,closestIJKAxis:{ijkMode:eS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function rS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nS,n),YC(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),Ul.implementCoincidentTopologyMethods(e,t),tS(e,t)}var oS={newInstance:jt.newInstance(rS,"vtkImageMapper"),extend:rS,...QC,...JC,...sg};const{vtkErrorMacro:aS}=jt;function iS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function sS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void aS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=gu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=mc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Es.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Es.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Es.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),iS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),iS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),iS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),iS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const lS={outputPointsPrecision:js.DEFAULT};function cS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),sS(e,t)}var uS={newInstance:jt.newInstance(cS,"vtkAppendPolyData"),extend:cS};function dS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Nf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=gu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const pS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function fS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),dS(e,t)}var gS={newInstance:jt.newInstance(fS,"vtkConeSource"),extend:fS};function mS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Es.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Es.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Nf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=gu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const hS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function vS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),mS(e,t)}var yS={newInstance:jt.newInstance(vS,"vtkCylinderSource"),extend:vS};function TS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=yS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Bf().rotateZ(-90).apply(a).apply(i);const s=gS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Nf().translate(1-.5*t.tipLength,0,0).apply(c);const u=uS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Nf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Nf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Nf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const bS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function xS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),TS(e,t)}var CS={newInstance:jt.newInstance(xS,"vtkArrowSource"),extend:xS};function SS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Bf().translate(...n).apply(e.getPoints().getData())}function AS(e,t){const n=e.getPoints().getBounds(),r=[0,0,0];r[t]=-n[2*t],Bf().translate(...r).apply(e.getPoints().getData())}function IS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Es.newInstance({name:"color",numberOfComponents:3,values:a}))}function wS(e,t){t.classHierarchy.push("vtkAxesActor");const n=nc.newInstance();e.setMapper(n),e.update=()=>{const e=CS.newInstance({direction:[1,0,0],...t.config}).getOutputData();t.config.recenter?SS(e):AS(e,0),IS(e,...t.xAxisColor);const r=CS.newInstance({direction:[0,1,0],...t.config}).getOutputData();t.config.recenter?SS(r):AS(r,1),IS(r,...t.yAxisColor);const o=CS.newInstance({direction:[0,0,1],...t.config}).getOutputData();t.config.recenter?SS(o):AS(o,2),IS(o,...t.zAxisColor);const a=uS.newInstance();a.setInputData(e),a.addInputData(r),a.addInputData(o),n.setInputConnection(a.getOutputPort())},e.update();const r=jt.debounce(e.update,0),{setConfig:o,setXAxisColor:a,setYAxisColor:i,setZAxisColor:s}=e;e.setConfig=e=>!!o(e)&&(r(),!0),e.setXAxisColor=e=>!!a(e)&&(r(),!0),e.setYAxisColor=e=>!!i(e)&&(r(),!0),e.setZAxisColor=e=>!!s(e)&&(r(),!0)}const PS={config:{recenter:!0,tipResolution:60,tipRadius:.1,tipLength:.2,shaftResolution:60,shaftRadius:.03,invert:!1},xAxisColor:[255,0,0],yAxisColor:[255,255,0],zAxisColor:[0,128,0]};function OS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PS,n),hs.extend(e,t,n),jt.setGet(e,t,["config"]),jt.setGetArray(e,t,["xAxisColor","yAxisColor","zAxisColor"],3,255),wS(e,t)}var RS={newInstance:jt.newInstance(OS,"vtkAxesActor"),extend:OS};const MS="resetcamera",DS="orientation",ES={MODE_RESET_CAMERA:MS,MODE_ORIENTATION:DS,MODE_SAME:"same"};function 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i,s,l;a&&(t.mode===DS?(r[0]=a[0]-a[3],r[1]=a[1]-a[4],r[2]=a[2]-a[5],qo(r),o.setPosition(t.focalPoint[0]+t.distance*r[0],t.focalPoint[1]+t.distance*r[1],t.focalPoint[2]+t.distance*r[2]),o.setFocalPoint(t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),o.setViewUp(a[6],a[7],a[8])):(o.setPosition(a[0],a[1],a[2]),o.setFocalPoint(a[3],a[4],a[5]),o.setViewUp(a[6],a[7],a[8])),t.mode===MS&&t.dstRenderer.resetCamera())},e.delete=Vt((()=>e.setSrcRenderer(null)),e.delete),a()}const LS={mode:DS,focalPoint:[0,0,0],distance:6.8,active:!0};function BS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LS,n),yt(e,t),At(e,t,["mode","active","srcRenderer","dstRenderer","distance"]),Pt(e,t,["focalPoint"],3,0),VS(e,t)}var NS={newInstance:Et(BS,"vtkCameraSynchronizer"),extend:BS,SynchronizationMode:ES};const _S={},FS={CameraSync:class{constructor(e,t){this.ctx=e,this.behavior=NS.newInstance(this.getProperties(t)),this.behavior.update()}getProperties(e){let{actorBounds:t,srcRenderer:n,dstRenderer:r}=e;return{distance:3.4*Math.max(t[1]-t[0],t[3]-t[2],t[5]-t[4]),focalPoint:[.5*(t[0]+t[1]),.5*(t[2]+t[3]),.5*(t[4]+t[5])],mode:NS.SynchronizationMode.MODE_ORIENTATION,srcRenderer:this.ctx.getInstance(n),dstRenderer:this.ctx.getInstance(r)}}update(e){this.behavior.set(this.getProperties(e)),this.behavior.update()}delete(){this.behavior.delete()}}};var kS=function(e,t,n){const r=e.get("synchronizedViewId").synchronizedViewId;if(!t.behaviors||!r)return;_S[r]||(_S[r]={});const o=_S[r];if(t.behaviors.autoOrientation){const t=e.getRenderers();if(!o.autoOrientationAxes&&2===t.length){let e=null,n=null;for(let r=0;r<t.length;r++){const o=t[r];o.getInteractive()?e=o:n=o}e&&n&&(o.autoOrientationAxes=NS.newInstance({srcRenderer:e,dstRenderer:n}))}o.autoOrientationAxes&&2!==t.length&&(o.autoOrientationAxes.delete(),delete o.autoOrientationAxes)}const a=Object.keys(t.behaviors),i=Object.keys(o);for(let e=0;e<a.length;e++){const r=a[e];if(o[r])o[r].update(t.behaviors[r]);else{const e=t.behaviors[r];FS[e.type]?o[r]=new FS[e.type](n,e):console.log("No mapping for",e)}}for(let e=0;e<i.length;e++){const n=a[e];t.behaviors[n]||(o[n].delete(),delete o[n])}};const GS={},US=/instance:\${([^}]+)}/,zS={},WS=[],HS={},jS={vtkPoints:mc,vtkCellArray:cc,vtkDataArray:Es};function KS(e){return e.map((e=>US.exec(e))).filter((e=>e)).map((e=>e[1]))}function $S(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return e.dependencies&&e.dependencies.forEach((e=>{t.push(e.id),$S(e,t)})),t}function qS(e,t,n,r){if(!t)return Promise.reject(new Error("No instance provided."));const o=GS[e];return 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1vdb8q+Wj5l/Jx9L8JNenNH9JPpbmN61+1Pwr+VjR/CvXvzR/as8zml8lH0vzr/Y8Izv5Upq/JR/r9+X2V/Wj5l8539L8MPGX5o+Jp752fqt81Pyr6bXmX+dh2blfXPr8tem17JaPF2X+OrPS/DjHV2/L/M1DVfP/Vj9q/p98rGv+n/ZL8/f2PNNb4y+U5v/teUZ24qms+a+erzX/b/XjpMzfNDSma/zZ+iHlx6y0vsHyUfP/ptea/7f68TbmH+8jrX7sXEzj+pTHQq/z9SmP50X96O9H7XkmX7/xmOtXcX9/zPW6WP/xmOt1JV+f8pjrdSNfn/KY62MzX5/ymOt1sf7kMdfrYn3KY67XVdmtfszc3+6P5+X2V/Xjvfdv+aj4rH7MPH57nlk7v1g/+vmbXt87P9/Nl/30I1/f81jo9a3brX5My/x1ZsX6oeL4Qn/z+EL1zeOz+vHO47f747Gfn9WPIz9/8rFY/5TzF4r1UTm/oSG71Y/jsv/q/qjrY/XjvMzfNDRe1/gLRf2V58esofOz+lHv702v586P1Y/tmH8830zR692bycKet2da/wg+5f6o569zrX8EX2n9Y9HeKh+5/xftFesfi/bi/HU8/6K9Yv1j0V7Mx7g+r2jvYGjr9wr/mI9uv+L+OHD/mdWP5faL9Y8636riu9L6R8VTWzu/Yv2jjt9ZOD/TsHy+f72I6x/dnq9/dP+wWy3zV6x/1PHVNf7muf56/LO6+JvZ/LWf35XWPzo/D7Y+1Pmz9TfiNzRkJ57Kmv+qfpx4+zOtf/TrGxrXa/yFxhp/xfpHz5+HUv7MWP8ofjjfRtXy7ye/PluNo7Mr0++Z9iOAT7X/AGz5yPMZ+Er7C4r2iv0ERXvF/oGivWK/QNFesT+gaK/YD1C0dzC09daF/yofz91+pfX9RX/Fev6i/WL9vs63pviIv6b4LR/Xzu9vuJf4+V9pvT34du+7c/++HdfXuz1fT+/++fp5b39mz786vrbGX7Ee3uOflfR3pvXuar8q/k61nt35C7Z/wvkNJX7PtT7dr0++Ht2v39/WGn/TYPW94rfnT8+PWVPnRz6W8mem9eI63616zL+Di5/k40D7Zw5ut/c+gvbLHDxWvz46+Xqz1uNW5a36+NHXfpjKVuPiY6T9L5Xtg8PY3rR4/uj3urG91fMM9d/B1WX9vbSf5WD34fXd5me4/yUHHyfvDe1XqQy+k/fS/pTK8e7Xe2k/SmVr6+K9WG+28j/oD7rvpf0mB6d3tF/sLzkY/NAe681ifNkB/sX+kcru7et78TyzOr+DLL14X2h/SOWmAS72gxz4/6XwF+0De54x+8p/UDzPRP9V+4PieYb2Dy4Gpf0bMb6BzV8bf6v4B6X9GfH8BjZ/zfnF8x/Y/AznH/kZ7ObPMzl/g9L+isjvoLSfIvI/KO2fiNdnUNovEa/fMGh/RLy+wwPth4jXfzjS/oeYH8O+9jvE/Bl28vVmq/OP+RXb43km8rPKv9hesX8h5mdm98d6cX/M7P7o+Ztt3B+zc+3HAl/l9WM+PjK7P/r4yez+6OMrs/ujj7/M7o8+PrON+2Nm90cf39mp9kMVx6/y0e8Pmd0fk3L7q/uj31+yjftjtnF/tHhqa+dX7E/S8br/ZdPRQWdf98fM7o/iz+6PSZm/Yj+Rji/1v3F/zOz+KP7s/ij+7P4o/uz+eL7GX5C+ZBv3R4tH+pTZ/VH82f1xjb9iP47ib67xV+y38fwp3R8zuz+W+CMfc33+y69Pod9/eT5K37ON+jHbqB+zjfrR8lH1heWj6g/LR9Un2Ub9mOej1zeWj6p/8nyU/Ur7Z4r+VvXjqNz+qn70+svOV/VZtlE/Zhv1o7Wv+s+OV32YxfrxXfWjXR/Vl5Yvu2v8levHbKN+tOuj+tbyUfWv5aPqY2tf9XOej2GNv6D6O89H2TfqR8tH1feWj5U1/or9M4q/scZfuX7MNurHbKN+tPO155f4fJPZ83W7eL7O7Pnan38ye77256Psrbx/ZoXt+dqfrzJ7vvbnr8yer/35LNt4vs7s+dqf77JFef+Mtaf9M+av/TN2/Or52p8vM3u+Pi63v8pHfz6189Xzq8Wv51uLp7p2fqvna38+tuP1/Jx9N2/3B3q+zuz52p+/M3u+bpf50/6Z4ng932fz8v6ZPP6Z3g9k9nzt7w+sfb1fyJ7L+2dy/oLeT2SL8v6ZIh6938g2nq8ze75+KPOn/TNF/Hq/kr2V98/k+fOg9zOZPV/7+xs7X3u/w/3S3j/eFu8fs3y+sHg/ZPmo90eWj3q/ZNdH75/yfPT3U9nG+0fLR73fsuuv91+WjyV9sHz092eWjyX9Xazvn7F81Ps5y0e9v8vz0d/vWfx6/2fx1NfOr/z+Mdt4/5jF94/vev+Ybbx/zDbeP9r11/tNO17vP/N89Pejlo96f2r5qPer1r7ev1o+lvTd8tHf31o+luqnt/X9M5aPej+cbbx/zPMxW+OvtH/G8lHvpy0f9f7a8lHvt+187f13fD+ebczPWD7q/Xmej/5+PduYn8k25meyjfkZy0e938/vj/7+366/5gcsHzV/YPmo+YVsY37G8kXzE/n98a3c/up5xuc37Hw1/2Hxa34k25ifyYrnmVH5eM2/ZN9b39MLzc9kxfNMYV+U98/k7c/W258FzQ/Z9dH8keWj5pfy+6P4W+TPM8X5b8zPWD5qfsvyUfNfFo/mx/L7o8+fWT5qfi3bmJ/JNuZnso35mTwfdf42P+Pzf1lpfibOHxb5WMxf53rt84vZxvx1tjF/nW3MX2cb89d5Pvr8p+Wj5kezjfnrPB99fjXbmL/ONuavs435a8tHze/m+ejzv3k+Kr6F9s8ontra+ZXnr3O99vnpLM5fDzR/neu1+NuYv8425q/teM2f5/no8+t5Poo/m7/2+flsY/4625i/zjbmr/N89PUB2cb8teWj1hfk+XhT5q88f51tzF9nG/PX2cb8da7XJf7IR9ZPxPUVef04Ltb35Hrt6y/y+tHXZ2Qb63uyjfU92cb6nrx+9PUh+f3R149kG+t78nz09SfZxvqePB99/Uq2sb4nvz9Oy+0/X2p9TLaxvifbWN+TbazvyetHX5+T67Wv38nm6fRuX+t7cr0Wf3n9WOavvL4n21jfk22s78nrR/Fn90fxt7G+J8/Hdf5K63vyfBR/G+t78vuj+LP6cY2/8vqebGN9T7axvqeoH/388/lC8Uf9yPoynm+sfrwo1j/m90dfn5ZtrH/Mn2d8fVuej77+Lb8/+vq4vH709XN5Pvr6umxj/aPlo9bn5fdHX79n+aj1fZYvWv+X52Nabr+8/jG/P/r6wjwfFf+b9s+ofa1fzO+Pvr4xi+sf37X+Mb8/ir/F+vrH/HmmvcZfaf1jfn8UfxvrH/N89PWd1r7Wf1o+an1ofn/09aOWjyV+N9Y/5vno61Pz+vGyzN80bK3zV1r/mD/P6Pw21j/m90fxZ/Uj62/j+txsY314trE+PK8ffX1vno++/jevH319sOWj1g/neu3riy0ftf4421gfnj/P+Pply0etb86fZ2RfaP9M0d+irvXT2cb6cDtfrb/O60fF/6b9M2pf67vzfPT139m8P/141/rwPB/F30L7Z7z9mdaf5/VjKPNX7J/x+Evrwy0ftf49vz/6+vj8eeZpjb+g9fX584zsG+vDLR+1fj/X67syf8X+GcWv/QF5Pur8nrV/xvOrtD48K60PjzMLme2feS32z2S2f8b3N2S2f8b3P2S2f+as2B+R2f4Z3z+RbeyfyTb2z2S2f8b3Z2Q2P1Mp9m9ktp/L93dktn+mVuz/yGx/Yep2m5/x/TeZ7Z85LLe/ykf1b/tnfH9KtrF/xuLR/hZrX/tfso39M1ncP3PxVSn2z2Qb+2cy2z/zr8zfSq9P1/gr7Z/JNvbPZDY/4/t/Mts/I/5sfsb3v2Qb+2cy2z8j/mx/ofiz+Zlm2X/1fC3+bP/MTpm/8v4Zi3/ro8zfSq91frZ/xvcfZbZ/RvzY/hn2b8X9XXZ9bH8X+E/3R/Cu9s+Aa9o/A25o/0zRXrF/pmivvL/Q8lH71+z6a39bno++/83yUfvjLB+1fy7b2F9o+aj9d3k++v48O1/t38vzUfHXtH9G7Wt/YLaxvzCrNLo7F9pfmG3sL8zzcVrmr9g/o+O1v9Guj/Y/5vno+yMtH7V/MtvYX2j5qP2Xlo/an2n5qP2bFk9zzX91f/T9n9nG/kLLR+0fzfMxLfNX7J/x/HnQ/tQ8H33/qp2v7W/lfeTG/us8H31/bLax/zrPR99fm23sv7Z81P7cbGP/teWj9vfa9df+3yzfX1jsD7Z81P7hbGP/dbax/9ryUfuTLR+1f9nOV/ubs43913k+rp1fef91no++vzqL+68H2n+d56P429h/nedja42/0v5ruz7aH275qP3jlo/aX55t7L/ONvZfWz5q/3p+f5Td9l9Xy/6r+lHv/zf2X1s+bq3zV9p/neejzm9j/3WejyX+tP86fh8g2/g+heWjvh9g+ajvC2Qb36fINr5PkW18n8LyUd83yPPRv3+QbXyfwvJR30/INr5PYfmo7y9kG9+nyPNxWm6/eL7W+er7D9nG9ymyje9TWPv6vkTW0PM1uPLX3drX9yns+uj7FZYv22v8lb9PkW18nyLb+D6F5aO+r5Hno/jb+D6F5WNtnb/S9yksH/X9j2zj+xR5Poq/Pz1f+/UtfZ8i2/g+RbbxfQrLR33fxPJR3z/JSt+niD0V9WPx/Z78/ujfV8k2vt+TbXy/x66Pvt+S3x/9+y65Xvv3Xywf9X2YbOP7PdnG93vy+6N/fybPR/8+Tbbx/R7LR33fJtdr//5Nfn/07+NkG9/vyTa+35NtfL8nvz/693my+P2efX2/J78/ir+N7/dkG9/vyTa+35PfHxVfTe8f/fz+9H2ibOP7PXk+rvNX+n5PtvH9nmzj+z2Wj/q+Uq7Xt2X+ivePir+yxl/5+z3Zxvd78vtjiT/uj3w/Kn5fyq6PfV8K/Feen0mK5xn/PlW28X2zvH7071vl+ejfv8rz0b+Pld8f/ftZWbHeLP++Vp6P/v2tPB/9+1x5Pvr3u/J89O975ffH13L7z5f6PlhWrDcr4ivWmxXxb3zfLK8fdf4cr++XZcv+QetC3zez66Pvn+X141OZv/L3zbJivVmJv/L3zfJ8FH9/5fmZon193y3bLc/P5PwFfR8uK9abFfZaeX4mvz4P+v5cno+HZf40P1PEv7XGX/n7ZlnxPd2R8oHv5zp/rO/h+3rMH1o+DorvP+Z6rfnXje8/Wj6W5icb6/tnLB9L85tWP2r+1Z6vNf9aW98/kz/PaH7Vnq81/7q7vn/G8qU0f2vP1/Ny++XvP9r5luaHG+v7Zyye+tr5rfJR86+m15p/jd9/fNf3H3O91vy/5eNlmb/y/hk7Xt+ftOuj71NmG99/zJ+vNf/fWN8/kz/P9Nb4K+2fyZ9nZK+t75/Jn681/2/147TM3zTo+54Wv77/aflYWt+w8f3HXK+VX5aPfH80fp80f76+Kr6Pm+u1f7+0qB+L75vmzzP+/dO8fvTvo+b56N9PzetH/75qfn/076/m9aN/n9XyUd9vzfPRv+9q+ajvv+b1o38fNr8/npbbX9WP/n3ZPB8Vn9WP/n3a/Hlm7fxi/ejnb3rt379d6fW8P9D3cXO99u/n5vVjKPOn/TPF8fo+b56P/v3evH707/vm90edn9WP/n1gy0d9PzjPR/++sOWjvj+c14+jsv/q/ujfL87rx7syf6t8fFvjL+j7yPnzjM7P6kf/vnKu17fij+drvs8cv9+cP8/0i++HWz7q+875/dG//5zrtX8fOr8/+vej83z070vn+ejfny7qx+L71Pn90b9fXeRj8X3rPB/9+9dFPrq9ofWPRX9x/rrcfrH+Ueer73Pn90fFX9P6R7Wv73/b8fo+eBa/H/6+vCq+H27Xp2RvaP2jtz9bb7/8/fA8H/375nk+ij+rH/376Pn9Uee/q/0zzl/Q99fzfJS9pvWPfn0e9H33PB/X+Cv2zyj+xhp/5e+H5/dH//685WOzxB/PM3y/Pn7fPn+e4fv24D+tDwfb73fw/gVc0/4ZcEP7Z4r2iv0zRXvF+vCivWJ9eNFesT68aG+Vj6xvLtor9s8U/sX68OL4Yn140V+xPrxov1gfrvOtKb6G1ocrntra+RXrw3W8rQ8HVyr18f4v68Pdnq8Pd/9Q8q9p/4yOL57P8viC1ccef74+3M8vXx+u9qvir/R7EM5f0O+V5Pkoe03rw/365OvD/frl68P9+obmOn+hucZfsX/G8ydfH+75la8P1/nq92Xy+tH2z0T8V9o/E3HxveZWvl6peJ6p5euJGto/U7Q3Le3f9+eZr3y9UvE8c5uvZyrtnynaC0P9fkZW2j9T+PM8U9iL55nCv3ieKbX/fJnvP/Hz3df6tdL+GcVTWzu/P8u/0vF74if+fsrFr/grnmcKe/E8U+LP98/48ftr/Pn+mSL+WVX8lfbPqP2q+Cvtn3H+Qom/Rr7erLAXzzMlf9abFe3/5evNSvxNS98X8OeZEn++f6bIn2L/TJFff6X8Wmr/zH/+z3//139u9muNemO3ub1fvbm53q1e1fdurxq7t9fX1dt6vdn83zu7je3t4dovNf3/+pR+qun4/uX//UtN6c3Tk//n8o81Mb3E/yX8GydiV6pwEM8i/t/Ucdiwt8BLPwh7Ml23Gz6THf/j+E9X/meOrVP8exyP/dH9zQ4eyP81/tPHvnT/gfDS/e0kFvGfsfyXjoPObwIm/ob8546n7m/MZZy0zn/qOOm5fyv6J1Azk3/PcVvnD0kt7Mfuj93wqfzh7wS7zv/UsZGOfwf/q/jvufznjjs6/wsw8S/kP3d8JX86vYz/dHX+V46Tqvt38d+Of97Kv+q4p/Mnkm60py33x2743v1T/usddp3/veNU+WlJgv1J/lPHfZ3/Mzia0iv5zx2/yh+nl/jPQOf/6jh9df8B/uTvh/xfHQ91/pDMRsF02/2xG/6SP/x9Ytf5fzm2pA9uScnfH/c3O3ik8//lLzbiaPxjN7wtf076L/4z1vlvOw4a/2P8yd89+Z85nuj8Oakxdo1/G5TgffnDXw27zn/fcdD4Z5AGqG3Kf+k40/lvgW0jkvznBbZBj7/dCSvxn6mfv9nBLY3/afRvRYckyL/n+NDP324SU+wa/9jBSUf+kb+EW8Ohn7/ZwS2N/yP8Y/4mffnPHR/5+SfcP4+IX+MfOzgZyZ9BTGrOdP4jxy2N/xn+MTWSTP5Vx8c6f1qaRXtb45+7alt8Huh8ObVjXf8gLP5otc3xx/I/dpzIHz2ym2rJf+lY/KFHCYeeKH/6wuIPPWpzvf7Jf+E4aPygRyccr+uH3bD4Q49MBE7lnzlOxB961CbeC/fHbril80ePTmNXbcWP3fCJ/OGP+7+d1Mjt4ET3T/TIROla/leOO4ofPfpHvMqfjvCl/OHvhuMV/6XjRPdP9KgNX3P5bzvuyh89gtSO+MNu+M790aMEfKbrL5wqH9GjDuP9Qf4jx32dP3rEcqBOSb+mjl/kD3/cFM/l/+I41f0TPepwvZ7l/+h4oPNHj85j/B1dP+yGP+UPf/R3ceD+n45T3T/RIysi3uTfcDxS/OjRBfFKP0fCf+6PHiXvHK/4/xwHjV/0qEO+LOR/7Hgsf/SIJOoof7Abrskf/jjfS13/muOg8YMedbjffct/4TjT+aNHl9FulXJwu+GK/OGPyK/kX3HcUv6iR13y9df9W8JT3U85/irG31X+Ygeb3hy4PiXwbYNadnBL+YMeWVG4I/8rx0fSD+6f18Sr+uHIsemN+cPfLscr/qHjlq4fetSlv6r8tx3P5M/9k5tKV+MHOzg5dH/0KeF630g/0aMb7Bt61CXehvxHjk/EH0nLTair+E8cJ7r/okcJ+XKr/k8ct8UfetQl/5Q/pkfgE50/t+7biK0oD263J7Mz+XOTA851/c4ct8UfetSLfKWp/B8dnyr+C/wj7om/U8eJ7v/oUYr9TvxdOm6r/uOketwUlb/YDf9T/KTuHfGKv3+OTW/MP/KZUm/dib8bx23d/9CjXsw/e2g5cLvhM8UPE/YQI/7OHCfSH/QoZbzci787xx3VS+hRL94/U40f7IbPFD96dE+84u/MsenNgeuTidyD+Ht03NH9Fz3qxfxNJ/I/dnyu+NGjB+IVf+eOE+kfepQyXh/F34vjjuqni/iPPQRq/GI3fKH40aPHiPvi78Kx6Y35wyfj/VH8vTvu6P6PHvWjPVX9id3wpeLHyR5Kxd+l40T6ix6ljPcn8ffpuKP6CT3qxyxKVX9iN3yp+NGjJ+IVf5eOTW8OXJ9SUvNZ/C0dd6U/6FGf8X4h/5bjK8WPHj0Tr/i7cpxI/9GjFGpexN+f467qJwZ1n/Gu+hO74WvFjx69EK/4u3ZsemP+8Ml4fxF/u4670j/0qM94V/2J3fCN4qdTe4kg/m4cJ6o/0KOU8f4q/mqOu6qf0KcB4131J3pkReKt+mfQMQgGyr9bx4men9GjlFR9U/9Nx13xhx4N4EvjF7vhW/GHHr1FPFD+3To2+ThwPbKXKO9+/cz+7npj/ujRAL40fk2PwHOP3/TonXiVf3PHqZ4/0aOUevnD+TM7uKf84yLZSx2NX9Mj8J3iR48+iFf83TlO9fyPHqW87/lw/sz+4XqT+8Mf+afxa3oEvlf8sMqZD8XfveNUz7/oUUq9vhB/fcc95R96NCT/NH5Nj8D3ih89WkQ8FH/3jlO9f+j5Q8jBp/gbOe6pfkKPhuiNxq/pEfhB8aNHn8Qr/h4cp3r+Ro9Snhe+xF/m2JjCn0E2JH81fk2PwI+KHz36Il7x9+jY9Mb84ZP3XV/i78hxX/UTejQkf1V/mh6BnxQ/gx4RGIq/J8ep6k/0KGW8f4u/E8d91U/o0ZD8Vf1pegR+UvzoET9kMxJ/T45T1Z/oUT4cxd+Z477qJ/RoFPM3qP7EbvhZ8V9gjngk/p4dp6o/0aOA/Uf8XTruq37iJmcvVVV/Yjf8ovjRox/iFX8vjlPVn+hRYLz/iL8bx33VT+jRKOZvUP2J3fCr4ocJewks/l4dp6o/0aNAFv+KvzvHA9VP6NEothJUf2I3/Kr40adf4hV/9pJAemP+Mf8C78v+1P+T44H4Q49G8KXxi93wm/rnUv1FbDNFwe3gVO//0SN7c7et6/fqeCD+0KMxfGn8Yjf8rvjRo+2Ix8q/d8ep5g/Qo8D7Onuolh08EH/8R3spr/FrXyMEfyh+9GiHeMXfh+NU8w/oUTjlePH35Xig/EOPxuSfxi92wwvFT0vclMfib+E41fwFehR4X7gr/n4cD5V/6NGY/NP4xW54ofjRo13iFX8Lx6nmP9CjgEjsib9tx0PVT+jROJqCxi92w5+Kn1PfI17x9+k41fwJehQozarib8/xUPUTF8UmUTR+sRv+UvzoUTXiifj7cpxq/gU9CpQGVfG373io+gk9mkR70PMjdsPfip+Ttkkd8fftOFX9iR4FxntN/DUdD1U/oUcTxrvqT+yGvxU/elQjXvH37dj05sD1yCah9p0/s++73th/QI8mjHfVn6ZH4KXHb3q0T7zib+k4qP5EjwJDu+78mR08Uv3EoLBJMdWfpkfgH8WPHtWJV/z9OA6qP9GjQGrVnT+z111vcn/4Y7yr/jQ9Av8qfpKOJMjE36/joPoTPQqcWkP89R2PVD+hRxnjXfWn6RH4V/GjR42IM/H36zio/kSfAuO9Kf5QiabrjfmjRxl8afyaHoH/1D961KQ/5d+fY9ObA9ejQCdb6n/qeCz+0KMMvjR+TY/A2+IPPdoiXuXftuOg9QvoUSDVrKgbuR08Fn/oUb6aTP5TxzuKHz2qEK/423FsemP+8Ml8bUX8nToeK//Qo8xKX+YSZQfvKn7TJyZ3W0nhb/aIg9ZPmD4ltDdvFf5mj3is/DN96ll73n9un+Z6Y/65PsX2puofe8RB6y9Mn1rRP5l6/9jBY9Xvpk/Hsb2O+scO3tP1R48SzufK+zd7xEHrN0yfutE/PfD+sYPtTR/+pk/z2F7f+zc7uKrrjx6lnM+r92/2iIPWf5g+Deh/6f1jB09UP5k+LRIO8v5HBwW2hzLpU+B8tr1/s0ccVH+aPo2jf9D1xw6eqH4yfarG9jL1jx1c0/VHj0KMPzlQ/9EODqo/TZ+m0b+l648dPFH9hB4l5POR+scO3tf1R49aMf5k5P1jBwfVn6ZPs+jf1vXHDp6ofuImkZDPJ+ofO7iu648etWP8yZn3jx0cVH+aPp3Sv64/dvBE9RN6lJDPZ+ofO7ih609QnRh/8uj9YwcH1Z+mT+fRv6Prjx2cqX5CjxLy+dL7Nzu4oeuPHnW4/kv1T/wRB9Wfpk9X0b+r648dnKl+Qp8S8vlG/ZN/txE31T961I39p8o/7OCg+TPTp3n076l/7OBM+YcepeTfvfrHDt5S/qFHvchfqvzbsvn0pNAb8yf/HqJ/X/mHHZwp/9CjlPx7Uv/YwRXlH3rUj/Gnyj/sYNMb8+dUn+lf+YcdnCn/0KOU/HtV/9hf8xWDB8ULj4gHEafKvwPhxP1Nn9443vPP7IZ78odP8u9D/r0Sdn/0aMD1V/4dCLfkD5+LiId+/c1u+Nj90aOU/PuS/7HjRPGjR0PibchfuCt/+PyOeCT+usJz90ePAvn8I/+541Txo0ejiIP4S4UH8ofPX44XfwPhhfwjDuTztvwXjoPiR4/GxCv+gvBY/vC5w/HibyxclX/EgXzek3/VcVD86NGYeMVfEJ66v+lTNeKJ+Js6TlQ/oUeB/N2Xf8txS/GjRxPiFX8t4Zn84bMecSb+Zo4T1U/oUSB/m/KfOm4rfvQoI17x1xY+lT98bnG8+Dt1nKh+Qo9CzN9E9Sd2wx3Fjx5NI26Jv47wufwjnwnjfSr+zh0nqp/Qo1bM30T1J3bDHcWPHk0jbom/jvCV/COfCeP9UPxdOU5UP6FHrZi/iepP7Ia7ih99OiRe8Ycetci/ufujR0kv4iP1P3ecij/0qAVfGr/YDffUP3p0RP/Kv57wg/xj/iXDiGe6fg+OU/GHHrXgS+MXu+G+4kePZsSr/OsLP8s/5l8y4Xhdv2fHqfhDj1rkn8YvdsMDxY8eHUfcFn8D4Tf5w+chx4u/N8ep8g89apN/Gr/YDQ8UP3p0HHFb/A2EF/KHz+OIT8TfwnGq/EOP2uSfxi92w0PFjx6dEK/4Gwp/yx8+/0V8Kv6+HQfVT+hRO+ZvovGL3fBI8aNHp8Qr/kbCv/KHzwuOF3+/joPqJ/SoHfM30fjFbnis+NGjf8Qr/sbCO/KHz2uOF387joPqJ/SoHfM3uZX/meOx4keP/kXcEX9j4ar84ZPxfib+qo6D6if0qMN4V/2J3fBE8aNHZxF3xN9EuC5/+GS8n4u/uuOg+gk96jDeVX9iN5wpfvTonHjFXya8JX/4ZLyfi78tx0H1E3rUYbyr/jQ9enW9wd/06IJ4xd/UcaL6Ez1KGO8Xzp/ZL1xvcn/4Y7yr/jQ9+nC9MX/06IJ4xd/UcaL6Ez1KGO+Xzp/ZL11vzB896jDeVX+aHn253pg/enRJvOLv0HGi+hN9ShjvV+IPPbpyvTF/9KgLXxq/pke/rjfmjx5dRdxV/h05Nr0xf/LvL+Jr9T903BJ/6FEXvjR+TY92XG/MHz26Jl7l38yx6Y35k3+7HK/rN3HcEn/oURe+NH5Nj6quN+aPHt0Qr/g7dmx6gz96lNQ4XvwdOm4r/9CjLvmn8Wt6VHe9MX/06IZ4xd+xY9Mb84fPRsS34u/YcVv5hx51yT+NX9OjLdcb80ePbolX/J04Nr0xf/isRDwXf/8ct1W/o0e9OLhTjV/shk8VP3o0j7gn/k4dm96Yf+QzTTle/F04bqt+Qo96MX9TjV/shv8pfvTojnjF3z/HpjfmH/lM2xwv/q4dt1U/oUe9mL+pxi92w/8UP3p0R7zi75/jRPUnepQy3u/F39xxR/UTetSL+Zuq/sRu+Ezxo0f3xCv+zhwnqj/Ro5Tx/iD+Hhx3VD+hR72Yv6nqT+yGzxU/evRAvOLv3HGi+hM9ShnvD+Lv2XFH9RN61Iv5m6r+xG74QvGjR48R98XfheNE9Sd6lDLeH8Xfm+OO6if0qB/zN1X9id3wheJHjx4j7ou/C8eJ6k/0KGW8P4m/heOO6if0qB/zN1X9id3wpeJHj56IV/xdOk5Uf6JHKeP9Wfx9O+6qfkKf+ox31Z/oUXrhemP+6NEz/Sn/rhyb3pg/+cfz4ov6/3PcFX/oUR++NH6xG74Wf+jRC3wp/64dm96YP/nH8+KLrt+u4674Q4/68KXxi93wjeJHj14jHij/bhyb3pg/+XfH8eKv5rir/EOPBuSfxi92wzeKHz16jXgg/m4cm96YP3w+Rvwm/hqOu8o/9GhA/mn8Yjd8q/jRozfiFX+3jk1vzB8+XyJ+F38Vxz3lH3o0IP80fk2P3lxv8Dc9eide8Td3bHpj/vD5zvHOn9nfXW9yf/hDbzR+TY8Wrjfmjx59EK/4u3NsemP+8PnJ8c6f2T9cb3J/+ENvNH5Nj75db8wfPfqIeCj+7hyb3pg/fC4jXoi/nuOe6if0aMh4V/1pevTremP+6NEi4qH4u3ecqv5Ej1LG+6f4GzruqX5Cj4aMd9Wfpkc7rjfmjx59Eq/4e3Ccqv5Ej1LG+6f4mzjuqX5Cj4aMd9WfpkdV1xvzR4++iFf8PTpOVX+iRynj/Uv8HTruq35Cj4bkr+pP06O66435o0dfxCv+Hh2nqj/Ro5Tx/i3+jh33VT+hR0PyV/Wn6dGW6435o0ffxCv+nhynqj/Ro5TxvhR//xz3VT+hRyPmj1R/Yjf8rPjRp2XEI/GHHo2kN+Yf8y/wvPij/i8d98UfejRi/lzjF7vhF/WPHv3Qv/LvxXGq+TP0KPC8+KPrd+O4L/7QoxHz5xq/2A2/Kn706Jd4lX+vjk1v8EePAvMVv7p+d44H4g89GjF/rvGL3fCr4kePfolX/L06Nr0xf/hkvuJP/D06Hij/0KMR8+cav9gNvyl+9OiPeMXfm2PTG/OHT+YrtsXfi+OB8g89GpN/Gr/YDb8rfvRom/UT4u/dsemN+cMn8xXb4u/d8UD1E3o0Zv5S4xe74Q/Fjx7tEK/4+3BsemP+8Ml8xY74+3Q8UP2EHo2Zv9T4xW74Q/GjRzvEK/4+HJve4I8eBeYrdsXf0vFQ9RN6NGb+XM+P2A0vFD96tEu84m/hOFX9iR4Fxvue+PtzPFT9hB6NGe+qP7Eb/lT86NEe8Yq/T8ep6k/0KDDe98TfruOh6if0aMx4V/2J3fCX4kePqqyfEH9fjlPVn+hRYLxXxV/N8VD1E3o0Ybyr/sRu+Evxo0dV1k+Ivy/HqepP9Cgw3mvir+F4qPoJPZow3lV/Yjf8rfjRoxrxir9vx6nqT/QoMN73xV/F8Uj1E3o0Ybyr/jQ9enO9wd/0aJ94xd/ScVD9iT4Fxvu+5v/Ro7rrjfn/2HqYeLzGr+nRp+uN+aNHdfpT/v04Dpo/G+XrYeLx6r/jeCT+fmw9TDxe49f0aOl6Y/7oUYP1E8q/X8dB67dG+XqYeLzWv/Qdj8Tfr62Hicdr/Joe/bnemD961GD9hPj7dRy0fmuUr4eZxvUt7j9yPFL+/dl6mHi8xq/p0a7rjfmjR03iFX9/joPWb6FHgfmKLfGXOR4r/9CjjPzT+DU9qrnemD96tEW84m/bcdD6LfQoMF+xJf6OHI9Vv6NHGXqj8Wt61HC9MX/0qEK84m/HcdD6LfQoMF9REX8njseqn9CjjPzV+DU9qrjemD96VAGzmc/8HQet3zJ9si9GTXuFv9kjHqt+Mn1qpbQxL/yxg3cVv+nTnPa8/9yeFnpj/rk+zeOsifePHTxW/WT6NI3ttdU/dvCerj96lHA+Z96/2SMOqj9Nnzr0v/T+sYPHqp9Mn65iez31jx1c1fVHj1LO59H7N3vEQfWn6VM/+qdz7x87eKL6yfTpNbY39P7NDq7q+qNHKeezVP/YIw6qP02fRtE/6PpjB09UP5k+bcf2JuofO7im648eBc6n4f2bPeKg+tP0KYv+LV1/7OCJ6if0KCGfD9U/dvC+rj961IrxJz3vHzs4qP40fTqif11/7OCJ6if0KSGfj9U/+XcScV39o0dt+lf+1W09Zlrojfnn62HmcX2L948dPFH+1W09TGzvTP1jBzeUf+hRJ/KXKP8ath4zLfQG/yxfDzOP61u8f+zgTPnXsPUwsb1L79/s4IbyDz3qxPgT5V/D1mOmhd6Yf74eZh5fZXr/2MGZ8q9p62FiezfqHzu4qfxDj7ox/kT5hx0ctH7L9Ok2+veUf9jBmfIPPUrJvzv1jx28peuPHvVi/KnyDzs4aP2W6dM9/ev6Ywdnyj/0KCX/HtU/dnBF1x896sf402PvHzs4aP2W6dNT9O/r+mMHZ6qf0KOUfH5R/9jBFV1/9Kgf40/n6p/45/nHTnJ/06fXiAd+/c1uuOX+6FFKPr/LvyXs8ZseDSJOF/IvY/eHz4+Ih379zW546v7oUUo+f8p/6jhR/OjRkHir8hfuyB8+vzjer7/ZDV/JHz7J56X8rxynih89GkUcxF8q3Jc/fP5wvPjrC7/KP+JAPv/J/9VxqvjRo1HEQfylwiP5w+d2xGPxNxLedn/0KJDPu/LfdhwUP3o0Jl7xF4Qz+cPnXsQT8Zc5TlQ/oUeB/K25v9nBLcWPHk2IV/y1hI/kD5/7HC/+jhwnqp/Qo0D+NuQ/ctxW/OhTRrziDz0K5N+p/Mm/LY5X/6eOE/GHHoXIV6Lxi91wR/2jR9OIW8q/jvC5/GP+JQnH6/qdO07EH3rUinwlGr/YDXcUP3o0jbil/OsIX8k/5l/SivhQ1+/KcSL+0KNWzL9E4xe74a7iR48OiVf8dYVv5Q+f3YiPxN+t41T5hx61Yv4lGr/YDfcUP3p0RLziryd8L3/4HHC8+Lt3nCr/0KNWzL9E4xe74b7iR49mxCv++sJP8ofPMceLvyfHqeon9KgV8zfR+MVuuK/40aNZxG3x1xd+lT98TiM+Fn+vjlPVT+hRO+ZvovGL3fBA8aNHxxG3xd9A+EP+8DmL+ET8fThOVT+hR+2Yv8mJ/JeOh4ofPTohXvE3FP6SP3wy3k/E35fjVPUTetRmvKv+xG54pPjRo1PiFX8j4R/3R48Sxvup+PtxHFQ/oUdtxrvqT+yGR4ofPTolXvE3Et6WP3wy3v+Jv23HQfUTetRmvKv+xG54rPjRo3/EK/7Gwnvyh0/G+5n423McVD+hRx3Gu+pP7IYnih89Oou4I/4mwvvyh0/G+5n423ccVD+hRx3Gu+pP7IYzxY8enROv+MuEm/KHT8b7ufhDj85db3J/+ocvjV/To1fXG/xNjy7oT/k3dWx6gz96lLxxvPdv9gvXm9w/4g58afyaHn243pg/enRBvMq/qWPTG/Mn/xYRX/r1M/ul6435o0cd+NL4NT36cr0xf/ToknjF36Fj0xvzh8/viK/EX9dxS/mHHnXJP41f06Mf1xvzR4+uIu6KvyPHpjfmD5+/HC/+Bo5byj/0qEv+afyaHm273pg/enRNvOJv5tj0xvzhc4fjxd/YcUv1O3rURW80fk2P9lxvzB89uiZe8TdzbHqDP3qUVCO+EX9Tx23VT+hRl/zV+DU92ne9MX/06IZ4xd+xY9Mb84fPesS34m/muK36CT3qkr8av6ZHTdcb80ePbolX/J04TlR/okcJ4/1W/J06bqt+Qo+6MX9T1Z/YDZ8qfvRoHnFP/J06TlR/okcp430u/s4dt1U/oUe9mL+p6k/shk8VP3o0j7gn/k4dJ6o/0aOU8X4n/q4ct1U/oUe9mL+p6k/shv8pfvTojnjF3z/HiepP9ChlvN+Lv1vHHdVP6FEv5m+q+hO74TPFjx7dE6/4O3OcqP5Ej1LG+734u3fcUf2EHvVi/qaqP7EbPlf86NED8Yq/c8eJ6k/0KGW8P4i/J8cd1U/oUy/mb6r6Ez1Kp6435o8ePUbcV/5dODa9MX/yj+fFR/X/5rgj/tCjPnxp/GI3fCH+0KPHiPvKvwvHpjfmT/7xvPik67dw3BF/6FEfvjR+sRu+VPzo0RPxKv8uHZvemD/59y/iZ/H37bir/EOP+uSfxi92w1eKHz16Jl7xd+XY9Mb84fOC48Xfr+Ou8g896pN/Gr/YDV8rfvTohXjF37Vj0xvzh89rjhd/O467yj/0qE/+afxiN3yt+NGjl4gH4u/asemN+cPnPOJX8Vd13FX9hB4NYv6mGr/YDd8ofvToNeKB+LtxbHpj/vD5EPGb+Ks77qp+Qo8GMX9TjV/shm8VP3r0Rrzi79ax6Y35w+czx4u/Lcdd1U/o0YDxrvrT9OjV9QZ/06N34hV/c8ep6k/0KGW8vzt/Zn93vcn94Y/xrvrT9OjD9cb80aN34hV/c8ep6k/0KGW8fzh/Zv9wvTF/9GjAeFf9aXr05Xpj/ujRB/GKvzvHqepP9ChlvC/EX9dxT/UTejRkvKv+ND36cb0xf/RoEfFQ/N07TlV/okcp430h/gaOe6qf0KMh4131p+nRtuuN+aNHn8Qr/h4cp6o/0aOU8f4p/saOe6qf0KMh4131p+kR+EHxo0+fxCv+0KOh9AZ/9CjlefFL/R867os/9GgIXxq/pkd11xvzR4++6F/59+g41fwZepTyvPit63fsuC/+0KMhfGn8mh5tud6YP3r0TbzKvyfHpjfmT/5VIl7q+v1z3Bd/6NGI+XONX+yGnxU/erSMeCT+nh2b3ph/5DMwX7EUfxeO+8o/9GjE/LnGL3bDL4ofPfohXvH34tj0xvwjn4H5ih/xd+24r/xDj0bMX2r8Yjf8ovjRox/iFX8vjk1v8EePAvMVv+Jv7nig+gk9GjF/qfGL3fCr4kePfolX/L06Nr0xf/hkvuJP/D04Hqh+Qo9GzF9q/GI3/Kb40aM/4hV/b45Nb8wfPpmv+BN/z44Hqp/QoxHzl3p+xG74XfGjR9usnxB/745T1Z/oUWC8b4u/N8cD1U/o0Zj5S9Wf2A2/K370aJv1E+Lv3XGq+hM9Coz3HfG3cDxQ/YQejZm/VP2J3fCH4kePdohX/H04TlV/okeB8b4r/r4dD1U/oUdjxrvqT+yGF4ofPdolXvG3cJyq/kSPAuN9V/z9Oh6qfkKPxox31Z/YDX8qfvRoj3jF36fjVPUnehQY73vib8fxUPUTejRmvKv+xG74U/GjR3usXxB/n45T1Z/oU2C8V8UfelR1vTH/L1sPE4/X+MVu+Ev9o0dV+lP+fTlONX82zNfDzOP6FvdvOB6Kv29bDxOP1/jFbvhb/KFHNeJV/n07Nr0x/3w9zDy+dHX/iuOR+Fvaeph4vMav6dGb6w3+pkf7xCv+lo6D1m+N8vUw8XitP0gdj5R/S1sPE4/X+DU9WrjemD96VCde8ffjOGj9FnoUmK+oa/1B2/FI+YceTcg/jV/To2/XG/NHj+qsnxB/P46D1m+hR4H5iob46zkeqX5HjzL0RuPX9OjX9cb80aMG6yfE36/joPVb6FFgvqIp/oaOR6qf0KMMvdH4NT3acb0xf/SoSbzi789x0Pot9CgwX9EUfxPHI9VP6FGG3mj8mh5VXW/MHz3aIl7xt+04qP5EjwLjfUv8HToeq35CjzLyV/Wn6VHd9cb80aMt4hV/246D6k/0KDDeK+Lv2PFY9RN6lJG/qj9Nj7Zcb8wfPaoQr/jbcRxUf6JHwe4H+OL/z/FY9ZPpE38n+OKPHbyr+E2fpvHvXnB/7BEH1Z+mT6m15/3n9lGuN7m/8Rv/bql/7OA9XX/0KOHvY+/f7BEH1Z+mT+34dzL3/rGDx6qfTJ/O4t9d9Y8dvKfrjx4lnM9c/WOPOKj+NH3qxb/TqfePHTxR/WT69Bj/Hnj/6NGqCljmemP+6FFK/0vv3+wRB82fTfL1MMs4arx/7OCJ8s/0aTsQtfePHVxT/qFHAT4b3r/ZIw5avzXJ18Ms40X0/rGDJ8q/fVsPE/8+VP/YwfvKP/SoFeNPlH/7th4zFHpj/vl6mNi/8g87eKL827f1MPHvY/WPHVxX/qFH7fh3ovzDDg5av2X6dBL/biv/sIMnyj/0KCH//ql/7OC6rj961I7xJ8o/7OCg9VumT2fx746uP3ZwpvxDjxLy78L7Nzu4oeuPHnVi/MnC+8cODlq/Zfp0Gf/u6vpjB2eqn9CjhHy+Vv/YwU1df/Soy/Wvev/YwUHrt0yfbuhf1x87OFP9hB4l5PNc/WMHb+n6o0e9+Hfa8v6xg4PWb5k+3cW/e7r+2MGZ6if0KCWfH9Q/dvCWrj961Ivxp1P1z3qKiIPqT9Onx/h3X9cfOzhT/YQepeTzs/rHDq7o+qNH/Rh/euX9YwcH1Z+mTy/x74GuP3bDB+6PLSWf37x/sxn2+E2PBhGnr/IXTuUPn+8c79ff7DmWP3ySzwv5jxwnHr/p0ZB4t90/EW7LHz4/Od6vv9kNn8kfPsnnb/mfOU4UP3o0jDiIv0S4J3/4XMa/R+KvJ/zo/uhRIJ9/5f/oOFX86NMo4iD+0KNAPo7kz3jejn+P1f9IWPyhRwG+duW/7Tiof/RoTP/KvyCcyZ/xvBf/nuj6ZY4T8YceBfiqub/ZwS3Fjx5NiFf51xI+kj/5t8/xun5HjhPxhx4F8q8h/5HjtuJHjzLiFX9t4RP5w2eT48XfieNE+YceBfKvIv8zx23Fjx5lEbfEX1v4TP6Rz4S/p+LvzHGi/EOPWjH/Eo1f7IY7ih89mkbcEn8d4Uv5Rz6TEP8+FH+XjhPVT+hRK+ZvovGL3XBX8aNHh8Qr/rrCN/KPfCYdjhd/N44T1U/oUSvmb6Lxi91wT/GjR0fEK/56wnfujx4lfY4Xf3eOU9VP6FEr5m8ylH/PcU/xo0dHxCv+esKP8odPxvtM/D06TlU/oUetmL+J6k/shvuKHz2aEa/46wu/yB8+Ge/H4u/Fcar6CT1qx78T1Z/YDQ8UP3p0HHFb/A2E3+UPn4z3Y/H37jhV/YQetWP+Jqo/sRseKn706IR4xd9Q+FP+8Ml4PxF/n45T1U/oUTvmb6L6E7vhoeJHj06IV/wNhZfujx4ljPdT8bd0HFQ/oUdtxrvqT+yGR4ofPTolXvE3Ev6TP3wy3v+JP/Ton/QGf/SoDV8av9gNj9U/evSP/pR/Y+E9+ZN/t/HvM/W/5ziIP/SoA18av9gNT8QfenQWcUf5NxHelz/5d8/xun77joP4Q4868KXxi91wpvjRo3PiFX+ZcFP+8PnE8eKv6Tgo/9CjDvmn8YvdcKb40aNz4hV/mWPTG/6HHiWv8e8L58/sF6435o8edcg/jV/To3fXG/NHjy6IV/xNHZvemD98fsS/L50/s1+63pg/etRBbzR+TY8+XW/MHz26JF7xd+jY9Mb84fOL450/s1+63uT+8IfeaPyaHi1db8wfPbqKf3fF35Fj0xvzh88fjhd/fcct1U/oURe90fg1PfpzvTF/9Ogq4q74O3KcqP5EjxLG+7X4GzluqX5Cj7qMd9Wfpke7rjfmjx5dE6/4mzlOVH+iRwnj/Ub8ZY7bqp/Qoy75q/rT9KjmemP+6NEN8Yq/Y8eJ6k/0KGG834i/I8dt1U/oUZf8Vf1petRwvTF/9OiWeMXfieNE9Sd6lDDeb8XfieO26if0qEv+qv40Paq43pg/enQbcU/8nThOVH+iRyl/z8XfmeO26if0qBfzN1X9id3wqeJHj+YR98TfqeNE9Sd6lDLe78TfpeO26if0qRfzN1X9iR6lHdcb80eP7uhP+ffPsemN+cf8S3levFf/t4474g896sGXxi92w2fiDz26hy/l35lj0xvzj/mX8rx4r+t377gj/tCjHnxp/GI3fK740aMH4lX+nTs2vTH/mH/pmOPF35PjjvIPPerF/Es1frEbPlf86NFDxH3xd+7Y9Mb84XMa/34Uf6+OO8o/9KhP/mn8Yjd8ofjRo8eI++LvwrHpjfnD5yz+/ST+Phx3lH/oUZ/80/jFbvhS8aNHT8Qr/i4dm96YP3yecrz4+3LcUf2EHvVj/qYav9gNXyl+9OiZeMXflWPTG/6HHqXnHC/+fhx3VT+hR/2Yv6nGL3bDV4ofPXomXvF35dj0xvzh8yr+/SL+th13VT+hR33Gu+pP7IavFT969EK84u/acaL6Ez1KGe+v4m/PcVf1E3o0YLyr/sRu+Ebxo0evEQ/E343jRPUnepQy3l/F377jruon9GjAeFf9id3wreJHj96IV/zdOk5Uf6JHKeP9Tfw1HXdVP6FHA8a76k/shm8VP3r0Rrzi79ZxqvoTPUoZ7+/On9nfXW/MHz0aMN5Vf5oevbvemD969E684m/uOFX9iR6ljPcP58/sH6435o8eDRjvqj9Nj8B3ih99+iBe8YceDaQ35k/+8by4UP9dxz3xhx4N4Uvj1/Tox/XG/NGjRcRD5d+941TzZ+hRyvPiwq+f2ReuN7l/xEP40vg1Pdp2vTF/9OiTeJV/D45Nb8yf/NvheF2/seOe+EOPhuSfxq/p0Z7rjfmjR5/EK/4eHJve8D/0KK3Gv7/E39RxX/mHHg3JP41f06N91xvzR4++iFf8PTo2vTF/+KzHv7/F38xxX/mHHg3JP41f06Om6435o0ffxCv+nhyb3pg/fG5xvPg7ddxX/YQeDZm/1PjFbvhZ8aNHy/j3SPw9Oza9Mf/IZ2C+Yin+zh33VT+hRyPmLzV+sRt+Vvzo0TLikfh7dmx6Y/7MBzNf8SP+rhz3VT+hRyPmL/X8iN3wi+JHj36IV/y9OE5Vf6JHgfH+K/5uHQ9UP6FHI+YvVX9iN/yq+NGjX+IVf6+OU9Wf6FFgvP+Kv3vHA9VP6NGI+UvVn9gNvyl+9OiPeMXfm+NU9Sd6FBjvf+LvyfFA9RN6NGL+UvUndsNvih89+mP9hPh7c5yq/kSPAuN9W/y9Oh6ofkKPxsxfqv7Ebvhd8aNH26yfEH/vjlPVn+hRYLzviL8PxwPVT+jRmPlL1Z/YDX8ofvRoh3jF34fjVPUn+hQY7zviDz3adb0xf/RoDF8av9gNL9Q/erRLf8q/heNU82fDfD1MPF79/zoeir+FrYeJx2v8Yjf8Kf7Qoz3iVf59Oja9Mf98PUw8Xtdvx/FQ/H3aeph4vMYvdsOfih892mP9hPj7dGx6Y/75ephlXN/i/lXHQ+Xfl62Hicdr/GI3/KX40aMq6yfE35dj0xvzh0/mK2rir+54qPxDjybkn8YvdsPfih89qhGv+Pt2bHpj/vDJfEVN/G05Hqp+R48m6I3Gr+nRq+sN/zM92ide8bd0HLR+Cz0KzFfsa/1B4nik+gk9mqA3Gr+mRx+uN+aPHu0Tr/hbOg5av4UeBeYr6lp/0HI8Uv2EHk3QG41f06Mv1xvzR4/qxCv+fhwH1Z/oUWC8N8Rf1/FI9RN6lDHeVX+aHv243pg/etRg/YT4+3UcVH+iR4Hx3hB/A8cj1U/oUcZ4V/1perTtemP+6FGTeMXfn+Og+hM9Coz3pvgbOx6pfkKPMsa76k/Toz3XG/NHj5rEK/7+HAfVn+hRYLxvib+p47HqJ/QoI39Vf5oe7bvemD96tEW84m/bcVD9iR4FxntF/M0cj1U/oUcZ+av60/So6Xpj/uhRhXjF347joPoTPQqM94r4O3U8Vv2EPmXkMx8lNX/4TSLeVf+mT9P4kbVeq/A3e8SmN+Zv62GsvWnhn9unud7k/hYfH+FU/9jBe+IPPbKPxB97/2aPOGj9lulTO/onc+8fO3gs/kyfzmJ7XfWPHbwn/tCjhPOZq39bj9kq9Ib/mT7xEcF06v1jB0+Uf6ZPj7G9gfdvdnBV+YcepZzPwvs3e8RB67dMn4b2kTvvHzt4ovwzfeIjd2P1jx1c0/VHjwLnU/X+zR5x0Pot06cJ/ev6YwdPlH+mTw0+gqr+sYP3df3RI/tR+5b3jx0ctH7L9Okw+rd0/bGDJ6qf0KOEfJ6pf+zgfV1/9Ki15COs6p/1FPyooNZvmT7xEce2rj928ET1E3qUkM+n6h87uK7rjx61Y/zJlfePHRy0fsv06Z99BNb7xw7OVD+hR/Yjy+fev9nBDV1/9KgT409evX/s4KD60/Tpgv51/bGDM9VP6FFCPl+pf+zgpq4/esSPoCTb3j92cFD9afp0Hf27uv7YwZnqJ/QoIZ9v1T92cFPXHz3qLvkIrvpnPQU/6qj60/SJj2j2dP2xgzPVT+hRSj7fq3/s4C1df/SoF+NPR94/dnBQ/Wn69GAf4fX+sYMz1U/okf3I9ZP6xw6u6PqjR/0Yf3rm/WMHB9Wfpk/P9K/rjx2cqX5Cj1Ly+VX9Yzfs8Zs+8SM06aP3jx6l5GPq/qZP7xzv/Zs9x/LnepJ/C/mPHCfqv2UfJY7HK/8S4bb8Gc+fHO/5Z3bD4g89Ssm/b/mfOU4UP3o0jDgo/xLhnvwZz3zEd+T5Z3bD4g89CuTfr/wfHaeKHz0aRRzEXyo8lD98/kU8Fn9D4aX7B/soc8Q78l86DoofPRoTr/gLwhP5w+cux4u/iXBD/vBH/lXl33DcUvxT+yh0PF78tYQP3d/0qcbx4u/QcaL6CT0K5G9d/j3HLcWPHk2IV/y1hI/lD598RDkTf8eOE9VP6FEgf7fkf+y4rfjRo4x4xV9b+J/84bMS8VT8/XOcqH7q2Eex40eME/nPHXcUP3o0jbgl/jrCF/KPfCaM96n4u3CcqH5Cj1oxfxPVn9gNdxX/lX2UOx4v/rrC1/KPfCaM90Pxd+04Uf2EHrVi/iaqP7Eb7ip+9OiQeMVfV3ju/uhRwng/En9zx6nqJ/SoFfM3Uf2J3XBP8aNHR8Qr/nrCD/KHT8b7TPw9OE5VP/Xto+TxeNWf2A33FT96NCNe8dcXfpY/fDLeZ+Lv2XGq+gk9asX8TVR/Yjc8UPzoET+C0BZ/A+E3+cMn4/1Y/KFHx9Ib84+4DV8av9gND9U/esSPKLSVf0PhT/mTfyccr/4/HafiDz1qw5fGL3bDQ/GHHp0Qr/JvKLx0f/Qo4UceTnX9lo6D+EOP2vCl8Yvd8Ejxo0enxCv+RsJ/8ofPy4j/ib8/x0H5N7aP0sfjNX6xGx4rfvToH/GKv7Hwrvzh84bjxd+u46D8Q4/a5J/GL3bDE8WPHvEjFh3xNxGuyR8+7zhe/NUcB9Xv6FEn3j8TjV/shieKHz06i7gj/ibCDfnDJz+icS7+Go6D6if0qBPzN9H4xW44U/zo0Tnxir9MuCJ/+HyJ+EL8VRy3VD9N7UcB4vEav6ZHb643+JseXRCv+Js6TlR/okcJ4/3C+TP7hetN7g9/jHfVn6ZHC9cb80eP+BGRjvg7dJyo/kSPEsb7pfNn9kvXm9wf/hjvqj9Nj75db8wfPbqMuCv+Dh0nqj/Ro4TxfiX+eo5bqp/Qoy7jXfWn6dGv6435o0dXEXfF35HjRPUnepQw3q/F39BxS/XTzH6UIR6v+tP0aMf1xvzRo2viFX8zx4nqT/QoYbxfi7+J45bqJ/Soy3hX/Wl6VHW9MX/0iB9x6Yq/Y8eJ6k/0KGG834i/Q8dt1U/oU5f8Vf1petRwvTF/9Igfwe4q/04cm96YP/nX5Hj1f+K4Lf7Qoy58afyaHlVcb8wfPbqNuKf8O3FsemP+/KgDz4tzXb8zx23xhx71Il+pxi92w6eKHz2aR9xT/p06Nr0x/5h/aYj4TvxdOm4r//7Zj2rE4zV+sRv+p/jRozviFX//HJvemH/kM+1wvPi7cdxW/qFHvZh/qcYvdsNnih894kfIe+LvzLHpDf7oUdrnePF357ij/EOPejH/Uo1f7IbPFD96dE+84u/MsemN+cMnP3L1IP4eHXdUP6FHvZi/qcYvdsPnih89eiBe8Xfu2PTG/OEzi/hR/L047qh+urAfNYnHa/xiN3yh+NGjx4j74u/CsemN+cPnEceLv3fHHdVP6FE/5m+q+hO74UvFjx7xI/B98XfpOFH9iR6ljPcn8ffpuKP6CT3qx/xNVX9iN3yp+NGjJ+IVf5eOE9Wf6FHKeH8Wf0vHXdVP6FGf8a76E7vhK8WPHj0Tr/i7cpyo/kSPUsb7i/j7c9xV/XRtPyoTj1f9id3wteJHj16IV/xdO05Uf6JHKeP9RfztOu6qfkKP+ox31Z/YDd8ofvToNeKB+LtxnKj+RI9Sxvur+Ks57qp+Qo8GjHfVn9gN3yh+9Ok14oH4Q48G0hvzJ/94XnxT/03HXfGHHg3gS+MXu+Fb9Y8evdG/8u/Wcar5M/Qo5Xnx3a+f2d9db8wfPRrAl8av6dG76435o0fvxKv8mzs2vTF/8u8j4g+/fmb/cL0xf/RoQP5p/JoefbremD969EG84u/OsemN+cPnF8c7f2b/cL3J/eGP/NP4NT1aut6YP3rEj+AOxd+9Y9Mb84fPH44Xf33HPeUfejQk/zR+TY/+XG/MHz1aRDwUf/eOTW/MHz75EapP8Tdy3FP9hB4N0RuNX9OjXdcb80ePPolX/D04Nr0xf/jci/hL/GWO+6qf0KMh+avxa3pUc70xf/Toi3jF36Nj0xvzh899jhd/R477qp/QoyH5q+dH06OG6435o0f8CPFQ/D05TlV/okcp4/1b/J047qt+Qo+G5K/qT9OjiuuN+aNH3xGPxN+T41T1J3oUGO9L8XfmuK/6CT0aMX+p+hO74WfFjx4tIx6Jv2fHqepP9Cgw3n/E36Xjvuon9GjE/KXqT+yGXxQ/evRDvOLvxXGq+hM9Coz3H/F347iv+gk9GjF/qfoTu+FXxY8e8SPQI/H36jhV/YkeBcb7r/i7czxQ/YQejZi/VP2J3fCr4kePfolX/L06TlV/ok+B8f4n/tCjP9cb80ePRvCl8Yvd8Jv6R4/+WD+h/HtznGr+DD0KPC9uq/9XxwPxhx6N4UvjF7vhd/GHHm2zfkL59+7Y9Mb8yT/mK3Z0/T4cD8QfejSGL41f7IY/FD96tEO84u/DsemN+cMn8xU74u/L8UD5hx6NyT+NX+yGF4ofPeJHGMfib+HY9AZ/9CgwX7Er/n4cD5V/6NGY/NP4xW54ofjRo13iFX8Lx6Y35g+fzFfsib9tx0PV7+jRmPlzjV/shj8VP3q0R7zi79Ox6Y35wyfzFVXxt+d4qPoJPZowf67xi93wl+JHj6qsnxB/X45Nb8wfPpmvqIq/fcdD1U/o0YT5c41f7Ia/FT96xI9gTsTft+NU9Sd6FBjvNfHXdDxU/YQeTRjvqj+xG/5W/OhRjXjF37fjoPoTPQqM932tPzhwPFL9hB5NGO+qP02P3l1vzB892ide8bd0HFR/okeB8V7X+oPgeKT6CT2aMN5Vf5oefbremD96VCde8ffjOKj+RI8C472u9QcdxyPVT+jRhPGu+tP0aOl6Y/7oET9Cmom/X8dB9Sd6FBjvDfHXdzxS/YQeZYx31Z+mR3+uN+aPHjVYPyH+fh0H1Z/oUWC8N8XfyPFI9RP6lDHeVX+aHu253pg/etSkP+Xfn+Og+bOxrYeJeEv9Tx2Pxd+2rYeJx2v8mh7tu96YP3q0BV/Kv23HQeu3xrYeJuKKrt/M8Vj87dh6mHi8xq/pUdP1xvzRowrxKv92HAet3xrbehiOF3+njsfKvx1bDxNx/IWk3P/K8a7iN306OF79r9Uu/M0ecdD6LdOnhPbmx4W/2SMeK/9Mn3q0p/7NTv+KP9en2N5U/WOPOGj9lulTK/onU+8fO3is/DN9Oo7tddQ/dvCerj96lHA+V96/2SMOWr9l+tSN/umB948dPFH9ZPo0j+31vX+zg6u6/uhRyvm8ev9mjzho/Zbp04D+l94/dvBE9ZPp0yK2N1L/2ME1Xf/8R+tje9vev9kjDlq/Zfo0jv5B1x87eKL6yfSpGtvL1D92cE3XHz0KMf7kQP2zniLioPrT9Gka/Vu6/tjBE9VP6FFCPh+pf+zgfV1/9KgV409G3j92cFD9afo0i/5tXX/s4InqJ/QoIZ9P1D92cF3XHz1qx/iTM+8fOzio/jR9OqV/XX/s4InqJ/QoIZ/P1D92cEPXHz3qxPiTR+8fOzio/jR9Oo/+HV1/7OBM9RN6lJDPl96/2cENXX/0qMP1X6p/4o84qP40fbqK/l1df+zgTPUTepSQzzfqHzu4qeuPPnW5/g3vv2nrMduF3ph/vh5mGl9lef/YwZnyb8vWw8T27tU/dvCW+kePerH/VPm3Zesx24XemH++HmYaX6V4/9jBmfKvYuthYntP6h87uKL8Q4/6Mf5U+Vex9ZjtQm/MP18PE/tX/mEHZ8q/iq2Hie29qn/shj1+06NBxKny70A4cX/TpzeO9/wzu+Ge/OGT/PuQf6+E3R89GkScKv8OhFvyh89FxEO//mY3fOz+6FFK/n3J/9hxovjRoyHxKv8S4a784fM74pH46wrP3R89CtxPf+Q/d5wqfvRoFHEQf6nwQP7w+cvx4m8gvJB/xIF83pb/wnFQ/OjRmHjFXxAeyx8+dzhe/I2Fq/KPOJDPe/KvOg6KHz0aE6/4C8JT9zd9qkY8EX9Tx4nqJ/QokL/78m85bil+9GhCvOKvJTyTP3zWI87E38xxovoJPQrkb1P+U8dtxY8eZcQr/trCp/KHzy2OF3+njhPVT+hRiPmbqP7Ebrij+NGjacQt8dcRPpd/5DNhvE/F37njRPUTetSK+Zuo/sRuuKP40aNpxC3x1xG+kn/kM2G8H4q/K8eJ6if0qBXzN1H9id1wV/GjR4fEK/66wrfyh0/G+5H4Q4+OuN+IP/SoBV8av9gN99Q/enREf8q/nvCD/GP+JcOIZ+r/wXEq/tCjFnxp/GI33Bd/6NGMeJV/feFn+cf8SyYcr+v37DgVf+hRC740frEbHih+9Og44rb4Gwi/yR8+Dzle/L05TpV/6FGb/NP4xW54oPjRo+OI2+JvILyQP3weR3wi/haOU+UfetQm/zR+sRseKn706IR4xd9Q+Fv+8Pkv4lPx9+04qH5Hj9rx/plo/GI3PFL86NEp8Yq/kfCv/OHzguPF36/joPoJPWrH/E00frEbHit+9Ogf8Yq/sfCO/OHzmuPF347joPoJPWrH/E00frEbHit+9OhfxB3xNxauyh8+Ge9n4q/qOKh+Qo86jHfVn9gNTxQ/enQWcUf8TYTr8odPxvu5+Ks7Dqqf0KMO4131J3bDmeJHj86JV/xlwlvyh0/G+7n423IcVD+hRx3Gu+pP06NX1xv8TY8uiFf8TR0nqj/Ro4TxfuH8mf3C9Sb3hz/Gu+pP06MP1xvzR48uiFf8TR0nqj/Ro4Txfun8mf3S9cb80aMO4131p+nRl+uN+aNHl8Qr/g4dJ6o/0aOE8X4l/rqOW6qf0Kcu4131p+nRr+uN+aNHVxF3lX9Hjk1vzJ/8+4v4Wv0PHbfEH3rUhS+NX9OjHdcb80ePriPuKv9mjk1vzJ/82+V4Xb+J45b4Q4+68KXxa3pUdb0xf/TohniVf8eOTW/wR4+SGseLv0PHbeUfetQl/zR+TY/qrjfmjx7dEK/4O/6/lH1Z07s67N5X6fxv6YzZl870IixZIXESst50spKQhQCBAJ1+92IZ8HtO22l7dY5+jmPlQdYjSzJvJ1O+ofMBT53IV4bfqpP7zP6Aj4Zgf2z/Uj7iOr6h84GPrqAvw2/dyZRv6HzAExE5YPhtO7nP7A/4aETsz2L7F8apvGH6Ax8FRB4x/DadTPmGzid4WhZ8nuF36OQ+i5+Aj0bEfi22f2GcylumP/DRDfRl+G07mfINnU/wtPrweYbfuZP7LH4CPhoR+7XY/oVxKm+Z/sBHN9CX4bftZMo3MB/4yBoR+c7wCzp5wOIn4KMRsV+LxZ8wTuUd0x/46A76Mvx2nWyy+BP4yIL9HjL8wk4esPgJ+GhE7Ndi8SeMU3nP9Ac+CkFfht++k00WfwIfWbDfQ4bfq5MHLH4CPhoR+7VY/AnjVD4w/YGPHkQeM/wOnWyy+BP4yIL9/mD4fTp5wOIn4KMxsV+LxZ8wTuUD0x/46EHkMcPv0Mkmiz+BjyzY70+GX9rJAxY/AR+Nif1aLP6EcSofmf7AR0/Ql+F37GSTxZ/ARxbs9xfDL+/kIYufgI/GsN9Z/AnjVD4x/YGfXqAvww/4aMz4hs4H+4Pz4putX3XykOEHfDQGvNj+hXEqn9n6wEdvWJ/Z37mTKd/Q+WB/cF58s+cndvKQ4Qd8NAa82P6FcSpfmP7ARxGRJ8z+Lp1M+YbOB/u7wefZ81M6ecjwAz6agP2x/QvjVL4w/YGPIiJPGH6XTqZ8Q+cDng8ifxh+eicPmf0BH03A/tj+hXEqX5n+wEcf0Jfhd+1kyjd0PuD5JnLM8EOdPGL2B3w0Aftj+5fy0afjG5hP+SgGfRl+QSdTvqHzAc8YPt/hR8fjjm+a+YAf8A3bv5SP0o5v6HzgowT0ZfjdOpnyDZ0PeH7h8x1+dDzp+KaZD/gB37D9S/ko7/iGzgc+SojsMvxunUz5hs4HPH9EThl+o04esfgJ+MgFvmHnR8pHZcc3dD7wUUpkl+F372SLxZ/ARxbs9y/Dz+3kEYufgI9c2O8s/qR8JHR8Q+cDH31BX4Zf2MkWiz+BjyzY71+G36yTRyx+Aj5yYb+z+JPykdzxDZ0PfJSBvgy/RydbLP4EPrJgv2cMv0Unj1n8BHzkgv2y+JPykdbxDZ0PfJSBvgy/RydbLP4EPrJgv+cMv1Unj1n8BHzkgv2y+JPyEdfxDZ0PfJSDvgy/ZydbLP4EPrJgv/8YfttOHrP4CfjIg/oRiz9hnMovpj/w0Y/IHsPv1ckWiz+Bn2zY7z+GH/BR0fENnQ985EH9nO1fGKfym60PfFTAesz+3p1ssfoZ8JEN58WCrX/p5DHDD/jIg/o5278wTuWI4Qd8VIK+zP6iTqZ8A/OBj2yoV5Ts+d06ecLwAz7yAC+2f2GcyhHTH/ioBH0ZflEnU76h8wFPqFdUDL9HJ0+Y/QEfeVA/Z/sXxqn8YfoDH1WgL8Pv08mUb+h8wBPqFTzD793JE2Z/wEdTsD+2f2GcyjHTH/iIh/4Jhl/cyZRv6HzAE+oVPMMv7uQJi9+Bj6ZQv2T7F8apnDD9gY8E0Jfhl3Qy5Rs6H/CEeoXA8Pt28oTFT8BHU6hfsv0L41ROmP7ARwLoy/BLOpnyDcwHPrKhXiEy/H6d7LL4CfhoCvVztn9hnMop0x/4SAR9GX5pJ1ss/gQ+smG/Swy/qpNdFj8BH01hv7P4E8ap/GX6Ax9JoC/D79vJFos/gY9s2O8Sw0/sZJfFT8BHU9jvLP6EcSpnTH/gIxn6Jxh+WSdbLP4EPrJhv8sMP6WTXRY/AR/NYL+z+BPGqZwx/YGPZOifYPhlnWyx+BP4yIb9rjD89E52WfwEfDSD/c7iTxincs70Bz5SQF+GX97JFos/gY9s2O8qww91ssfiJ+CjGex3Fn9SPvp0fAPzKR+poC/D79fJNos/gY9s2O8qq/9bneyx+An4aQb7ncWflI++Hd/Q+cBHGqzH7K/oZJvVz7ymH4Z8nq0/6GSP4VfQfhjyebZ/KR/9Or6h84GPdOifYPZXdrLN+re8ph+GfJ71v4w72WP4lbQfhnye7V/KR1XHN3Q+8JEO/RPM/spOtln/ltf0w8xJf0s33+tkj9lfRfthyOfZ/qV8JHZ8Q+cDHxmgL8Ov6mSb9W8BH9lQr+AYfriTp8z+gI8w2B/bv5SPlI5v6HzgIw70ZfjxnWyz/i3gIxvqFRzDb9nJU2Z/wEcY7I/tX8pHesc3dD7wEQJ9GX5CJ9usfwv4yIZ6BWL4rTt5yuIn4CMM9sv2L+Uj1PENnQ98hEDuDbr5nWyz/i3KTz0T/l75rp1Px4k8ZfET5SeHfJ9ldn8/HMZBFpn+lJ8C+L5u/WZ80PINnU/5yYS/V96tD+MgT1n8RPlpTr6vz9aHcZAl9vyBj0z4PbtufTpOZJvFn5SfBrD+r1sfxkGesviJ8tOJfN+IrQ/jIMvs+QMfWfB7Ht36dJzINos/KT+NyXwr6NaHcZBnLH6i/BSR73O79ek4yDJ7/sBHFvyeH1sfxolss/iT8pNnwt8779aHcZBnLH6i/MST75ux9WEcZIU9f+AjG36P3q1Px4lss/iT8hM24e+dd+vDOMgzFj8BH5lgzwu2PoyDrLLnD3zkEP3NUbc+jINss/iT8tMS1mfPH8ZBnrH4CfjIBHtesfVhHGSNPX/gpz7R31x162u0H3PQ8g2d3/TDwN8r79aHcZBnzP402g9Dvm/H1odxkHW2PvDRANZn9qfTfsxByzcwHzf9MPD3yrv1YRxkzOxPp/0w5PuO3fp0HGSd2R/w0YDobzL702k/5qDlGzq/6YeBv1ferQ/jIGNmfwbthyHfd2HrwzjIBrM/4KMh0d9k9mfQfsxByzd0ftMPA3+vvFsfxkHGzP6AjyywvxtbH8ZB5pj9AR+NiP4Wsz8YB9lm/VuUn+6wPnv+MA4yZvYHfGSB/T3Y+jAOMmLPH/hoTPS3mP3BOMg269+i/PQ04e+Vd+vDOMiYxU/ARxb40zdbH8ZBRuz5Ax+Nif5WwNYH/UHudfMpP0VEnnTPn45T2enmAx9ZYM8xm+8wudOf8tGEyFbK5v+R2XzAMyGy2z1/Ok7leTcf+MgCe/6y+fNONpn+wEcu6Cuz+UwesPmAZwaf754/Hafyic0HPMGef2z+qZMtpj/wkUdkm+FnMXnM5gOeBXye4TdmcsTmE9kGe67Y/KiTLaY/8JFHZJvhZzHZY/MBT57IU4afx2S+mw98ZIM9i2w+38k20x/4aAr6MvxsJmM2H/CUiDxj+OFONln8BHxkg/0q3Xw6DrLD9Ac+moG+DD+HyUs2H/BU4fMMv2Unmyx+Aj6ywX51Nt/r5D7TH/gIg74Mvz6T12w+4GnA5xl+wEcY1mf4AR/ZBC+T7V8Yp/KArQ98NCeyw+xvwOQ9m0/szzTh82z9fSebDD/gI4fgZbL9C+NUHjD8gI/mRHaY/Q2YfGLzif2ZDpEX7PmdOtlk+AEfOQQvk+1fGKfykOkPfLQAfRl+QyZf2XzAc0jkJcPv2skWsz/gI4fYn8n2L4xTecT0Bz5agr4MvxGT72w+4DmBzzP87p1sMfsDPnKI/Zls/8I4lcdMf+AjH/Rl+I2Z/GTzAc8pfJ7h9+xki8XvwEcO8Z8m278wTuUx0x/4yCdyn+E3ZnLE5gOecyKvGH5RJ1ssfgI+6hP7Ndn+hXEqT5j+wEcrIvcZfhMmJ2w+4OkTec3wSzrZYvET8FGf2K/J9i+MU9ll+gMfrUFfhp/L5IzNBzxhv68ZflknWyx+Aj7qw35n8SeMU9lj+gMfbUBfhp/H5KKbD3xkwn7fMPyKTrZZ/AR81If9zuJPGKeyx/QHPtqAvgw/j8k8mw94wn7fMvz4TrZZ/AR81If9zuJPGKfylOkPfLQFfRl+UyZLbD7gCft9x/CTOtlm8RPw0QD2O4s/YZzKM6Y/8NGOyAOG34zJKpsPeMJ+3zH81E62WfwEfDSA/c7iTxinMmb6Ax/tQV+GH2ayweYDnrDf9ww/o5NtFj8BPw1gv7P4k/JR1PENzKd8dID1mP3NO5nyDcwHPjI/8PlufTp+6PimmQ/rA15s/1I+Sjq+ofOBjw6AF7O/eSdTvqHzwf5SIh+750fHjx3f0PnARwPAi+1fykdZxzd0PvDREfRl9rfoZMo3dD7YX07kE8Nv2MkOsz/goyHYH9u/lI+Kjm/ofOCjE5GHDL9lJ1O+ofMBzxI+z/CbdLLD7A/4aAj2x/Yv5SO+4xs6H/joDPoy/PxOpnxD5wOeAnye4TftZIfZH/DREOyP7V/KR1LHN3Q+8NEZ9GX4+Z1M+QbmAx+ZMpEvDL95J/dZ/AR8NAT7ZfuX8pHa8Q2dD3x0AX0ZfqtOpnxD5wOeGpGvDD+/k/ssfgI+GoL9sv1L+cjo+IbOBz66gr4Mv3UnU76h8wFPDj7P8Nt0cp/FT8BHQ2K/Fos/YZzKG6Y/8FFA5BHDb9PJJos/gY8s2O8Bw2/fyX0WPwEfjYj9Wiz+hHEqb5j+wEcBkUcMv00nmyz+BD6yYL/fGH6nTu6z+An4aETs12LxJ4xTecv0Bz66gb4Mv20nmyz+BD6yYL/fGX7XTh6w+An4aETs12LxJ4xTecf0Bz66g74Mv10nmyz+BD6yYL/fGX73Th6w+An4aETs12LxJ4xTec/0Bz4KQV+G376TTRZ/Ah9ZsN9Dht+zkwcsfgI+GhH7tVj8CeNU3jP9gZ9CIo8Bv//6H//jP/+n/7jyl+tVFjXxqGpnTZMuR16QjgdZNHhZ1kXlvwmqLpz/47/8p//+H7dDeqv/5/91Sv3l3/JzITP6z+jwVeVekhxK8u/590H+uf6Pffgeun8+Re/v5f0lQ8F8mi6fL97zFU1avb/ua2bOfA8dZqWyO0XIer7epZYXhY2MIAqHGRo+sJKP5d/kEc16SFDnA3N0krRKz/TB/HlIwwgLj3eYlpp++aGTM0v2UagqoX70K/j7YQHmrdKPlou98UBvZfNL7/0ys423tEab5eBeYWE3v8xGF1t5IGc2zfNZ8nhUaNDPi+PdrDa4GP60y/UQnh0kjMT3Rem/VZ9ztuVgOLrnDyyY28c1R4jTEVdNXnTccM1YtR6b6wdzI+e7+90n3BzFv7t5HAUnIdQ/vSAXxEha4GLO7avgStZHV/Xdm+VkfeH5Fcej7UTGclUax/Xo4NY4+eneuCIkVdxFGI3F+vv3GGXi6XSB3+/11eMOfj8ah5o3uFXXHBvKNCjPcTkL0C8Nx1WNr2Kj8XnbG38mNT6c/L7N7yrBv/o6J/FI8Nf333cx2RznBX4/HHT5PIfWj1tx00Rzn7cy5Ax9X43q5+NgpOY3naw/R0r/y5nqMpBtI42CuQPry340twpYPxMyr3LzQrO5wT44f+84eWHR+szo36tEP7+/vdf4ib7BTbVZCb+fl3u/7xd+/00Wh/JNxi/fuGGsmWq29LGoWKifjYbTHSrSaqJN4ynvo9NuctYAf/Xjf2WK/6Z/WXAIIa1C+1vM0ecj7UdJNFnsuQcarkz3cR8liq2f56N+Cs+/9JcKetPnr68PM1Ov10d4encGKkfWX7ni7nE7JQGS5PEy429RWhnj4PYW6qWuWOxV4VAE+0utfVVo5Pcj+YmeEfx+5Tj5zg/DaW0/t9/+7r6SkWqj+nnkGOxfWDp2TwX7n1l9o3f81Pgbo132vgD+qvo+8J/913a4546/VIP52bXV8WyWqMvtY4fvG0fRJ8IsGaH1w7nkSV5UNloPP0LR+7hrjOTBMk8QMjw0WSztnRycipDLbkgX5twywFqtFT8wViMHWa+tIsevWR7q6u9SvcF+ZePGrSn+iY2HDf5c/aSVeKdyWBl+incJ88XHcTRY4Udk6xt9a2xrfAcYoW2OjrC+cFlUp99kKfhc7OzwuP59Hi78YHU+gP7i21ydI0Ea2Mo4k7X5149LLC+j4jvT9NMPTUPcC+ZkPjqM+doj1ocmrGacvV2Oht4OBaPDY/6A9ee3wXqWry0Tl6NEyF+fMJGR89nNjDPR3+jp0ZQ+f8Wx1Ct9/qudGDmTuP79XG+gzcYwn0um+3JvK5MeilNnfBgR/PSYO0K9dYX1uf+UPevxXaHF4RRLGsGfu9rID0F/LcqL8DO5LALOU/pPXYoNsRKsSa6O+MmNx+p2hqqqt//0UF+SrE/tv142ci5iqN7UeIN/um5w0cWWH2g9fwyOyemxq+rno3w/H8WwsTG77Zrn91xNY+rf9MFLRPdhwO2xWDqqPoyVsYfE73jlJ8I88PXP/nJPYb6wNGxelcYDDxWL201ayPju65mzkURYXy94+RDcTp8AfZOPNtfN18PmetnvfgX9ldPSw+Ug+6bGhHv0+KkVnW1OzNYf4Tc4zHF5muTXCOb31gtrObrHz9AY3oK1OfxxY1yE/V/Mwfrv8OQkh8t1Xuk4/Y51wJ/H9+RnJ0T/xXcdN/vz0r+ndH+qNp+NXvD7L+M01SdkPsqVmMPfchHi6uwufe9K8Jteg8sC1tfDj5rotf/ZYWX1S7c54M+7vq+cif7GaHueXy6adMRrS1lrm7eDU0P6cuPdy3s/6+frLxyfJE2wOs0VfNRE94TSl/K91fbxDdHbCDZa/f01f6230l4bJ6MR+kbelD4/Iz72PBH2H3er9tM9+L/NOBYb/povdP8I/FX/hs9qDPbnLDgp0WD+N9TGqDyUFuZOk1Llb6c4QNM4XgSwvp7erprMGXcJF7ZxuD1I0get/MC/DEB/wfJTj+9VGj6qd8kxw7EyR9Z089DrDZtUyNj2jhjm69rrvOZGQ3eHhDn/ftT6xTZ3+Khrun6hqX2H2k+1rPp0/xv4ii0B9C/mrtD4T70SMPWfKJNf8xf8fnV8jGX6/Kf1+W3nvdIgRIbBLR3ATzO9h5DB+om12vHXfPetkG09cqMm5Rz/+u5mvQf9N2+19uCnOVfx4vJyfF+Ppxv+7LQd2ii7Y4SUxzKuav8o2BwXxcGs5q8rLl7zOpzR9PMPScX9Z9VBFm8bpjRvnh+anlBG998+Ex3qP43P6mk5wM9qb4vnlN9m46LhN26/6n158H/VRdScHtjf4aIXFD/El/R1KHOsD+XVeQDr96fCwdmdJKnSS/GCNrV/XGHl/botBirR395s+qv+ix/5yrtnuu9sM1AxV8yWp9o/nueoz6kF2u3IfL6YoEE9/4TLfYTqZ4p0HWXJjEuWZH1j64nVufYPY1zv/mB9Af8pYMs6f4CfX1fzSvlNn+7ub8pvweIxov6PU75OUNH9W42U3Rzsb3FSr1odKlWV7jif0IT1f+Z+P7eGU0NHM8HCJP4T6vjE9R07e9X6C9vbwlCMb+lw6+XskOargWdrPmeKg+pcDbExlDX9VccfIzTXzGk4OOl1fPGJFWRN7lpc2/eofX6LapzR/cf5441G/Sd/F1v+G49fTfzIXQc7zav1S7GcY/Mljrhshwaup1uk/8XWL3trP7pP6vnaPL4+qf3L/Qlf1PYj2frj8htwsL62VjNZeePZD6Xy+cPX8ZUc6ng1Uq+gv+L08mjm9aoel8vbJDhJp6LilqNPSJ7PHfOpvdqdYb6tuChfRpkacunLkRaAHzpNbrIF6zt5XzSiS+2/UOp7WKyfj4+NvvneTED/SfUtafzBneLrjcYfqjCm/ZJzdFnyd+r/uGeqTOv4txRxldtPndpf7vbvGV3/fdat8b6qUixyGydVAf/V0jy8dKI/cqaFwhvz3Q+Hj97Y5NSl73BiulT30kwvfbSI+3b/ttEemFP4TX4UTpyMzKI/o8/P4PaHB91/sikqOfWfo/t7RPlPx1OliV+UV7C31hBfPrxfn54PDC2o9BTOB/xCyWQH+Pf+me05mG/03Z9G/Re3yb9XcUDszzHOi5tI1keXvD4NGVucYfHibXWxf8zmCJv8J6D6xxf7jI/8qsCf29h4Pavce6BqvfMLeZ3V8aPAo5DOl9JB+/yL6Lal+9e46rlL/a+uZ6pK+XMcK3IT/wxOo90b9C+muI0Pk7dyo/GhPjXGBxt+P389pNcfzDevpy31f/pq95r3AL8KJ7n8hPUPu82WQ+tKC/URKl0M+IvKeCevQf8Nfw9G7mfs+RoOvM8s8L03vn3vRrC+Lrw6/j3O1/H4GYsVd54LQ/r8tOFQ9+j+u0qzhPpP4xvfPMp/1WTcqyTgz/zqnGn8yb03F8+C84d+L9rzw0s76/T8oIuppvkQ//HfCZa2MP8wmXwp/+hDfZXY4P8K5D71A6wv7kYLij+H0mxZvjZJTVKOu13cQH9jwF+H2S8dV+rl7EXelvfr+C1I1ONkf/imaL7f36odmY9SPEgWsP+EItXAf8yR09uHA9L/XPuXy6hvQ/yiTIb39AXxpzkXKusA+r8PbfzOLeQ2fp9XQULjP2O6ON40mM/f9eVVQkjVkd17zaj/48T3bTiD9fViOko5sL+fUsr5HvDnNmHvCvrLVqz3L6ZXnrjdOubNXaDavrp47soN53JPrIxuXI8+v80v4+j+M7jZfUD9Z/WI9w3/XZclpvEL0stgNob4TUZGe36wDM37JeR8aEwM8Zpmm+EEa8feU7/Xv3+OPCMb8O5KTUMUSd8rhvmCYEo8jR/Cw2iuaM9bVvsndLmYsL683OoDun9851h7BcP5+Ujhjpcv6C8s9/bJ4b05z3lj20yud/TxdUd7OTv4/ZV3caMpzBeN1/xN/U85Ga0WwB/iXbCGN1h/u01nb4PEb/ruvj5uIP42xGRovEB/P3wNLJecr9DrmiX0fGUEknGZwe/flPhK4z99+16fN7vyUsf33scNZsC/2Lltqf8znsuB1T9p1Rj/Mtm9GYD/LNWSAhP9UeX0r9zdjqb4edjdx6vDZ+vpe8U6nYueZvsy9+pn+201f+NUe8T+QF2GDvfayvnT/o08W81Wt8NidXzc6iD24Bm9oA/xZyXmem6KFT9NJTTjh/wOv66V8x4bo/LH4RnWdqdMrve3v5QD6br0Uvx8Ph+Cu7jW54+nu6tij8yXetl8nI3M2hH4B2u5MxbZ0uFml4WXhGR9uV8Ti5U9ogInd6PmkOvUOnHpep0GN6K/WhrG8eF+zhX2LgNpJ89nP57L7xtuJ+h8vf+GxbpnwPxwVFofXbfsXh2tT9SoP1BGvsa9xvYY1t/q59vedX/n1Pj4Gcd9TWNga4+yKAzQ//5YoMHQzerz77nmSu/Nh66tTT49J+C39e/Pr0EcHD0yP7xvzuW9t+IqEV+2myHgl322P+0H66d53zq4n4nnq+nTEUcU/5POn0eg/8m9nKVb/DxV1bqm1Oer+k3xTVO/qiTn6gqhsDgPwH71wbRmGXj+8nEY7tFU8k5IH05spz6/Z6Fecr2nDPYjalPzg+bxcI6WUrESErJ/jF9w0/Zgf2L2aPMz6luLJdh/XLDOt/T8KAZn6bKF+cPe87im549XYjfxkzHebT45rD8fPMPbFvb/ce83/kNHU1X3Qf+F+NxM/XlRP79t5z8Ijzf+/53FLX9vJwuV+g9l9m7jZ1m33MZ/zKrfhvoPTru0/mPK/IcfDkLqPxQUvzzqP7bcfdT4DyE8o8Z/IE5aUf/BC8GS+g+u78wy6j+qKTI21H+o+Dqm/sPoP7wB9R+GdkmswRH36ue3MIaSnorjSuYzb974/1epuNT/B/r42vj/Mty8qf+X0LX1/1mkPKn/NzK0/TT+/+jedOr/K+FbUv/PVU7r/+Vb1uVv/Nb/cx98PFP/r95kH1P/b/Bt/MtJQeFS/68ivvX/fOf/DZws5cb/pyG38FO33n95gGRzDf4z/pqbCPjP4E4rbwPzPxc3LyH+rA8FajKj54e959w8OP8F+56DT3B+f+gLkaP5l/2jzb/6v9eooPnD8DDkh8DfpZH/zjT/0tPP/TPMN6LHJPzB+UvvG8u0OX8M4x2Jj+v1P+sbHkL8gB6Mf73bne4fzluvIrOyd/X+G8y9ooj8On58clp2APvVg0ExbOarhoFCiB8nsqWFL4jfMqX/WED8I+4TDocQPy6ibRBbEP9d+Pe+ovGTVd6WND96LZeCGZH4kUvTgZNB/FXbXZu/Sb6vAsF89BLMcAHxmxEU7flFG9i9+wbi1+y2v2oQ/5XjXfv8DstbQPcfV8MZ5RA/fsNnvjmD/SYrY1FQ+5PP/USE+cLs6O8KiN9uyiayIxI/Iv8124qwvqpH5WQH8d/pkp1OkP/iguOryY/zV8mcLyF+FFf9njuA+PG6PoomjZ9P+gfT/Iuv7UYVgvOzt9KwcQtr/KTzuT1/4DOWOQzx47EcDKeAP7+WvRHl33U2C6zXbVrzS/isg6EgSH/4bi+U3RD8j3crt/T5o/ApDIZwfij3Wu8ox24WIf59W5DzkWQjqRqJPTh/CEL/iA/D+LNDs5lrGvX5ka+QZY6FO5xfKvV9Smn+23hoymFEzm/G1D7uaP5Nsq8jfjki8x/azN/I5Pxn3DjlSs+f6rLYJQ9YX58P2+cXpYsm/iqj+Vqh/vOeu9Ey+tk1/wrWsomfjPoo1MS/erluzi9c0p0/DRW350+vO38i8Tyo6PlTQeXySs+fAprF9PxpKMlOas6fnmeK9Pz5W/AcPX8ahV316flT7kdngZ4/y+3k154/Hzln0PPv8VX7LXh+E1mJ6fnT2JnjJT1/CvlmEmSWVZ8/74p63OoXo+ZfqTfXmvyB/Gj373p7F2j+gOMC16f5Ay6nf4532kNJehw1+YMAb2yaP+D30zZ/kOn1qYnmD95Ls8mPK/qizY8/z3KbP53y/I/mD6pI7PIHaZs/QDNx3zy/332879H9t1vcm/hZf8YlT/mvWmDxsd9/LYe7JsrLOAJ/TK+TvPF/ZhnPVZi/8G6xAPihy8DtjWj+RcnNJKX5M19Ta3dN8p+TyWgaQv5GiPdtfenlOzqp1cm2ERbrgwv5n99QtiSaf9YnlTw39Xq+IUvWZQn5o+LZ5V+8sJhS+68p73mi/qsKbM2g/CPMluMmfjBvdvEpNP6Gb9fZ4D7KhnX8F4t5MIf8HZrErt3438vA8RD4v/L36m3B/pG89Wd0fSPD6RtB/jHRTjMf8o/o8Dud9pB/E85vw6X1J6GoDvclyT9yePycaZC/4/vOwaL5b25wL0Kaf/R7w3EA+T8kjtvnJz/KZv+hyaK4Uv9Z3vCy4b/D11jT+EWPs8dOgfzjUvDCzRj4/6ymS+cH5y/VvzT+D52z424J9r9+rfpZSfNXm6l4gfWV+gAi0vwjDtLeFPL/qHd/2bT+V85GtdlC/nGyrnoird9Mq3hswe+Xprf8QPOPq8PledUh/7jJEvUL+EnqoM2/XPo5z2E4f8uOuNrD+Zd/5rbUnD+OW21Tyqc6/vw5h+NirudHPO8Vckrjvwt/Hjb8yU9UX4X8M8psUpQi/qs/riNSaj/X4THLIX+tFg91Ur7fvIeuldS/upC/D9OJ14P8t2Y+tyGtL177TmhmpL5oaONkSesXWh3yYgvmi9PLp3l+C/exovtPXg+HK+o/T5N7TPlPz2fHMY1f9I0iNPHnwtQ5TT6oYiXK42tJ6xe6P0QhrV/o26qpX+jvmoeo/9OVA9fWLxYPj+a/0ff3aeoXv31+cGj9Ig2+ZVO/wJZU0foFuohGW79wbndavzDGs97yC/NR/9Fv6hfR/FPR+oWxlSOb1i+4z4qLaf4ls0RM6xfG4lReaP3i589KvbBLIzU2ed/DoTWszx+f4Qg39aeXkhk0/hEUTqX1J64Q/V9TfxKH9pDu/1TcRLT+hCxn3dSfxINSYVp/+tm829SfNtOoqT+K4bytP2K5rT8ZA9W9NM/PSPfN/kvRb9jkX5/Ttv4kaKNnROOXjdrWn9BZx5ieH4qbrYjx9FwEnDRx3z96/lDmC5XGf8I4fm9p/uygfyYc8K/hi7sF9X/F+9ML6fqFOr3PjwR/lOzx+07rf9eqrZ+fFpJO9r8QGqfQS1JaP1x19bfZutpS++U2e31O/U/5iFr+WFtHm/K/sb8Mn038ppthk395S8q5OT+Vp+NX80anHQ5/9ktUTm59/v1u42XpwvlpdZSiJn6Vcryk9elI1QfHHfQP7MfCi/pvzRiUZ1p/Hl/d5HcB+5t80yPVn+O7+r7hX7LgQ+r/+n3uZEvoD6hSp60fSoiPmv03eT1U6j8lQW3z53fkyjR+4UyhjT9VcdaeH3ai+6HnP2O7Ug7L/eKX4tewV3kTOD/NXRT2Ysg/PUavFY3/ND+KxQPED/3Bd34bE/7Vs+KFG/83Oh7cCvaPdJRSxQD707v+BuNufqQM6uOZMJNCi9THUTboIdq/YPRHWUDrb8NJlX1XxH/qL0k2aX9C8Vu/BzT/jFeXwViE+POxe89p/4GqjMZN/mU0fk2w8MtdWw0H0pr2H9xHbtsf4o63btO/cFq4u0HTX9Br+z9+8arpj0Cr6Luh/RO8xu0vtL+jP+Ga/guuPr/fmv6A6vPIaf8Geg9y+vxQnrT7T/EU36b+M1XdNeW/+kH6TX9JtbyddBp/9kxv2PSn3J7t+Q+Nf7cePb+/jXVC+1+08jSaj7JXff4trXVZu02S/5ScpUjPH4YVbfEQ4j/9/hxct3V8qKNIKbOm/mfMVj+59s97zHFZsCmh/s7051J172p1VJHjol8mMu1vMGK17T85zB7NfGN5b+tvDD+j161vFM43oPnn9V5szn+6fdo1+tfR+3lF+w/+PL98aV1p/qJc3M+9F9gvfnTP/9TfFzR+L5NrMDyA/Q/U1n7Qa+lGlL9+q72NtrB/smdnf7vg0PR31Ewz2p1g/wlaa786PxDvOvSH/LaDnUHrp8z+OV6dfF90/dN9v6T1A6y1+8e4/bzKovmXr//8jKH/Zb9p9x+XhcFvBvYrD1e7pQLz2f41jEv0Maj9cwc/of5ji+Vm/+u5OXsrsH/KcW+pjKj/CFv/gRLT3Xiw/9BAKLIKfr/b+R/uvkjftP5bBO/NLgP8PofOf8VaxdP6h7LIL8EF8I+Hrf/T9Z7yzbaWnuL3uMik9Her/X/y7vzno7fTaP+F8JQv24Y/ytb/6slk1Kf9G6rxLswn+P8L89/9iXGk/R+C6JWN/sz/c1udk2j/CDd+cJcJ+P84bvlD36vzEa1/C3h/G8zA///hn8mh7V+RnuYwUcH/S4eWv/Rgzp3jcFv7/+/wp3NN/rbjP+NWtvUThRfa+tcf/kTHQVN/5lH0lWn/wDjq+PfJz670+Qk7sd1/f/hby05N/4t8WqxXKsx/D1r+5763X0Hrv+p7j3sPWF/6dPHDJDvmtP5h8KfAsKj+LP64Vqfcg/jxWFRjhcaP52Ubv6B4jeMI4kfheg+a8zOLf1CxN88ziB+llWxORBr/dfETkr75lfZX/XT32vTH/Ym/zkfxZtL4Rav59QXzdyx+m23yPIbzX3lKD/X/kvVZ/IecAsk6zZ8FTzQKQf9+Fz8Kk+y7fUL8ZfBy+qb9Fyz+5PqLQnDg/Cs42aLp3zhM7238unuV1Y/2r8ivb9P/ceriX+63X8vU/5WzX9T0j7D42TjuZ03/CxqdM5XG/yz+5sKgVOj5W5onbf6Exe+66V1cmr+sz7efbx/ixyBp43+hPP4Mmv+tAn+/oP0X1+78wI1v0YXmP4yDmse0f4OdPzh/dxRp/F9qs+hB+z/Y+UXP7+6A9s8Z98mu6R9Z9Nrzj5Ge5+cM5gvnXdt/ws5PdSjwEh1YX/tMlSutv7Lzl/71R8Id9C9jdRG+53Z9/vxzfot8PqT9F/pn2DeGMP/P+c+4Xza0f0PweqeXDev3X6/2/GguBgHdf7L+WmnUf6Jbe/40tnu0pP0jnGsWBfU/x2N7fuVuXf+Lrpw9ZQT4/YLu/Du63Wxa/9U2UYptOH8mg/b8rEfVLbq7t/r8eS5tPHgP86XDHbPu/P3pcU3/hXZerosB5A+mfnt+1/cHXNX0izBWtNO4WZ+d/3WTa/s/RNxfR9n/kj94rVSniZ+iQKT+j+Uf0O74O9D+k9qM0l4A+ReWv+Dmp7itnwt6rKuQP2D5D8ObTL+DOr664fsmcee0fvUnfzK6b5r6sa7t2/r/n/yLOm2fH28vfZvuP5a/4abOrqD8J55HbfzyPXX5n5W/a/hDTo3dlIfz36PLH6Gk639R1d7Xo/kzln9C37HT1H/FnWVnF6g/JF3+SnkK98iF/GP4c55jWn+ZRG3+qz60JvGM5h8nS6TxkP9j+TPOPAzPV7D/sjf3VvT8zfJvnG33mv5VifuGJxp/Sl3+jrvqj7cK/RfcjT9i6v/+5P8uoZhsaP4xS976iOYfu/wh2klXewz5R20+Hu2ONP94bfOPcuT0L0fI34mHwjnR/guWv+TO6WaiQP5PHGrt83sdu/znTyqnjf9Mp9+A8h/Ln+rcR3Kb+HPktP0jLP9qnBebip6f0dULdzT/IUlt/lbPuv4XLt62+ePBos3/Gjf+sqH1G1Q7HXsD+cfdrM0fK8OLbdH6WfWuw7um/6LLPxvSdt3kj9WFnyLav8Hy11wPT5r8SbFf5zt6fmX5b8Myfivan6yOs7Z/5Njlz/XlxLu/YL7B7Vr/F+3b/LsxePGiB+v/TKHtX0keXf7+phyFN+ivuKK1NPKF73AHq83/6/eN1fZfHPpPlMN8TWzrB2jnbVZ0/9VmP5hS/6l87039gZtg5ULjF7X8tfHnuatf6M80WtH+EQkvz28av8Ws/vE6oJD2n3B1GL2g/mscdfUTvut/Kbyjc7oA/nHW1l+Q+bC908HhCxwV62Uw9Kb3EzfR2/oNUibci/ZfqM+No9P5/Xdb/0Hmte3f0CbzuPG/4Wnf1I9QcUyHNP7hFtF1fvlX/Ul397Omf0SViuGRxn9/6lcb3xzR/hPN0eKU+j9W/0LXd9d/FPP9wx3qT/mvrZ9x/RF6rDTr98bPwbet3/Pjrv+iPqE1z0+72G3/05/63eniVJT/hJ3xlWj88qf+Nzm25wett2jPf6x+iNxSvtL4uxwalweG+UlXf+Rqt6NR/kGb6YH0B9br/6lfPrr+F0mUi14B+ge3tv6plVL8u0H9+nGf/w60fs3qp7p+ResPrV97c1Wm9RuR1V+/6Cv0gP+Fw1TSafyJu/otd1U/Ib0fwGdee363u/ovekvZ1YL8Uylhc/eG+eKprR9z/kfEAq0/PD7DqPF/s7b+rGdnXBMI1D+X63B8BP0ldzym9Wstry65A/Vf0Xh6Jn1+q01b/+a+m35M/ac2SL2G/4r5ramfo97wLNH4Uzpv2v4Pc4va+nv/M1rR86+4sYZfmr84sfp9Vs0PNH/IIcna0vwvq/9zViGLtP5SeFIdoEH9jKu6/oENlzT9L8Z6F903tH7N+g+GeDpcOnY6xbWF3XdT6L8wHGd8OZL+BS1e6zMd94IfjgzlvDtD/8bXavsfBD29a1mpD4/4ej4s/Tf0f/zpn7i94uek1IIC52k21fbQPyLfjr4whf6L6DBfYph/tnYP7wH9J6x/Q7nga6zB+jfhN6r/nfSvsP4PWdg6sQn6B4divP9lT8nTed54PKD/Rek/toMM+i+ec9F1VJgvP6vVuyL9J5o+LpcP6N+IY6X3kmF9T9q+TK8yxUpcxZNdCv0fQehd9mvQ3xLFzzIl/S/KZLOobNr/YvBpIMHvL32vd0mh/+X6+Ywr6D85KMv+pw/4Oa8UnWF9RTtelA30r9zn86eyAPzPTvJJFqD/5jW6rwfxusLYm6+ROpoFPMf6f3Q3s5r6vbge7Ua0/261avuHkD1Zyk3/6+a8Fpr+5a7/CO2mqOn/4Pac+lX/3b+0iQtE8y/Ffbz+0vsThtT2PxnH7/xGzx9oFLX9w8W565/aD5cq5d+fn+2a/rnJp+u/2oRzyZzaTm1/V6Xtf1nybf8Wpz2RSf1H7SqVxn+w/i9uNj8b1H9w4UVo/Mei6x/jlN/42viPRNpzzf2wzn9wU3Rp/AeqJj2Z+o/Vve1f05XJ60r9h6irbfy06vrfuNPoGlD/oYid/2D9c8ovzs0t9D9+x4fWf7D+O2T7y8b/q5NLG38Nf13/npo7b+r/pX4/G1L/vwxb/4+CS6/x/+X30/p/+9f2DxrJTU6o/1encrCi/p/1Hxqb46zx/9xPP3yp///Tv3hn/Lvgy4D6/7511foz0v+oPSyBp/1z2qTrf2H9k/q1VK60//h3Ldr67yZu+y/ReRka9P5GEVd5c//mkbT9m2iU7q70/p763bf9I6z/E23vPc+A+aIYOwXtPzm/2/5RTixQE/+qpr060f4V1n+qH3d54/91rlc+VOh/ZP2rcrTczJr+uzdq+y8C1v/KqRpH+5e54tbWjwPc9s+iVDwO6f2PX8K39ZNY6vpvt762avLP09/tTvtHPLXt39U1l8to/oW/lnpO+08Kuev/1cwko+cP3vY5TPtXSqHtH+Ymy+ue7h90sKc8jR/xou0/NtKwj2j/hcF1/S991r+cJ9qW9m8Ucm6ntP7L77v+Z+dyzGj/h5TZuKT1j8ep7Z/mrlF7v/J3ktr6j7cMm/5rrnfOjrT+Kj6fbf2cu5nt+YX1r6BZ1PYfGXrb/23gnjW/ZrW3x+lcGr59wQx7XNL1jxu/563pf9L20m9E+5fjrv/cmPy6/o1N1d7/KKxf27/+Xbb1L1X9cIP7v/rf0XC+b/pHOMH7NffPhqjrn5esNv+izXZ+c/9isG/773XUa/tXFOf+8mj/cbFr+/f1hTceDMKe98PBdB5+6Pnzu2v7/znh0j7/ctH1vyjd/QHuOHkr9PxZKI9xU/8dHNv7B9y4+u3p+VPR9239Y7Jv7y8Yo+8joedPdfc8Nvk3dv9Bn5eD5vypWd35k92fQDNuhuj+41x52PhPdv9Cucmr6wXuz+Sjbxs/zfpt/Kvbm/b8goLFy2vyB+z+R1rOmvwBsqplkz84me39EWRvRk3+XNzrfUTzBwlq758gx2jzz4ZTHZv8wffY3l/RX13+pfSdRG/6b7L2/gvi/cebnj94vuc0+YN+zUf0/ox6VqKmf7gK923/Bbt/g15cf9jcv/C7/pdtd3+H23yzogf1c6U/b/s/Fp/u/k/Qr3haPxKfy9WI9o9suvtD3HCLNZo/lYZn80j7T9j9I/32aJ+fcbjHPbr/qu7+EnJnbfzMG2MlO8P9mWDY3X96LYUn7V9Gq8kSNf0XZXt/Su/5++b+x8+VlRHt31jP2/tX+mcTZ/T+nIjDtv7M7m/pK8to6m/CR9eb/hF2/0u/cLcFzT9X2fuzpf0nf+6Ppcp7Te0fCSez8V/xdNncP+O46FvQ+EFbjWe/CvKP7P6a7qFNRPsvyrKPWv87eTX339DbSjPavyGrXf9L/9bdn9N26pr2fwjmsJjS/KO6fzX379DmJPq0fiqt015Ffz+7v8fd10bTf1L4Uvv8/tz/ux45v+nfe06dBeW/MWc19wcNPltfhE9ha3hdnAVT0fJzavy09v4ht/bcPu2/0NR3nNP7HxW7vygt+Qft3+DQK2nuz62UqLn/yOW6dqf9A4a8mfyOkP+2z/vm/qTBybFO629VXPLN/d00DJr7l2hYnCY0/8xfBubKgf3L7m/qbyuRaP5FsJTNm97feGXd/c+RUSL9ltTxyfaiWWMa/72P7f1RIzlfBpQ/FXl+vNL6xcvs7p/O8rlB7Ue+df0vBru/at9meVO/OByPHK1fsPuvHB9tprR+IbD6xb0XNfdnUTVDV7r/lLV6aPJ383dXv7CqXKf1C3Ueo4jWL4bs/q7o6jt1+X7scFxv0Rk9Py4mXf/FxmnzN3wdPzf1p4fX3h+uqWpzau4/Bse0qT+x+8eG0cua+mE1LAOR1p/wvr2/zPld/a2wnH1bf5Lb+8+cnAcCzT8b0Xve1J/Y/WnOib0jzb8IAy380vrTG6un+4jcv5bncn9I709IwSJo+p8R197fRkjQEL1/VhyLq0D7N9j9b26hnvb0/q7Adf0vWdjeHzeeu+uTvn+Bl0xtRvtH2P1zTj4NzvT5KdkQVWvYf/tbe38dPc2W/3T+0sYvF769/65Lt1lzflAiWc0/Y3L/mt2fVy7d/Xq+7O7fP4Lufn7S3d+XVt39fiPq7v+707J5P0ARdu8P6Dvt+wX+vH9AW3fvJ2DvL+Dk7v0G5ap7/8Gnez8Ct+jenyBr3fsVtO79C8js3s+gfM2tTOvX4rl9vwM6svc/sPdDzIT2/RHcsnu/hLrt3j9RH5Ka91P8eX8FOnX9Lxp7/0XU9Y/8eX/G8zJu3q/x5/0bv3v3fo49e3+H1L3fQ5G6939g3W7vD7D3hxzY+w/Y+xH+vD/hz/sV2PsX/r6fgb2/gb3f4c/7H/68H+LP+yPY+yX+vH/iz/sp2Psr/rzfgr3/4s/7Mf68P4O9X+PP+zcy9n6Ov+/v6N7v8ef9H+z9IH/eH/Ln/SLs/SN/3k/y5/0l7P0mf95/8uf9KOz9KX/er/Ln/Svs/Sx/3t9Ssfe7sPe/yPQ9NYYiydfDVVeuGn+QDFk+6FdZ5/WzeD1f1ev5vym8LF3/8Z6a/8cp/35PjST+f7ynZjs+9L3ogRca+hplr36uioCvYTg7ovm61JFTbVR80PiVixx7MUKn2nhxfOAWR+M2NQO0dCwbJ5PX7sh9+qeUq/DKxGUW4yO3HXMOF5a9B5ZPBTrKPbwgfTvfDTZS3tXU4Pubo3XhxKSvXq+Ua2R65DKbjssnSdnPi1nE3VfqHqeutg25YpeeuE/pP3EkuIuKW/uZw0XPdIbz3Fz4nPkuZOQliwWOn1U/1N3dKkC9+93By2X80oQs28ro9uQt/KyK3JWrdJCiJJX7ONWLXWKMcLbjrov4jN/Vx9e4mZPLXChaMo4Gg5VmLH9fnrtN5wGW/dXI5V7cpdZP1zWsBeujpmy3pYdu5vqIxbmd+pLxmI/Q7hY5WDM2U59Ln9cdF/ujEKtpOaq47GbwXGEuMvzarnahsd54PDdbxXKt/2hZGcMCR9yvh+f4HDiOrXOWMELbwtvjtxcdfHmxeu/QRx4M8fkr9RN9cOBlNL3rN5wel6MjUmaPEcr8uY4jPNsfkW7pPLcN0RTH343vos+U23Hqxulh9Dx5mj7Vfg46bPc9LKiTOjgsJMRzaPc1sWh7Ssjz9n2Fzneh5mF8efvqbJ8/uGXPHuHcKVY13sPpirvcRxPMy8asjju5c8BZ74eCb/5r63OcfOE57z6Z4Xj9nvtoqxYnVDqbIz4JZexrSfnuofUp4fAhPBQub/PbX210tf0dh8T+LGJ/R4XY34vYXwD2tyL2NyD2l4D9XYn9vYj97Tr7k1Jif3Znf/qP2N+d2d+ns79CJfYnEPubgv05nf1lYH/Lf9nfurO/Htjfkdjfl9jfGOxvWdvftfx09icQ+xsS+xOI/aUnYn8ysb85s7+E2N+S2N8d7G9G7G9G7C8wiP1dif09wP5SYn/8v+yvR+xPB/tbEPtTiP1lYH9VZ38/Yn+/orO/FdifQ+zvS+zPIvb3/RH704n9acT+Nrfa/iKe2B8m9hfviP09if2Nmf1tif2pxP6+YWd/HLO/J7G/hNnfjdjfHOxvSOyvJPbHg/2NavuT3sT+BLC/JbG/nNjflNgf/hH7mxP7c8D+lrX9CT1if0+wv+H/0f52xP7O08kdL90Drs+Z0TJF+n5k4ld8HfjGY+pECIu/G45lredyS6n/QNio48j06yJNS3erHRIukVbrb/HH+vgXROjSWzyx1vfurtTH2YiLX9891vH5bquFUz+y1fx0xXx24ELVjosfWoxjE9+za+TXhCJG6PN2dFwHew9NTuqjBAoejoPlvmm6eliWMrrOnhhL8uF9VME/pZvnCxtov/alKqifZ8zfXFwVk8xW4PmJ80d97lmNez6K776HOJX/1vhLqc0/V1KK8uFgjB/qWUiUudWX0WKwumNuXkcd6iwyI1Sp3zPm95/5UQ7HWW0f2+kdq2E0ruQ6VtY5w+AlrCwjqdJ6n3mE+mcO4a85UEN1d3RktET6Dt+GGncUZW+YIjyt8fv2CX4Jw2/P8JvX+KmA34Dh5/9f8FP/iV/2Zfg5BD+zw+/C1fjJPYKf1+HH2QS/guF3+xd+AcHvA/jFNX7pl+D3APy2HX5LwG9D8Ot1+PEHgt+C4Hcj+E2lGj9tSPCTGX4bht+3xi8VCH57gt+7FxZ4H/D3SpsF2zk6zWp98msxTNR9tY3QUjIT/FyptT9bzt8rZPichFNxN3bl+2WTojca8DhZqpYrpStRR6tcHOOPHKiVdC/qc5xXeyEcb1bTSuZe7wcKJ2KIneEjCQW5J9Vx0hJjfF6Flcaft28eGc7Er/dPgV05Jvhu0KDe3+tzL5FL4s8uV/WDs3P4DovFceug+/L2wU/eHzR8VQ4vP3xdfl1fBvvbC9YaO5/50/+p6ltHsveLcG6+nETrCcMHSiTugx/YmbmKMhB5JGkzDUcPKdYEYfri0S+Oj/h5R2ElHaZS7f/sk4zzreNWqmRs6/19M3r4or/sUD3jIY/037qP95mPNWFzHOpo+Sb47Rh+FcHv/k/8YobfGtf4xfMOvyngd+nwexwIftsOP76s8Tt5DL8VwW9P8AP73GKC343gVzD8hA6/EPCLCH7gb0uJ4BcT/MD+DgS/gfJP/DKjwy991/iFiOAnE/xkl+C3YvipNX6vWYffHfALO/wuJsGP7/CLw5rtZpV8CsXDqcbPn+Tf3Mb9ryvaylBGd/57zt/ib5NoN2XrIVUfWTlXaedEM/CzPtf1Rt+ci4aVyw8Nv7bfMkpy+az+bOEphD9kxT0zR8d5YGu9cf350XfxyO94tbeVvrwdofllmeW77BtpBeA7Ubhb/lgegiPd//Hb2+TVhl8d5Qnh343G/3IjDQYJP9OrABXn0S2vonzkaxAf+R9pkydGcffp/tXE3TL/Xr2JTfFbX4NB/jzKZqIEAdnv72Sdh+h+cxUNCTJaS8Ej5yRhWPsz+9FDzi345b/fRa5KIfHrc1Tu+LmxTze+ImSPB8qFyyOPuPXNlodTsT6nFEsjX+rjxBZEJPHo/qjxiyyG34ngd2X4zWv8jB/Bb8TwK/6FX0jwswC/6b/wm9X4rScdfu6e4Gcx/LYEvzvBz2X4nf6F34rgpzL8lA4/fVDjlw3/hd+W4bft8DMYfjnBL+3wKz2Cnwj4XQl+n3/hJ3b44enqkkfq7FbvD63G65NHtUXy09LVQudVz695Pw/yl6PpfduO0MRPy1wpt9aRG72Rx2laEeby6b1K1Ll67nEZGp9zbv0MQnnjGymXi499rpufQR0/ycEJGYbVy4M4qPkA8P3s51IeLo5mQvf/UknU/OkMa8ZzCD+Fq+8wV08P5aiuSLw4Ehaf3Piuzkdpf5j2uLi/93LRQ5WvQTwuz7GUPxbPUchhwh/zdC7n8Q3FvqKR/YuziVPbqyocqf11+h2pftpP2eVK7sWaGs+nOy5Mpu9cduPClgb96ZzjTyc+V/DOrM/VEd5x/jST8yzihzaX3O05ct7yNY/dDFW8Kw4jtK4IfhLDLyT4xf/ET3r8C78zwW8F+L3+hd+Z4JcR/GB/fz41fvVRvcXP39f4PWSC3wDwcwh+KcFv3eHHTf+JX41DjV8K+PX+hZ9X45f0CX7AH5gj+AkEP7A/0E9TCH45w+9N8EsYfhrBr0/wq3SC35bgNwL8vgS/gOCXEvwu/dE4f6F04CP84R9o9bZP+fp7c11DxFUd/4jFKs/DtZ9wnNfjUeZtl/kj8nytdpTGidP6uyKXrHl2VJL3TUfRnce56nztCvWiGg9ls7HyJC+3vg743odJL1/94kllwP5/L+0kX7vG/agBP/2+4iiPP8mm/n0kfpo8Ey5P73fs6gtyvvFCWcvLyOddPqrdDtrskkv++tQnWPQj8fdcfJr510rq9R4kfrn5lyQ/H6qsUkTCH2q+uubPpzx2UUz271Mvkzx3ZhuXA/wum94ul77BwOW2Xh3f8VMvy0v/sKlkPq9G6LSdJPl9sFr7+qeOmzjcm4/zwHdWvh7Vpo82i0zJ17l3DwVhW/ML3hD83gQ/A/BLCX4Pht+6xk+8EPy+gN+S4OfV+HGI4CeLBL+K4OcT/G5Sh9+D4Pe51/itrgS/B+B3JvjdCH4i4Lcm+MUEv2mHX+UQ/F6An9zhxzP8BgS/oMPvMiD48YAfIvidCX7g/15pjV+mM/xeBL/PP/GrXIJfCfgNCX4Tgt8D8Pt1+MUEP/9wueYzN631Eb17fV6+zHb5+GHONeSWyxWydsEXzxfuxtWV4BcgI67jmUci7DQ9tac6N+1ZDxzOex9XGc9edfy5txScFRvPN2KC7215XuKM03cVh8n+V39XDx/mluvrwE9pEo3wXiu+rgbxU8qXI7wNnPo8OCbxvWTPc5wrWg3rjZzHa2ca4NuK/9rCVRTnaGbUccd79V353Iic91IueONwX+1CnSPx8yHhMD4e4lcdYpD4xVtoITa9nedywL/PLHHwTq32GrLI/p1OP0dcR09uohcEv+14eMLP2UKyZam3SlFZbSz8KY/bUL9kRsD9picHv0N7HerZqndCr9/Uxps4txPpFG51ZI0IfmuCnwT4qQS/kOBXAX7zGr97RPCbEPyOCsFPYfhZ/8RPAfx2BL8BwS/WCH5bgl8O+Dkdfi7BTyT4Babe4YcJfiOCX/Av/OC8nF4IfmeCH5w/dgS/04zgB/HfqCD4OQQ/4N+Q4Qf+z/vW+KWI4FcBfnuCn0jwEwA/n+AnEvz2gN+X4Hck+BUEv35UfvBCvs98VN0tHc1s18Nn/ukeuZKvan/NWQd8RwdPq0O7O3kPzOiGjQnJJ/Bi7d+X+TrE6JDUfJERfJdBgbEW9vlQhv3/Hp/GuHxoXsj1gZ/wV8RZYvVCBPFTNQ4eOF+hr0bje85xF/i7jWeaDufPiRoZWK+EXpO/nInGC1eW/fCl0DJ+nF/xKpaiuxWiB8kvvCbhFef+eVohm5zfuLR+Hu+eNLNVmcTP4qbY4+e7cDSDJ/HLcxo42BgOpy4aEv4YKYs1lswy1uSQ7N/353vBmi7U/Hkk+N1l/Y7lCx7VzwvX+ghqmeD3nXN947W413wq8hscI0U78o9Lfb6eZ//ETxgT/CyCn9Dhx70YfnOCX0Lw6xP8Xp9/4qfmBL+K4KcBfkqNXxYR/B6A34Pghwl+C8AvJvhpBL91h18pdvitbILfg+AH+ZmnSvC7Evzg/GvoNX5RQPCD84f46vATOvx0jeAH/Dv6dPiB/3vva/z0gOAH9nffEfxeBD+J4Xch+L0Jfr237uFPYPd8I/efUc1HwQfH78lPU6OvGKD7O+zh6Cn/XCXnhQC536jCvBu8Es0l+JrOd4d/8cg98irZ/9x0PcOVka5tFfjd4nY+Fuq4tcaXxE9X1zzg6ONufRrfr+0oxw/hd2jOn3Vojerz2qlIaH7poyl9LE2u+3o1ku98nr97LM0D7Nfc8Vuhz+Bs4up0D0NtT/JZ1eoY4wBd80o9kvyBttzN8CO6zm0Rzm/hfuPgxNh+XBo/q/b8hHntmLkqxC+mt1jgSnesowz8cfGmG6xdt6Ytl2T/njX5i41DGldKRPDLj5MIJ2oZ28o9eI3QbPKwcDaOpETQd9sfum8Jfp9/4lehf+KHJsMaP6XDj+//C7+I4Xck+CkMv7DGL/wS/HyGX/Uv/O4Ev4DhFzD8NgS/EcEP8oGVU+N32xL89h1+78c/8UvN/z/8dJfgB/7v4hL87v/CT+zwE2aXJ75OnxNbucTbFIloE+NzLsSJKE23MpJe4QX7908vUTiCb5y/v/h6+/YTGfb/4aDP8P5nzzXpTPhpo5eTfP04Zj6Nn96chvLFghtW8pTE9+78leaHdJX79Pz5VDyEr6P+4kjzIxaqTwurBA1cFfJ3n2nNB+vjBCW8RfLz5G849LzXcWfbCNXnyaQez9fJBIV0nMzPV280sOn818dD+cXpL3z6/ZPglWKy/rFZv9YPL9bcUKP6rWr98fp3zOpxov+l/n35LrXnFf19X/z+5tf1tx/S3y+J4SX3408vVMD+ykgJvd52eEyofvKG4Pci+MF4CvitO/zI92Py/Rrl9/WJ4Pcj+MH6r+x/hx/f4ffa1/id5Q4/GxP83h1+MeBXdfgVdTzvpQy/yCD4Hf+FX87wq78/v3odfmT9v/hFGcFv3OG39wh+QYffieBH7KOivy++EvxuBL8nw+/e4SeRYOdcCXFI7Y/YZ07s06X2mSeTKE/KMnaVG7FfYt+5sUljTQF+Ivafa2hrunJO7N88LRZ5xTuWL0N8r4bzU87nx8xW4fz5+GzI+Xb7ac73Wrmb5e/Tde7S/cfX+zMn+1Oj+/PzOpt51bv/z/LOtDlxJErXf6Wj436juxCS0HK/2eAdTIJlsN3RMcEiMDsISJaJ/u8335PKTKSy3VU9PRETdzpK2VXHBkmPtlzOk5okPo0nzbbbN+70R6xnhbi+173yNcf9U1xPuP7FkRL3EnF/ncj+5464f/DJYd89yfvHSNxfOO4vPXl/qcavEcf9O3GvcP8pDNoNjvt7zQtwf7p83r7yYuW2FllU/6uL+xs/OaP5xL/H/W3iTC748uDuq/L+11hPKxz3x4m8P0p+nuaH+2vKj55PcR/8Xgy/uua3In4R+J0Mv7cP+OH+n+FXBb+e4fds+A3A7wL8Cpqf7YHfyPDzwY/urx3x/OLTFfjR82tUA79Zjt8I/K4Nv0OWH56fkeWCXy0U/Kwq+FH9ZXoLfivw24PfwBmtOJ7PJ/l8vhTPb77qVy968vltd0X7adEt1Hoh3V/fRf2Ju2t2l8jn/+J+G/NgXxol7gD1A9QfuHN9XJ/cBeoP03B0xVE/S9v3JVF/47PkcHWS9TfUT/iy7jRqsn6C+gvnvcGjX7xE/SWKLI+jfpjI8c/GPJxzq1ud9pw56j+oH/GgWLqI5Phk+Fxr8W20bpxk/eko6lcc9auTuB5F/Qr1V77jlYtE1r9QPxPtdb+eFG5QP0P9jfv+tZW4N6i/PbH2hBfi5HLiHlC/Q/2ZhzdPd5G4tEX9r9modDnqhydZPww7VocnvbIfyfqj/QB+MfjR9Yv6E3fH4Ef1T9RPedAEv9jwu/+Un2Vn+RXAb+6CX9nwi8CP6s/RBPz24DfT/E4F8FsYfg74dYnfFPxewI/q70dRv+f8Ffyofu8PwS8BP6r/z8vgN83xS8DvTvMrPoHfnvi9gd8T+FH9mRXAzwc/qr88RLU6H7izWlTYo/1SeT2ueMsaN3qyfbMV7R+O9mMSUP2Ji/YlXxV6L0nQBb/n8m2fzwotp+aW0X66Hax6fHsb1SYB9Y/EBfE8QPsL7+kR7a/LS3/CL+9e61XZvm3XCoz3K+u57yNxubjajRZ8Mju9JoGH9l390Rvwxev2uVdgaP+dhpMRfx9Z21ppgvaheBJwjvbjpED5GAvRPudoX0ayfTkT7U+O9mfV36H9aV8M6xzt015A/ff98eCJ80rw6sv269t9pcx3YafeC6n/5X5fmfLx5mJVLTfQ/i2L9jFH+/gUlMC3vOxvefOt1qnK9vP44rHKX7q8Okmf74+aH93/9sTPA78h+EXE7wn8fPC72YJfDfyOOX7Ufq22wK8Ofhb4vQouHP0Hvuw/WDXA7w38isQvBL8++NWIX1HzG4PfEfzQfzEpUD7B/Bn8puBH45fLmuDXrYIf9X/YTfBrgh/1jwzAD+1/v0D9p29l8CuD3w78Hi4Ev8kr+FH/QbkLfgn4UfvNuwO/FvhR/TkqNF7Z/eayKa5v9O88e/GQNW43zVrRRf8Pazbv2YhdPfdk/xD6j9i48dzuyf4ja1DfsVOv2/Fl/1JsX7wyZza6qcr+p3lftJ/QP1WV/VPov2Lov6rK/qv3SZywuHLa+WUaX0L/F0P/YS+g8c+XuyRm85kttueI/rH6yfXZ8SqyqtYC/We166TANvMxq8r+NfRfMvS/RYUS+t/WT9WEPU/CcST75ya3yQV7ttYPvuz/LM87FYb+vZ7s30P/KfPuBP5CAf1//uz1wOyn5i6S/YN88/LEpv16dArucf03bw7PDP2LE9k/27nclVk7qo+TtP/xAvwi8KP6S38Bfjfgt9D8jrUcv4Pmh/5Rhv7RamFF/Dj4zcCP+k/fT4Lf4An8qH+1dSP4bQrgNwc/9C8y9C/WipSfU4/Arw9+c8MvBj9G/AbgtwI/Gj9fVgS/dgR+1D88dsBvA35z4meDHwc/6l9eHsHvCvyKmh/6p6My9V/t+uA3BT/qP0D/NkP/dsoP/d8M/d/VsIT+73h2d8/m/uamV2yifxz95wz95zXZf47+dYb+9bR9j/53Vu6tDzWH+u+CZvmVlYf19UmOb2D8g/khxhe26L9vNh5E+7bklSLZv9/sN12WXKIeQ/3/7p45bNqe3SUFGp9ft9/qrBQUTz05/nJ/3VqxsPU8iOT4AsZvGMYfIjl+E5UTj02D29tT4RrjE+v7psPG1z3xvKDxi3BbuWAYH+rJ8Q29fT25fRgfYYXZTDw/iS/GT5jzunieeHT/rFU3R4bxlUj2v3ROhQLD+MtJjr9c37pDtnJ2Rd+i+n00AL8p+FH9D+M7DOM7NTm+Q/wcX/PD+AbD+MYn/DA+wjC+FMnxpWYg+K3fwM83/FrgR+MrGH9h9h34LQ2/A/i9EL8p+PXBLyJ+K8EP42OnQgX8VmXBb/Jm+IWaX+Vzfq0sP7cJfg3ww/gUK+80P+T9sNFJ81sPlw8M44NVOT7IwueYYfywJ8cP97N4Kv7dfk/b9xi/ZeFs0+mVqf5ydRjt2ek1dv0jPT86LyNx35yUbk9y/Jc7SZtNnPF7NR0fLo5uGMY/J3L8E+OjbFetP9Tk+CjGT1lyOIx7Mv/msL17Z6c+v+vJ8emXnrVnYX10M7FqGH9dO/UOO4kGZyTHt2urwjubFLqjNP+luXvasfZYtELk+O79tjVlGP+tyfHf6sPFJSsOm6OaHF+PnpYJc0VTqVaS4x/7uy0Lp7enqhyf9+t3FVbo+YOJT/VPcTwHDOPTE1m/T96fQvbk3ic12f4kfquK5vfCwO8xy++wzPE7aX77W/Aran6dIfhNDL+54Ld9yfErG34h+E3BzyV+Cfj1s/yOBfCj/LxaWfBD/kDKr9UQ/N4efpLfm+E3EvwKrzl+C81veiX4IT8i5fe85ltWja63VZk/kUSMs0bi9hOZX9E+hBc83s+ricy/GEz6rqhwXNV82b+86657fL4uTnyH6i92r+Fz5HecSvT8WN8WVnxau2pU5fVr1/dLjvyQScqvVGnzm1lz1pP5I5uXeM+RX9KT+Tnx0/uKI3+nJ/N3+txbifridJEcIuSnPBdvAo78lYnMp/Wth4ivZwdWdTfIbzkcmaifNienk8x/ee3aE7H10ySR+TEP16I+sx4/P/rp+Dm37/l6M/J8h57vM35j8dXRq1Qdqn/6k4LDN7vX+6rsf2aLy4TP+l4tbb+XL9rX/M2O2En2jzwTP1fz65/Ar5fjd6P5lXzwm2p+Kwf8os/5tQW/q1aOX0fzG4wFv/mV4TcU/Pj7JMtvAH6UD+pPwW8AfpQfdHgS/AZ3mt/LGvyqmt99G/w6OX5Nw28o+K3X4LclflXwKxh+L+D3rPl1G9NLjvyyiddFftmkOTlw5J/5Mv+sGhZEnal04yWy/yB8tBxeXi8d36f22+Piccz9aHnvu1R/Rv4gt9qrZuQuUX9p2s9jXqhXw5NHzw/kz3Hkz03S/BHP2vLNwNnUZH5iqSm2J1zei+cB5S+un99r/LR62NXK98jPQ/4eL0Rv7Z7jIH8v3s0YR35f5LWQ3ze0rsT/ri8vqzL/b3V7FfDdoTI9Sb7iWXvJx4XhMs1/iUb9IbcK3ULiyef7zfaNB73BuObR+O9g1Jpxr1UfV50G6velxtLnxXbFitwntD8b4emGb8Ja8ST7R55Y8MqR/xjJ/MerR8EP+ZEZfnGWn5fk+L0bfnPwaxp+t5of3f/CruC3fjf89uDXB7+E+F2BXwn8KL98cyP4hRz8XOIXC36OBX7RR/yWjuDHW+C3NvzWOX4e+N0afhH4OZof8mOrziP4WQXwW2l+yA/lmzL4bcGvOd+/c+TfVmX+bbC6u+Tz+fCmF86Qn/tYeWjwdWvR7BVp/AP559wKwkZSnKD/APnC/JTEi55H7bfu4u2Cl3YPzsSn9sdrtfgoeHWiqsxHfjwG72gTiPY+PX87h/obX26W3Z7bxPXbmK5djvxoP6yCH/Kpubc83vkyn/q91e5x261uejL/+n3QHHH35fmuWnhHvvbwsBbtyWQVnQqM8rtnVoUjH7wqr//HXqvFkT+eyPzxdSWaceSbizMI9Sf4ERz56YlbR/3+4vbQ4cHIEtcvtd9PxedLfhyvWSSa4oWrAnwNjnz5KByi/669SQq82+seqrJ/WfJrgx/1/w0r4GeBH/VfId+fI9+/59UNvyP4UX7Iywn8Zppf4wn8huBHz9/XlvgYfIX0/if5zcDvJsdvS/wuwe8O/HzitwU/Bn6U7z5qgd8J/Cj/eXwNfifwOxp+M/Cj/LX1E/gdwO+F+K3BbwZ+1D4qOtsOD1/Bb0j8huC3Bj8a/x0Qvxj8qP9z7JZLfLiYNCLpx/T49pUPZ26zVgiZaF+PjhfianrpdXoB9Z8+vjZaPNryN3E+ov+qOnmy+ODkurUy5Tc8XrImi64KjVNA7V+v3Sqxp8fnblSk9ofzdNFl7V1YrwaUf1foNGJ+mxREfZzyQ0Wzt8179yvx/Udcv28XjQN/bbY7fpHOv8rxsVT37zd3fv3i4u6i2G82Duz1ud05FT38vCw+z3rXq7dJ0cbnrzpPMbspFtZRmZ5PTuWiy9vHsF4LHrF++7pV4ti+XrGD7auJ7edRvdDwgxm2/6b6ZGHuMrdapv6l1rLRYtGKi+9fYf9HTxchA58ooP75x2XE6xfpf2L7RmPwG4If/Ryf5/h8Ij9/fQI/Bn70/Q8H8GuCH60f25fyo+2zK+Bngx/V/6o3TzG/ccCP9s97BL9r8KP97+/B7xn86PzbzPeTjeC39O9o+8CXge9J8g0G4HcPfsS/YDdihuMTyeOD45fyo+PntzU/Or61A/hdgR+137F/bOCDX0znxx34bcFvTPwqmh+NX/bF+cdw/lVFO0ecf++vWX9rcA9fJta+Fu4PDOd3Oj6P85/h/Pfl+X/swn85aP9lnfNd6n7Wr8L1d+5TjS61P0X59bj/pb4UXb+4PzK7Dj+F+J75KHT9s5z/9HqZ9Z1wf2G4v/TS+0sVPsk7fBJqf+L+lPoj1D9y5ivR/Qv3N4b7W+on4f537iPh/pj6R3R/lL6b8Y0s8tti7Rfh/suCqfbZpL9m/KEzXy0y/FaG3yzrAw3fs/4P7n8Mz4+afH5Iv2cKfp7ht9Q+j/THjL+D5xdbLrWvg+fbuZ8j+RkfR/LbGX7k2yTgR/lXZ37NNMePxs+DMvyZOfwZej7j+Z3xZ+B/bA7an8Hzn+H535PPf+nPXML/oPrVytb+jIv6A+oXzLLhf1D9AvUPNj4a/+MF/syb9j9Qf2HuvfE/dtr/iIw/c4L/QfVH1C/ZKYT/QedfaaT9jw3qT6hfMdSvlD+T8z9Qv2Wo3/pek/yZtfZnFuR/LODPcPgfVL8LB/A/YvgfVP+rkv8Rwv+g+iHqj9/7M/c5fuTPtDQ/r635of7FUH+tyvoX8SvdgN+N4WcbfrfwZ0o5fpcf8WsZfyYCP9vwI//o3vBbav8I9Xu2Wf0Nv6uv+fkv4Ef5f6g/s/JY87saaH+G+KH9cu7PdI0/Q+0bRv5HE/4HtX98489M0D6akf/hwf+g9hPaV6n/Qe2r+2v4H0/a/3glf6aq/Y9jJevP+OR/DLT/8Uz+zBv8D7p/9pm3YmgfTmT7MK68r9j8QvsziZP1Z6Jr+DNt7X+U+ln/Y0X+zAv8D2q/lsifWcL/oPbtlvyPCvyPPtq/A/I/hvA/qH2M9vO5P1Nuwp+xtT/DyD/qG34T8JuD31TzW+X5TQ2/NviNNb838mdqmt+B/JnXv+G3MvyK4Fc2/MifcQ2/F+3PEL9n8Lsx/pFd/4hfTfOzyZ951fx28I/mc/Cj/gP0zzC+0Pzah6w/g/4djv6d1J9B/w9H/0/qz0yf4c8UtP/hk/+xgP9B/Ufon+PoX0r9GfQ/cfQ/Kf/Dg//xov2Z+5w/g/4t/nbmf6zgfzS0/5Hcwv+4gf9B7Q/0n/FwBP+D6k/oX0v9GXr+RmXtf1D7M+jAn3nR/keHwf8Yav8D/Xt84mt/Bv1/HP1/yp85jPb80DT+TBH+xyP8D+o/3Lez/kzSgj9jaX8G/Wt8eWP41bU/Y/ihf1Pxq4Bf6VP/6EN+7Y7hB/8I/a/Kn3HAr2v49cCvDn51w+/V8CN/ppTld+7PFHP+jJPjdwt+kyy/U57fyvCbgZ9n/Jkw68+gf/zcn3mpfuTP7OB/XOf8GZbzZ6h/fn+T+hWf+TNl+DNvxv9YaX+GxgcwfsC9kfZnML7AMb7Qk+ML0p+5g/9B9T+MT2T8jwD+TFv7M8344Ypv7rP+zNn4gvQ/EvgfS+1/OEX4HzR+Ym20P3Pmf0y1/4HxGb5KtD/TmWh/hvKHHk7anzH8nOccv3fjz6jtU/y2mh+dnxh/4uN7ww/+EcanfDk+Jf2jTY7fu/Fn2vBnAvBLcv5RQ/tHGD9L/Rnid+bP6OOb+kcYnxO336x/VH4BvyvjH5E/Q/nPUQh+ffDbkj8zgz/jw59p/pA/Y/yPW+N/FOB/rD/0Z+ysPzN5gf+xgf9B458YH+UYH1X+zED7MzR+WiP/I4b/wYw/8/qR/0H151ZndskxfhsFVH/B+O5X/gzG1zP+TN7/GMCfOfM/jD9D488Y30/9j5XxZ2of+TOSXwB+fePPzMDvCfw2hh/5MwXDz/gzkp+l/SOMz2f8mW3Wn8H4dOofUf8Vxv/5kfwZ6v9AfgBHfkBV5ge05tqfIf98XIU/U/ncn+lrf4bOP4y/c+fd8NvCnxmC3yHnz0w+8meQP8GRP1GV+RPRDv7MEv7MPfIrvNe+8j8o/wL5Gak/Q/kZD+TPvML/qCF/420F/6ME/4PyDwbGn2nh+kd+COvW4X/c4/mE/BGG/JHUn5mTPzPV/szhpP0Zyj+xhvA/pvA/qH+kTv5HH/4H9X+ujP9B7Y/XWtafqb5n/Rnkz5z7H8hPSv0ZOv+eV/A/nuB/eMi/2U/gf3jwPyg/ZzvQ/gzl77zDn0F+TyLze8puP+PPlNdZf4b4jffgR/lDryvtz9D5ifwjhvyj1J9BflLqz9DzfV7T/gzVPxfGP3oAv/0E/ELtH52I38jwG4DfK/g1Db8Z+NH8B+0e/JkH7c8gP4shP+t7fjeG3y34WYZfAfwo/2tfBb8C+JH/sQ01P8ofQ34ZQ35Z6s8g/4wh/yz1Zxj5M77xZ97gfzwZ/4PBn3mC/7E3/geH/0H5g8iP+97/WGh/5kj+TF/7M8hfZNse/I8m+R9d7X9Qfh7y9xjy9yKH+u+Q38eQ35fxP878mbo4HvO99meQP8iQP5j6M9Ot9mfO/I9H+B8L7c8Ez/A/+sb/GGt/BvmNqT9D+bXIf2RJpP0Z4jep5vyjG/Bzcv4MnZ9PF+BXBL9b489scv6R8WeOK+3PUH5nSP5RZPwZ8Aurxj8CP6sDfpQ/ivzS1D9aZP2ZQlHzWza1P1Oaa37kzyA/U3y/4fcGfveG3z347cGP8mPfyT/agV+J+HW1P0P5tZc5fya+hT/DtT+D/OfUn6H83foj/Jkq/A/ii/zf1J+h/F/kBzPkB1dlfjDyh1mpr/2PEfkzA+1/IH+bTcn/aKH9aRn/g/K7kb/M7ELW/7AD+B+h8T+a8D+o/w/50Rl/5gh/xvgf07z/EcH/OMH/oPwb5Lcz5Gf3ZH428reZH8H/4Mb/IH+Gzj/kf6f+zJD8mXXWn5kYf2an+Z1OWX7Sn3EMvzDHj/yjK8OvlOV35s9Ifr72Z4if9I9i48+8Gn5z7c8Yfuf+DPgtmobffc4/+jt+5B8VP+JH+cncB7/jp/4M/AUGf+Ef+jMv5M8sc/7M46f+DPkf8DOUPzPM+jPLwQf+TK3yc/4M/JJP/A/pz7T+NX9m/5r1Z/Bzhp//Q39mRvzamh/8nww/sX8svvg5fyapfOrPVPlP+jMN8HvU/F76/zV/Bv4Wh7/1mT9Tg/9h5/yPsfY/BpvHDg9u4X/sjf8RwP+g+in8Mw7/rOpR+2kCf2ZzbfyPJ/gzU+1/wA/k7z3jz3TgfzRz/kdf+x/w57gzh/8hx4+m2p+h+0P7lPVnhn7Wn4G/l/oz0v94bDe49Qj/g9of8P9Sf6as/Q/4gRPpB8IfTP0Zen7AL8z4M7WcP5OAXxf8lsY/GoIf1Z8ugiy/8gn8iuC30fzO/Rnwm+5z/M78GczNCv8ykf7gGb+i4Xdj+F3l+OX8mSH5R6uv+Un/yPDbz+80P/hHFgO/uuG31fz6i6w/c0H+zMj4M8b/WBj/Y278mTf4HyP4H+TXwl/m8G9P0r+Fn8vh56b9I/B3Ofzdk6x/oH7Cl/2cPzOE/0H+L/xqDj84Kc7J/7DDOT968D9mxv/w4X/Q+GWB/A+m/ZnTSvsz1P/skf9xgv8RGH+G/I9r439w+B9X5H9w+DNr+B8c9Tv40xl/Js75M7bV4fCvM/7M3Pgz5B/Fhl8Z/C7Ar2v4kX800fxQP6sWrw0/bvyZDfyZAPzcD/2Zk/aPpprf6Qb8KP8AfjuH357mN8B/T/0Zqr+fyJ95Nv5Mzj+CX5/yo/lf4N+L6wv8bjW/Ygf8NsY/etD+DPz01J8hfvD/Ofz/SLZfrnP+zJ78mR78D7r/YX4BjvkFEjm/AOYf4Jh/oObaaD/VQ+3PUPt0kvNnML8Bx/wGVTm/AbwVjvabL9tvG/JnBvA/aH6EBvkfz/A/aP4Eh/yZOvwPml/BNv4MtR83xp+h/Kakpv0Zmr8B8zuk/gzN74D5M879j95K+x/Uf4X5I1J/hvoPMD8Hnw7hfxzBt7IcbTnaz6n/sRg9Vnlnp/2Z/QD8FtqfOZI/cwS/2PAjf4bmv8D8GKk/Q/0js1jzo/67es6fwfwbvNcBPxo/wvwcqT8TgB971PzuDL++5uec4M+EvuK3vdL+zC34bcg/Kmp/xhtpf4ba9+NKll+f/KMS+NH8OY977c/Q/CWh8Wfo/MT8J+f+DOZHYZgf5STnR8H8KQzzp9Tk/Cls9Df+zCP8GfI/aH4M9N8x9N8pf2bzN/5MBH/mwfgzHfgzN9qfkf7HmT8D/+N0of0PzE/DMD9NVfavnfkztvFnhtqfGef9mQ78D8f4M0/wP+rG/2jDn4mNP1OH/zGC//FA/kcn68+0D/BnuPFnmj/iz0TgJ/23myy/2Sbnzwxz/gz4DeKP/JnF5/7MFPxo/iPMj8TQP1kNmh/5M4sx/Jma8WdutT9zxo/8mY7hd+bPXGf9mW0Afkvwu/3IP5L+zMr4M234M8Uf92dqjwvmFuF/XBv/I4H/keT8D5r/CvOLsWRr/Jl6zp9p5vwZ+B+Yf6snxw8wPxcrtOF/dMn/uMr5Myvtz1D+Aeb/+t6f2WX9j7P5zaT/8Q7/o2P8mSn8j6bxP8ifIb+znfNnML8ZW1d/zp/B/Gk1OX+a5MfBb0P85un2peMzkt+ZP7OBPzMw/kzzI3+mnuUXrox/RPxG4Ef5i5gfLfVnaH45zD/HJo8/4h+d8XvL+TN9wy8Cv6Pxj8if4Zof5tfL+DOPOX/mOufPDOF/rOB/UP3vqjXasyP5M/T8xfx/rJDA/6D7H+YHTP0POT/gMOvPYH5GhvHRjP9Rgv9B+SOYn5BhfLUn5yfE/IUM49cTOX8h5jdkmN8wkvMbYv7IjD8D/6N9o/2PuxD+x4v2PzA+nPofdH4+jeF/HOB/LMj/uIA/s4f/QX6nt9H+B/Wfjp/hfxS0/4H5H1l0YfyZ66w/I/2jluH3Dn/mCvxKhp+T5TetGn4M/HpZfttejl+Y4zc1/lEN/DbgR/mfmF+TnR6y/M78GczPyV7Xxp8ZZP2Z7/gdwe89y69g/CPJLzH8pll/BvOPMsw/mvozmJ+UYX7S1J+JW1l/5p38jwn8D2p/YP5YPn+A/0H+tJtof4bGlzYv8D9u4X/I+VM3e9Fgu9T+Rw/+x01J+x+YnzX1Z2h+1gn8j9kG/gflj8fwZ3Zl+B+UH4r5X1N/hvKXMT8sX79r/8N6yvoz04esP4P5Z/m6q/0PzE+b+h/0fFoy+B9P8D+o/oT5bTnmt63K+j3mD+azEfwP6j/F/Lj87aj9mUEr689gfl2O+XUVvwfBbxYbfj3tz0h+t9qfkfzqWX5d8mfeDT/jzxC/MflHruEHf2b3rvl1yJ9pG37kz7wZfvCPBnXNb0z+zK3hR/7Rk+aH+ZE55kc+55c8GH4R+B0NP/KPmuBH7fe+8WdofmbM33zuz2B+bL45wP+g/hfM/5z6H9T+xfzQ3K/B/yB/H/NHc8wfHcn5tzG/NEd+3EnOL82drD+D+ak55qeuyfmpbfJnpvA/aP5qzG/NMb91Tc5vLf2ZGvwPTv5HAf7MFfyPGvL7MH92xp9ZwJ/Zan8G829zzL+d5r9gfu7U/6DnE+bvTv2PI+pPyE/k3iX8D6rfl3ban6H2J9uebjjmB1f+TDHrz2B+7NSfeTX8Opof5ifn3sLwe9D+keR3o/2ZRs4/amp+Z/6MPQK/V+MfEb8D+JH/sZmDXxX8aP51zM+e+jPkJ4yewe8e/MaaHx/k+O1y/HY5frfgd9D8/HvDL9b+DNP8pD9D/DB/ferPPJM/8wZ/Zq39GcyHn/ozXfJnnu4eOPJ70/k3pD/D4X9Q+xfvhzj3ZzCf/7n/8fik/RnP+DOB8WeuPvVnDjn/g96fgfcdcLzvIPU/hi3tz8j86LZV4dMi/A8L138958+Ik36G/siWX3g2/kwI/6NB/sdi2+GBBf+Dxs9PXPsfPfSP4H0TqT9D40ftetafqR/B7037M9I/In9mmfNnGoafB36HnD9D/jnen3DuH+F9H6L9bPwZ4rcy/swE/Dbgt8vxy/tHM8PvpP0j4jdbaH8G+ekc+elJ8JDzj9qG3wH8mpof3uei/CMGfjvtz0h+5M9Q/+f4NevP4P0LHO9fqMn3L1w60zLH+xp68n0NeL8Dx/sdEvl+B7wPgju8Xk7k+yC8m6sLXhz06yf5/gi8b4LjfRORfN/E+u7mluP9FJPgDu+nwPssON5nUZPvs8D7Lzjef5HI91/gfRkc78voyfdl4P0aHO/XOMn3a+B9HBzv4zjJ93Fs6u41x/s7JiHD+zvwvg8unlHzk3zfx/J60+B4P0hPvh8E7xPheJ9IIt8ngvePcLx/xJfvH5lWLqbi/nEoRvJ9JXi/Ccf7daLCBu83qZ46Hsf7UKryfSh4fwrvl4/rnnx/Ct53kfILwA/vZ+F4P0si38+C97lw+xb86H0ueP8Gx/tfTkGD+J00P3pfDN4vw/F+mYl8vwzeR8PxPpqafB8N3l+T8qP317wewC8Ev43hNwK/DvhN5uB3A370Ph28f4dvL8DvlviNBb+nylrzm4HfAvwqxO9Z8zsYfm3ND+974XjfUCTfN4T3E6X86P0w11XBr1sEP3qf0ctb93qzXLLWqbjD+49+/Qsva9r040VM72JadZP0TUnWwcK7k8YD8Y/S2TuX+HZaWfaX3Va8GMRJZ7wYLPf4xVWyXMXJdhxv6IsWu3kvThrDWvcYJwjZWNEgXuFji778tT8yK5T/OeJp6nT7nlm7/kkpsEql2LJ+zW5OQ3znTU1uT5x8sC29bn86Spa7Bb7tj9I367dfVPGn+G3zYxs/t77Zv/2iCvycj+P9apls5Q8txHPfsN0vn8aDeFAbj96348UoRTbDP6+Xs9lyX+nO46Sr4mAi/m7RpsYbSvFazpbJ5W44/O4n1Xi1fc/8ZBF3k8psvFqJVbFZdxFHy5n49kUfRMTGWVhJP/2FVncxiq8Oq+5iM14u6BfK4se7Tfz03hXHbpN+qQjQmlgcz+QuILrs92c7fLDV3Y6X8utFeN49jOe7+WN6kPEZfI+LQ7bYim3pb8ccW1P60aN+fmxzR90PHL/nfXzUU67fH/Phst+dseV4kTts1rdSWX5xOfQs13PdAEdwtdyMt5LPH/Y33/Udv+yV3JJVdgPnt18+CLnfLKcsQoFnOV6p7IbqVHlefXei/Jk/HvgN55vrhoHl+KVyaAdB2f/tl+CbFdgl33ZDz3PcklP6E5fnz+GybLsfD9yPcV30t8uPrhA+3ox749l4e0xP0dW4P+32ZnH6z0HSHZ39U5wrl7he8OFtsotxoiTj0XiRZ23l2X73w404TPGHl6X4RrHT3cwnfz//6HCZ9OPGqrve4QuG3dkmVtFIXA2b2a4vqakfic1O6t1tMj7oFVofFP/oB3/iRBf7QtfBH3/8uom39e5qRfejP8RFsdni+vy//+c/M8dJnIt//frnn/gV8QEmD8rxi48MLLdLH/nzR++mZ6fDByeK2IDciaK3On+OiNvRcoYb1VisUKx1Gy1X4qY1Oqa3ioRuwNF4HuvbRBdv4Lvo9+PNpr4cxOkv4pB3E30dCHjiEggc17I9zwr9sh3/7pRANkz/K/nlsi8uuz/lMawtl9PdKsL5+JT5KvPl7fz53Kfb65FeCfjYpU38XcfTbSvpj58F6HYm9rO7TVe2uYzFKRYDU3qn//7I3y1Wuy1eQfh3B//3gfgl8YGzk+Bs7778dOj8/HmQHu2PzwO1LbnzgS1nx3RX8mfECrfXTeYdjoN4WLY8zxXNUd/zy77XD/r9ge+W4nLciwc98w5H1CNm3c1Gr4a+C083eXh+NRFsWPTJKx9VPaOynK+Wi1huj4MjOT7hA1gfTk6cIimcOd2nfi+pJxmOIf29r75DRBa72Yzuvf/kt51vdsn1y1bJD60gtF3fE6e0rT9vfXMk+1IptC1x8w+9r7+v9M12BFS3ZIu9CW3bKcW/W+HZ9rgl1y45Zc92fMcKy6WPvo7uUOJMPmaPWOyVAz+wQ8tz43hgu33fGfYDezgYuEPfD8P/KNloL35wyCrxbGaOgzxqWFPuiD2Lw/h3B6xkDhit7sMjZp0fAi+wPj4KtJvDcTwbpB9WOzq04uHQtX275/kD33finlVyel1X7LqoBNjl/xDfWRp8sJ+Zd4mqs3Nz2H54anruj++pWN+HO/rZHVGfPvkb44ccUNEUlaD0+SkvVnUhJ/FovNkm+ofisk/vbr/Saffzr2T9EWaPy2QuHsX/fZe0IFMqua44rq7lBq5XCs+hff+jry549QHLd13XCUp+6bvvyvzoi+8S13sQuuI2IC5gcczLPl3vP3gvUUc7CMuuqBmWPPf8s/I2Yvm+54k1/BvngTpIf31a9/ybR4l4MOUeIdlH2mctRWoBUUuxDDy8O9vF/7CS8H72SWruyKpmd7uTu3r+tbq+ueguaBPkhzzP9t2yhQp6aJdQOf/RmGpVxqLZRys6+9bvao2oJPWWPM7+YqYurH9RVH0uvvvds7rt5XdrVD/szlbvXX2y8RitgCd5MVEtSEYqZ6eO+b1MXWgQH0Qrlw5n+kvbtFnwxx92WYDOFKjS2Lg7nRdfRtHUOiu+jNr4uym+jJbwd1N8GbXwd1N8FXX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+var base64Str = 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n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15],y=n*s-r*i,T=n*l-o*i,b=n*c-a*i,x=r*l-o*s,C=r*c-a*s,S=o*c-a*l,A=u*m-d*g,I=u*h-p*g,w=u*v-f*g,P=d*h-p*m,O=d*v-f*m,R=p*v-f*h,M=y*R-T*O+b*P+x*w-C*I+S*A;return M?(M=1/M,e[0]=(s*R-l*O+c*P)*M,e[1]=(o*O-r*R-a*P)*M,e[2]=(m*S-h*C+v*x)*M,e[3]=(p*C-d*S-f*x)*M,e[4]=(l*w-i*R-c*I)*M,e[5]=(n*R-o*w+a*I)*M,e[6]=(h*b-g*S-v*T)*M,e[7]=(u*S-p*b+f*T)*M,e[8]=(i*O-s*w+c*A)*M,e[9]=(r*w-n*O-a*A)*M,e[10]=(g*C-m*b+v*y)*M,e[11]=(d*b-u*C-f*y)*M,e[12]=(s*I-i*P-l*A)*M,e[13]=(n*P-r*I+o*A)*M,e[14]=(m*T-g*x-h*y)*M,e[15]=(u*x-d*T+p*y)*M,e):null}function v(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15];return e[0]=s*(p*v-f*h)-d*(l*v-c*h)+m*(l*f-c*p),e[1]=-(r*(p*v-f*h)-d*(o*v-a*h)+m*(o*f-a*p)),e[2]=r*(l*v-c*h)-s*(o*v-a*h)+m*(o*c-a*l),e[3]=-(r*(l*f-c*p)-s*(o*f-a*p)+d*(o*c-a*l)),e[4]=-(i*(p*v-f*h)-u*(l*v-c*h)+g*(l*f-c*p)),e[5]=n*(p*v-f*h)-u*(o*v-a*h)+g*(o*f-a*p),e[6]=-(n*(l*v-c*h)-i*(o*v-a*h)+g*(o*c-a*l)),e[7]=n*(l*f-c*p)-i*(o*f-a*p)+u*(o*c-a*l),e[8]=i*(d*v-f*m)-u*(s*v-c*m)+g*(s*f-c*d),e[9]=-(n*(d*v-f*m)-u*(r*v-a*m)+g*(r*f-a*d)),e[10]=n*(s*v-c*m)-i*(r*v-a*m)+g*(r*c-a*s),e[11]=-(n*(s*f-c*d)-i*(r*f-a*d)+u*(r*c-a*s)),e[12]=-(i*(d*h-p*m)-u*(s*h-l*m)+g*(s*p-l*d)),e[13]=n*(d*h-p*m)-u*(r*h-o*m)+g*(r*p-o*d),e[14]=-(n*(s*h-l*m)-i*(r*h-o*m)+g*(r*l-o*s)),e[15]=n*(s*p-l*d)-i*(r*p-o*d)+u*(r*l-o*s),e}function y(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8],u=e[9],d=e[10],p=e[11],f=e[12],g=e[13],m=e[14],h=e[15];return(t*i-n*a)*(d*h-p*m)-(t*s-r*a)*(u*h-p*g)+(t*l-o*a)*(u*m-d*g)+(n*s-r*i)*(c*h-p*f)-(n*l-o*i)*(c*m-d*f)+(r*l-o*s)*(c*g-u*f)}function T(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return e[0]===t[0]&&e[1]===t[1]&&e[2]===t[2]&&e[3]===t[3]&&e[4]===t[4]&&e[5]===t[5]&&e[6]===t[6]&&e[7]===t[7]&&e[8]===t[8]&&e[9]===t[9]&&e[10]===t[10]&&e[11]===t[11]&&e[12]===t[12]&&e[13]===t[13]&&e[14]===t[14]&&e[15]===t[15]}function re(e,t){var n=e[0],r=e[1],o=e[2],i=e[3],s=e[4],l=e[5],c=e[6],u=e[7],d=e[8],p=e[9],f=e[10],g=e[11],m=e[12],h=e[13],v=e[14],y=e[15],T=t[0],b=t[1],x=t[2],C=t[3],S=t[4],A=t[5],I=t[6],w=t[7],P=t[8],O=t[9],R=t[10],M=t[11],E=t[12],D=t[13],V=t[14],L=t[15];return Math.abs(n-T)<=a*Math.max(1,Math.abs(n),Math.abs(T))&&Math.abs(r-b)<=a*Math.max(1,Math.abs(r),Math.abs(b))&&Math.abs(o-x)<=a*Math.max(1,Math.abs(o),Math.abs(x))&&Math.abs(i-C)<=a*Math.max(1,Math.abs(i),Math.abs(C))&&Math.abs(s-S)<=a*Math.max(1,Math.abs(s),Math.abs(S))&&Math.abs(l-A)<=a*Math.max(1,Math.abs(l),Math.abs(A))&&Math.abs(c-I)<=a*Math.max(1,Math.abs(c),Math.abs(I))&&Math.abs(u-w)<=a*Math.max(1,Math.abs(u),Math.abs(w))&&Math.abs(d-P)<=a*Math.max(1,Math.abs(d),Math.abs(P))&&Math.abs(p-O)<=a*Math.max(1,Math.abs(p),Math.abs(O))&&Math.abs(f-R)<=a*Math.max(1,Math.abs(f),Math.abs(R))&&Math.abs(g-M)<=a*Math.max(1,Math.abs(g),Math.abs(M))&&Math.abs(m-E)<=a*Math.max(1,Math.abs(m),Math.abs(E))&&Math.abs(h-D)<=a*Math.max(1,Math.abs(h),Math.abs(D))&&Math.abs(v-V)<=a*Math.max(1,Math.abs(v),Math.abs(V))&&Math.abs(y-L)<=a*Math.max(1,Math.abs(y),Math.abs(L))}var oe=T,ae=J;function ie(){var e=new i(9);return i!=Float32Array&&(e[1]=0,e[2]=0,e[3]=0,e[5]=0,e[6]=0,e[7]=0),e[0]=1,e[4]=1,e[8]=1,e}function se(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[4],e[4]=t[5],e[5]=t[6],e[6]=t[8],e[7]=t[9],e[8]=t[10],e}function le(e){var t=new i(9);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t}function ce(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function ue(e,t,n,r,o,a,s,l,c){var u=new i(9);return u[0]=e,u[1]=t,u[2]=n,u[3]=r,u[4]=o,u[5]=a,u[6]=s,u[7]=l,u[8]=c,u}function de(e,t,n,r,o,a,i,s,l,c){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e}function pe(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function fe(e,t){if(e===t){var n=t[1],r=t[2],o=t[5];e[1]=t[3],e[2]=t[6],e[3]=n,e[5]=t[7],e[6]=r,e[7]=o}else e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8];return e}function ge(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=u*i-s*c,p=-u*a+s*l,f=c*a-i*l,g=n*d+r*p+o*f;return g?(g=1/g,e[0]=d*g,e[1]=(-u*r+o*c)*g,e[2]=(s*r-o*i)*g,e[3]=p*g,e[4]=(u*n-o*l)*g,e[5]=(-s*n+o*a)*g,e[6]=f*g,e[7]=(-c*n+r*l)*g,e[8]=(i*n-r*a)*g,e):null}function me(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8];return e[0]=i*u-s*c,e[1]=o*c-r*u,e[2]=r*s-o*i,e[3]=s*l-a*u,e[4]=n*u-o*l,e[5]=o*a-n*s,e[6]=a*c-i*l,e[7]=r*l-n*c,e[8]=n*i-r*a,e}function he(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8];return t*(c*a-i*l)+n*(-c*o+i*s)+r*(l*o-a*s)}function ve(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=n[0],f=n[1],g=n[2],m=n[3],h=n[4],v=n[5],y=n[6],T=n[7],b=n[8];return e[0]=p*r+f*i+g*c,e[1]=p*o+f*s+g*u,e[2]=p*a+f*l+g*d,e[3]=m*r+h*i+v*c,e[4]=m*o+h*s+v*u,e[5]=m*a+h*l+v*d,e[6]=y*r+T*i+b*c,e[7]=y*o+T*s+b*u,e[8]=y*a+T*l+b*d,e}function ye(e,t,n){var 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n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const cs={color:[1,1,1],ambientColor:[1,1,1],diffuseColor:[1,1,1],specularColor:[1,1,1],edgeColor:[0,0,0],ambient:0,diffuse:1,metallic:0,roughness:.6,normalStrength:1,emission:1,baseIOR:1.45,specular:0,specularPower:1,opacity:1,interpolation:is.GOURAUD,representation:as.SURFACE,edgeVisibility:!1,backfaceCulling:!1,frontfaceCulling:!1,pointSize:1,lineWidth:1,lighting:!0,shading:!1,materialName:null};function us(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cs,n),jt.obj(e,t),jt.setGet(e,t,["lighting","interpolation","ambient","diffuse","metallic","roughness","normalStrength","emission","baseIOR","specular","specularPower","opacity","edgeVisibility","lineWidth","pointSize","backfaceCulling","frontfaceCulling","representation","diffuseTexture","metallicTexture","roughnessTexture","normalTexture","ambientOcclusionTexture","emissionTexture"]),jt.setGetArray(e,t,["ambientColor","specularColor","diffuseColor","edgeColor"],3),ls(e,t)}var ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||n[0]!==t.mapperBounds[0]||n[1]!==t.mapperBounds[1]||n[2]!==t.mapperBounds[2]||n[3]!==t.mapperBounds[3]||n[4]!==t.mapperBounds[4]||n[5]!==t.mapperBounds[5]||e.getMTime()>t.boundsMTime.getMTime()){ps("Recomputing bounds..."),t.mapperBounds=n.concat();const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}return t.bounds},e.getMTime=()=>{let e=n.getMTime();if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}if(null!==t.backfaceProperty){const n=t.backfaceProperty.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection(),e.processSelectorPixelBuffers=(e,n)=>{t.mapper&&t.mapper.processSelectorPixelBuffers&&t.mapper.processSelectorPixelBuffers(e,n)}}const gs={mapper:null,property:null,backfaceProperty:null,forceOpaque:!1,forceTranslucent:!1,bounds:[1,-1,1,-1,1,-1]};function ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gs,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["backfaceProperty","forceOpaque","forceTranslucent","mapper"]),fs(e,t)}var hs={newInstance:jt.newInstance(ms,"vtkActor"),extend:ms};const vs={Int8Array:1,Uint8Array:1,Uint8ClampedArray:1,Int16Array:2,Uint16Array:2,Int32Array:4,Uint32Array:4,Float32Array:4,Float64Array:8},ys={VOID:"",CHAR:"Int8Array",SIGNED_CHAR:"Int8Array",UNSIGNED_CHAR:"Uint8Array",UNSIGNED_CHAR_CLAMPED:"Uint8ClampedArray",SHORT:"Int16Array",UNSIGNED_SHORT:"Uint16Array",INT:"Int32Array",UNSIGNED_INT:"Uint32Array",FLOAT:"Float32Array",DOUBLE:"Float64Array"};var Ts={DefaultDataType:ys.FLOAT,DataTypeByteSize:vs,VtkDataTypes:ys};const{vtkErrorMacro:bs}=Kt,{DefaultDataType:xs}=Ts,Cs=1e-6;function Ss(e,t,n){const r=e.length;let o,a,i=Number.MAX_VALUE,s=-Number.MAX_VALUE;for(a=t;a<r;a+=n)if(!Number.isNaN(e[a])){i=e[a],s=i;break}for(;a<r;a+=n)o=e[a],o<i?i=o:o>s&&(s=o);return{min:i,max:s}}function As(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;if(t<0&&n>1){const t=e.length/n,r=new Float64Array(t);for(let o=0,a=0;o<t;++o){for(let t=a+n;a<t;++a)r[o]+=e[a]*e[a];r[o]**=.5}return 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t.size>n*r&&(t.size=n*r,e.dataChange()),!0}t.classHierarchy.push("vtkDataArray"),e.dataChange=()=>{t.ranges=null,e.modified()},e.resize=r=>{n(r);const o=r*e.getNumberOfComponents();return t.size!==o&&(t.size=o,e.dataChange(),!0)},e.initialize=()=>{e.resize(0)},e.getElementComponentSize=()=>t.values.BYTES_PER_ELEMENT,e.getComponent=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return t.values[e*t.numberOfComponents+n]},e.setComponent=(n,r,o)=>{o!==t.values[n*t.numberOfComponents+r]&&(t.values[n*t.numberOfComponents+r]=o,e.dataChange())},e.getValue=n=>{const r=n/t.numberOfComponents,o=n%t.numberOfComponents;return e.getComponent(r,o)},e.setValue=(n,r)=>{const o=n/t.numberOfComponents,a=n%t.numberOfComponents;e.setComponent(o,a,r)},e.getData=()=>t.size===t.values.length?t.values:t.values.subarray(0,t.size),e.getRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,r=n;r<0&&(r=1===t.numberOfComponents?0:t.numberOfComponents);let o=null;return t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents)),o=t.ranges[r],o?(t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple):(o=As(e.getData(),n,t.numberOfComponents),t.ranges[r]=o,t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple)},e.setRange=(e,n)=>{t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents));const r={min:e.min,max:e.max};return t.ranges[n]=r,t.rangeTuple[0]=r.min,t.rangeTuple[1]=r.max,t.rangeTuple},e.setTuple=(e,n)=>{const r=e*t.numberOfComponents;for(let e=0;e<t.numberOfComponents;e++)t.values[r+e]=n[e]},e.setTuples=(e,n)=>{let r=e*t.numberOfComponents;const o=Math.min(n.length,t.size-r);for(let e=0;e<o;)t.values[r++]=n[e++]},e.insertTuple=(r,o)=>(t.size<=r*t.numberOfComponents&&(t.size=(r+1)*t.numberOfComponents,n(r+1)),e.setTuple(r,o),r),e.insertTuples=(r,o)=>{const a=r+o.length/t.numberOfComponents;return t.size<a*t.numberOfComponents&&(t.size=a*t.numberOfComponents,n(a)),e.setTuples(r,o),a},e.insertNextTuple=n=>{const 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0!==arguments[0]?arguments[0]:1;return e*t.numberOfComponents},e.getNumberOfComponents=()=>t.numberOfComponents,e.getNumberOfValues=()=>t.size,e.getNumberOfTuples=()=>t.size/t.numberOfComponents,e.getDataType=()=>t.dataType,e.newClone=()=>Es({empty:!0,name:t.name,dataType:t.dataType,numberOfComponents:t.numberOfComponents}),e.getName=()=>(t.name||(e.modified(),t.name=`vtkDataArray${e.getMTime()}`),t.name),e.setData=(n,r)=>{t.values=n,t.size=n.length,t.dataType=ws(n),r&&(t.numberOfComponents=r),t.size%t.numberOfComponents!=0&&(t.numberOfComponents=1),e.dataChange()},e.getState=()=>{if(t.deleted)return null;const n={...t,vtkClass:e.getClassName()};n.values=Array.from(n.values),delete n.buffer,Object.keys(n).forEach((e=>{n[e]||delete n[e]}));const r={};return Object.keys(n).sort().forEach((e=>{r[e]=n[e]})),r.mtime&&delete r.mtime,r},e.deepCopy=n=>{const r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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r=t.clippingPlanes.indexOf(n);return-1!==r&&(t.clippingPlanes.splice(r,1),e.modified(),!0)},e.getClippingPlanes=()=>t.clippingPlanes,e.setClippingPlanes=t=>{if(t)if(Array.isArray(t)){const n=t.length;for(let r=0;r<n&&r<6;r++)e.addClippingPlane(t[r])}else e.addClippingPlane(t)},e.getClippingPlaneInDataCoords=(e,n,r)=>{const o=t.clippingPlanes,a=e;if(o){const e=o.length;if(n>=0&&n<e){const e=o[n],t=e.getNormal(),i=e.getOrigin(),s=t[0],l=t[1],c=t[2],u=-(s*i[0]+l*i[1]+c*i[2]);return r[0]=s*a[0]+l*a[4]+c*a[8]+u*a[12],r[1]=s*a[1]+l*a[5]+c*a[9]+u*a[13],r[2]=s*a[2]+l*a[6]+c*a[10]+u*a[14],void(r[3]=s*a[3]+l*a[7]+c*a[11]+u*a[15])}}jt.vtkErrorMacro(`Clipping plane index ${n} is out of range.`)}}const Ls={clippingPlanes:[]};var Bs=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ls,n),jt.obj(e,t),jt.algo(e,t,1,0),t.clippingPlanes||(t.clippingPlanes=[]),Vs(e,t)};function Ns(e,t){e.getBounds=()=>(jt.vtkErrorMacro("vtkAbstractMapper3D.getBounds - NOT 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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e=a>-1?a:r;t.interpolateTuple(e,s,r,s,o,i),Gs("Unexpected case in interpolateData")}else t.insertTuples(s.getTuples());else ks("Unhandled case in interpolateData");else if(r<0||o<0||r>s.getNumberOfTuples())e.addArray(s),n.getAttributes(s).forEach((t=>{e.setAttribute(s,t)}));else{const l=s.getNumberOfComponents();let c=s.getNumberOfValues();const u=a>-1?a:r;c<=u*l&&(c=(u+1)*l),t=Ds.newInstance({name:s.getName(),dataType:s.getDataType(),numberOfComponents:l,values:jt.newTypedArray(s.getDataType(),c),size:0}),t.interpolateTuple(u,s,r,s,o,i),e.addArray(t),n.getAttributes(s).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.copyFieldOn=e=>{t.copyFieldFlags[e]=!0},e.copyFieldOff=e=>{t.copyFieldFlags[e]=!1},e.copyAllOn=()=>{t.doCopyAllOn&&!t.doCopyAllOff||(t.doCopyAllOn=!0,t.doCopyAllOff=!1,e.modified())},e.copyAllOff=()=>{!t.doCopyAllOn&&t.doCopyAllOff||(t.doCopyAllOn=!1,t.doCopyAllOff=!0,e.modified())},e.clearFieldFlags=()=>{t.copyFieldFlags={}},e.deepCopy=e=>{t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. The attribute must be a vtkDataArray.`),-1;if(n&&!e.checkNumberOfComponents(n,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1;let i=t[`active${a}`];if(i>=0&&i<t.arrays.length){if(t.arrays[i]===n)return i;e.removeArrayByIndex(i)}return n?(i=e.addArray(n),t[`active${a}`]=i):t[`active${a}`]=-1,e.modified(),t[`active${a}`]},e.getAttributes=t=>n.filter((n=>e[`get${n}`]()===t)),e.setActiveAttributeByName=(t,n)=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e.setActiveAttributeByIndex=(n,o)=>{const a=r(o);if(n>=0&&n<t.arrays.length){if("PEDIGREEIDS"!==a.toUpperCase()){const t=e.getArrayByIndex(n);if(!t.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. Only vtkDataArray subclasses can be set as active attributes.`),-1;if(!e.checkNumberOfComponents(t,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1}return t[`active${a}`]=n,e.modified(),n}return-1===n&&(t[`active${a}`]=n,e.modified()),-1},e.getActiveAttribute=t=>{const n=r(t);return e[`get${n}`]()},e.removeAllArrays=()=>{n.forEach((e=>{t[`active${e}`]=-1})),o.removeAllArrays()},e.removeArrayByIndex=e=>(-1!==e&&n.forEach((n=>{e===t[`active${n}`]?t[`active${n}`]=-1:e<t[`active${n}`]&&(t[`active${n}`]-=1)})),o.removeArrayByIndex(e)),n.forEach((n=>{const r=`active${n}`;e[`get${n}`]=()=>e.getArrayByIndex(t[r]),e[`set${n}`]=t=>e.setAttribute(t,n),e[`setActive${n}`]=t=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e[`copy${n}Off`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!1},e[`copy${n}On`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!0}})),e.initializeAttributeCopyFlags=()=>{t.copyAttributeFlags=[],Object.keys(qs).filter((e=>"ALLCOPY"!==e)).forEach((e=>{t.copyAttributeFlags[qs[e]]=Object.keys($s).filter((e=>"NUM_ATTRIBUTES"!==e)).reduce(((e,t)=>(e[$s[t]]=!0,e)),[])})),t.copyAttributeFlags[qs.COPYTUPLE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.INTERPOLATE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.COPYTUPLE][$s.PEDIGREEIDS]=!1},e.initialize=jt.chain(e.initialize,e.initializeAttributeCopyFlags),t.dataArrays&&Object.keys(t.dataArrays).length&&Object.keys(t.dataArrays).forEach((n=>{t.dataArrays[n].ref||"vtkDataArray"!==t.dataArrays[n].type||e.addArray(Ds.newInstance(t.dataArrays[n]))}));const a=e.shallowCopy;e.shallowCopy=(e,n)=>{a(e,n),t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.shallowCopy(e,n),{data:t}}))},e.initializeAttributeCopyFlags()}const Zs={activeScalars:-1,activeVectors:-1,activeTensors:-1,activeNormals:-1,activeTCoords:-1,activeGlobalIds:-1,activePedigreeIds:-1};function Qs(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zs,n),Hs.extend(e,t,n),jt.setGet(e,t,["activeScalars","activeNormals","activeTCoords","activeVectors","activeTensors","activeGlobalIds","activePedigreeIds"]),t.arrays||(t.arrays={}),Ys(e,t)}var Js={newInstance:jt.newInstance(Qs,"vtkDataSetAttributes"),extend:Qs,...Ks};const el=["pointData","cellData","fieldData"];function tl(e,t){t.classHierarchy.push("vtkDataSet"),el.forEach((e=>{t[e]?t[e]=We(t[e]):t[e]=Js.newInstance()}));const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),el.forEach((n=>{t[n]=Js.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))}}const nl={};function rl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nl,n),jt.obj(e,t),jt.setGet(e,t,el),tl(e,t)}var ol={newInstance:jt.newInstance(rl,"vtkDataSet"),extend:rl,FieldDataTypes:{UNIFORM:0,DATA_OBJECT_FIELD:0,COORDINATE:1,POINT_DATA:1,POINT:2,POINT_FIELD_DATA:2,CELL:3,CELL_FIELD_DATA:3,VERTEX:4,VERTEX_FIELD_DATA:4,EDGE:5,EDGE_FIELD_DATA:5,ROW:6,ROW_DATA:6},FieldAssociations:{FIELD_ASSOCIATION_POINTS:0,FIELD_ASSOCIATION_CELLS:1,FIELD_ASSOCIATION_NONE:2,FIELD_ASSOCIATION_POINTS_THEN_CELLS:3,FIELD_ASSOCIATION_VERTICES:4,FIELD_ASSOCIATION_EDGES:5,FIELD_ASSOCIATION_ROWS:6,NUMBER_OF_ASSOCIATIONS:7}};const al={UNCHANGED:0,SINGLE_POINT:1,X_LINE:2,Y_LINE:3,Z_LINE:4,XY_PLANE:5,YZ_PLANE:6,XZ_PLANE:7,XYZ_GRID:8,EMPTY:9};var il={StructuredType:al};const{StructuredType:sl}=il;var ll={getDataDescriptionFromExtent:function(e){let t=0;for(let n=0;n<3;++n)e[2*n]<e[2*n+1]&&t++;return e[0]>e[1]||e[2]>e[3]||e[4]>e[5]?sl.EMPTY:3===t?sl.XYZ_GRID:2===t?e[0]===e[1]?sl.YZ_PLANE:e[2]===e[3]?sl.XZ_PLANE:sl.XY_PLANE:1===t?e[0]<e[1]?sl.X_LINE:e[2]<e[3]?sl.Y_LINE:sl.Z_LINE:sl.SINGLE_POINT},...il};const{vtkErrorMacro:cl}=jt;function ul(e,t){t.classHierarchy.push("vtkImageData"),e.setExtent=function(){if(t.deleted)return cl("instance deleted - cannot call any method"),!1;for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];const a=1===r.length?r[0]:r;if(6!==a.length)return!1;const i=t.extent.some(((e,t)=>e!==a[t]));return i&&(t.extent=a.slice(),t.dataDescription=ll.getDataDescriptionFromExtent(t.extent),e.modified()),i},e.setDimensions=function(){let n,r,o;if(t.deleted)cl("instance deleted - cannot call any method");else{if(1===arguments.length){const e=arguments.length<=0?void 0:arguments[0];n=e[0],r=e[1],o=e[2]}else{if(3!==arguments.length)return void cl("Bad dimension specification");n=arguments.length<=0?void 0:arguments[0],r=arguments.length<=1?void 0:arguments[1],o=arguments.length<=2?void 0:arguments[2]}e.setExtent(0,n-1,0,r-1,0,o-1)}},e.getDimensions=()=>[t.extent[1]-t.extent[0]+1,t.extent[3]-t.extent[2]+1,t.extent[5]-t.extent[4]+1],e.getNumberOfCells=()=>{const t=e.getDimensions();let n=1;for(let e=0;e<3;e++){if(0===t[e])return 0;t[e]>1&&(n*=t[e]-1)}return n},e.getNumberOfPoints=()=>{const t=e.getDimensions();return t[0]*t[1]*t[2]},e.getPoint=n=>{const r=e.getDimensions();if(0===r[0]||0===r[1]||0===r[2])return cl("Requesting a point from an empty image."),null;const o=new Float64Array(3);switch(t.dataDescription){case al.EMPTY:return null;case al.SINGLE_POINT:break;case al.X_LINE:o[0]=n;break;case al.Y_LINE:o[1]=n;break;case al.Z_LINE:o[2]=n;break;case al.XY_PLANE:o[0]=n%r[0],o[1]=n/r[0];break;case al.YZ_PLANE:o[1]=n%r[1],o[2]=n/r[1];break;case al.XZ_PLANE:o[0]=n%r[0],o[2]=n/r[0];break;case al.XYZ_GRID:o[0]=n%r[0],o[1]=n/r[0]%r[1],o[2]=n/(r[0]*r[1]);break;default:cl("Invalid dataDescription")}const a=[0,0,0];return e.indexToWorld(o,a),a},e.getBounds=()=>e.extentToBounds(e.getSpatialExtent()),e.extentToBounds=e=>ki.transformBounds(e,t.indexToWorld),e.getSpatialExtent=()=>ki.inflate([...t.extent],.5),e.computeTransforms=()=>{w(t.indexToWorld,t.origin),t.indexToWorld[0]=t.direction[0],t.indexToWorld[1]=t.direction[1],t.indexToWorld[2]=t.direction[2],t.indexToWorld[4]=t.direction[3],t.indexToWorld[5]=t.direction[4],t.indexToWorld[6]=t.direction[5],t.indexToWorld[8]=t.direction[6],t.indexToWorld[9]=t.direction[7],t.indexToWorld[10]=t.direction[8],x(t.indexToWorld,t.indexToWorld,t.spacing),h(t.worldToIndex,t.indexToWorld)},e.indexToWorld=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.indexToWorld),n},e.indexToWorldVec3=e.indexToWorld,e.worldToIndex=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.worldToIndex),n},e.worldToIndexVec3=e.worldToIndex,e.indexToWorldBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.indexToWorld,n)},e.worldToIndexBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.worldToIndex,n)},e.onModified(e.computeTransforms),e.computeTransforms(),e.getCenter=()=>ki.getCenter(e.getBounds()),e.computeHistogram=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=[0,0,0,0,0,0];e.worldToIndexBounds(t,r);const o=[0,0,0],a=[0,0,0];ki.computeCornerPoints(r,o,a),ua(o,o),ua(a,a);const i=e.getDimensions();Ea(o,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],o),Ea(a,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],a);const s=i[0],l=i[0]*i[1],c=e.getPointData().getScalars().getData();let u=-1/0,d=1/0,p=0,f=0,g=0;for(let e=o[2];e<=a[2];e++)for(let t=o[1];t<=a[1];t++){let i=o[0]+t*s+e*l;for(let s=o[0];s<=a[0];s++){if(!n||n([s,t,e],r)){const e=c[i];e>u&&(u=e),e<d&&(d=e),p+=e*e,f+=e,g+=1}++i}}const m=g>0?f/g:0,h=g?Math.abs(p/g-m*m):0,v=Math.sqrt(h);return{minimum:d,maximum:u,average:m,variance:h,sigma:v,count:g}},e.computeIncrements=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;const n=[];let r=t;for(let t=0;t<3;++t)n[t]=r,r*=e[2*t+1]-e[2*t]+1;return n},e.computeOffsetIndex=t=>{let[n,r,o]=t;const a=e.getExtent(),i=e.getPointData().getScalars().getNumberOfComponents(),s=e.computeIncrements(a,i);return Math.floor((Math.round(n)-a[0])*s[0]+(Math.round(r)-a[2])*s[1]+(Math.round(o)-a[4])*s[2])},e.getOffsetIndexFromWorld=t=>{const n=e.getExtent(),r=e.worldToIndex(t);for(let e=0;e<3;++e)if(r[e]<n[2*e]||r[e]>n[2*e+1])return cl(`GetScalarPointer: Pixel ${r} is not in memory. Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. Current Scalar numberOfComponents: ${r}`),NaN;const o=e.getOffsetIndexFromWorld(t);return Number.isNaN(o)?o:e.getPointData().getScalars().getComponent(o,n)}}const dl={direction:null,indexToWorld:null,worldToIndex:null,spacing:[1,1,1],origin:[0,0,0],extent:[0,-1,0,-1,0,-1],dataDescription:al.EMPTY};function pl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dl,n),ol.extend(e,t,n),t.direction?Array.isArray(t.direction)&&(t.direction=new Float64Array(t.direction.slice(0,9))):t.direction=pe(new Float64Array(9)),t.indexToWorld=new Float64Array(16),t.worldToIndex=new Float64Array(16),jt.get(e,t,["indexToWorld","worldToIndex"]),jt.setGetArray(e,t,["origin","spacing"],3),jt.setGetArray(e,t,["direction"],9),jt.getArray(e,t,["extent"],6),ul(e,t)}var fl={newInstance:jt.newInstance(pl,"vtkImageData"),extend:pl};const gl={LUMINANCE:1,LUMINANCE_ALPHA:2,RGB:3,RGBA:4};var ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const 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u=(n[1]-n[0])/(r-1),[d,p]=[n[0]-u,n[1]+u],f=d-.5*u,g=1/(p-d+u),m=d,h=(r+1)/(p-d),v=e.getData(),y=e.getNumberOfTuples(),T=e.getNumberOfComponents(),b=t<0||t>=T,x=o[2]<=1?2:3,C=Ds.newInstance({numberOfComponents:x,values:new Float32Array(y*x)}),S=C.getData(),A=[0,0,0];tc(A,r+2,o);let I=0,w=0;const P=[.5,.5,.5];for(let e=0;e<y;++e){let e;if(b){let t=0;for(let e=0;e<T;++e){const n=v[I+e];t+=n*n}e=Math.sqrt(t)}else e=v[I+t];if(I+=T,a&&(e=Dl.applyLogScale(e,n,n)),_a(e))P[0]=A[0],P[1]=A[1],P[2]=A[2];else if(i){let t=(e-m)*h;t<1?t=0:t>r&&(t=r+1),tc(P,t,o)}else{P[1]=.49;const t=(e-f)*g;P[0]=t>1e3?1e3:t<-1e3?-1e3:t}for(let e=0;e<x;++e)S[w++]=P[e]}return nc.set(e,{stringHash:l,textureCoordinates:C}),C}(n,u,a,t.numberOfColorsInRange,t.colorTextureMap.getDimensions(),i,r)},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const 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fc={newInstance:jt.newInstance(pc,"vtkCellArray"),extend:pc,...cc};const{vtkErrorMacro:gc}=jt,mc=[1,-1,1,-1,1,-1];function hc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return gc(`getBounds called on an array with components of\n        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n},e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.deepCopy=e=>{e.initialize(t.points,t.pointsIds)},e.getCellDimension=()=>{},e.intersectWithLine=(e,t,n,r,o,a,i)=>{},e.evaluatePosition=(e,t,n,r,o,a)=>{jt.vtkErrorMacro("vtkCell.evaluatePosition is not implemented.")}}const xc={bounds:[-1,-1,-1,-1,-1,-1],pointsIds:[]};function Cc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xc,n),jt.obj(e,t),t.points||(t.points=Tc.newInstance()),jt.get(e,t,["points","pointsIds"]),bc(e,t)}var Sc={newInstance:jt.newInstance(Cc,"vtkCell"),extend:Cc};function Ac(e,t){t.classHierarchy.push("vtkCellLinks"),e.buildLinks=n=>{const r=n.getPoints().getNumberOfPoints(),o=n.getNumberOfCells(),a=new Uint32Array(r);if(n.isA("vtkPolyData")){for(let t=0;t<o;++t){const{cellPointIds:r}=n.getCellPoints(t);r.forEach((t=>{e.incrementLinkCount(t)}))}e.allocateLinks(r),t.maxId=r-1;for(let 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let n=t;for(t>=e.array.length&&(n+=e.array.length);n>e.array.length;)e.array.push({ncells:0,cells:null});e.array.length=n}(t,t.maxId+1)},e.reset=()=>{t.maxId=-1},e.deepCopy=e=>{t.array=[...e.array],t.extend=e.extend,t.maxId=e.maxId},e.incrementLinkCount=e=>{++t.array[e].ncells},e.allocateLinks=e=>{for(let n=0;n<e;++n)t.array[n].cells=new Array(t.array[n].ncells)},e.insertCellReference=(e,n,r)=>{t.array[e].cells[n]=r}}const Ic={array:null,maxId:0,extend:0};function wc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ic,n),jt.obj(e,t),Ac(e,t)}var Pc={newInstance:jt.newInstance(wc,"vtkCellLinks"),extend:wc};const Oc=0,Rc=1,Mc=2,Ec=3,Dc=4,Vc=5,Lc=6,Bc=7,Nc=9,_c=21,Fc=41,kc=42,Gc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],Uc={getClassNameFromTypeId:function(e){return e<Gc.length?Gc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Gc.findIndex(e)},isLinear:function(e){return e<_c||e===Fc||e===kc},hasSubCells:function(e){return e===Lc||e===Dc||e===Mc}};function zc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Oc},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let r=0;r<n;++r)if(t===e.getCellType(r))return!0;return!1},e.insertNextType=t=>e.insertNextCell(t,-1),e.getCellType=e=>t.typeArray[e],e.reset=()=>{t.maxId=-1},e.deepCopy=n=>{e.allocate(n.getSize(),n.getExtend()),t.typeArray.set(n.getTypeArray()),t.locationArray.set(n.getLocationArray()),t.maxId=n.getMaxId()}}const Wc={size:0,maxId:-1,extend:1e3};function Hc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wc,n),jt.obj(e,t),jt.get(e,t,["size","maxId","extend"]),jt.getArray(e,t,["typeArray","locationArray"]),zc(e,t)}var jc={newInstance:jt.newInstance(Hc,"vtkCellTypes"),extend:Hc,...Uc},Kc={IntersectionState:{NO_INTERSECTION:0,YES_INTERSECTION:1,ON_LINE:2}};const{IntersectionState:$c}=Kc;function qc(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;const o={t:Number.MIN_VALUE,distance:0},a=[];let i;a[0]=n[0]-t[0],a[1]=n[1]-t[1],a[2]=n[2]-t[2];const s=a[0]*(e[0]-t[0])+a[1]*(e[1]-t[1])+a[2]*(e[2]-t[2]),l=jo(a,a);let c=1e-5*s;return 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u=[],d=[],p=Xc(e,n,l,c,u,d);var f;if(i.t=u[0],i.betweenPoints=(f=i.t)>=0&&f<=1,a[0]=d[0],p===$c.YES_INTERSECTION){for(let t=0;t<3;t++)o[t]=l[t]+a[0]*(c[t]-l[t]),s[t]=e[t]+i.t*(n[t]-e[t]);if(Yo(o,s)<=r*r)return i.intersect=1,i}else{let t;if(i.t<0)return t=qc(e,l,c,o),t.distance<=r*r?(i.t=0,i.intersect=1,i.betweenPoints=!0,i):i;if(i.t>1)return t=qc(n,l,c,o),t.distance<=r*r?(i.t=1,i.intersect=1,i.betweenPoints=!0,i):i;if(a[0]<0)return a[0]=0,t=qc(l,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i;if(a[0]>1)return a[0]=1,t=qc(c,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i}return i},e.evaluateLocation=(e,n,r)=>{const o=[],a=[];t.points.getPoint(0,o),t.points.getPoint(1,a);for(let t=0;t<3;t++)n[t]=o[t]+e[0]*(a[t]-o[t]);r[0]=1-e[0],r[1]=e[0]},e.evaluateOrientation=(e,n,r)=>!!t.orientations&&(function(e,t,n,r){var o,i,s,l,c,u=t[0],d=t[1],p=t[2],f=t[3],g=n[0],m=n[1],h=n[2],v=n[3];(i=u*g+d*m+p*h+f*v)<0&&(i=-i,g=-g,m=-m,h=-h,v=-v),1-i>a?(o=Math.acos(i),s=Math.sin(o),l=Math.sin((1-r)*o)/s,c=Math.sin(r*o)/s):(l=1-r,c=r),e[0]=l*u+c*g,e[1]=l*d+c*m,e[2]=l*p+c*h,e[3]=l*f+c*v}(n,t.orientations[0],t.orientations[1],e[0]),r[0]=1-e[0],r[1]=e[0],!0)}const Qc={orientations:null};function Jc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Qc,n),Sc.extend(e,t,n),jt.setGet(e,t,["orientations"]),Zc(e,t)}var eu={newInstance:jt.newInstance(Jc,"vtkLine"),extend:Jc,...Yc,...Kc};function tu(e,t){t.classHierarchy.push("vtkPointSet"),t.points?t.points=We(t.points):t.points=Tc.newInstance(),e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.getBounds=()=>t.points.getBounds(),e.computeBounds=()=>{e.getBounds()};const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),t.points=Tc.newInstance(),t.points.shallowCopy(e.getPoints())}}const nu={};function ru(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nu,n),ol.extend(e,t,n),jt.setGet(e,t,["points"]),tu(e,t)}var ou={newInstance:jt.newInstance(ru,"vtkPointSet"),extend:ru};function au(e,t,n,r){const o=n[0]-t[0],a=n[1]-t[1],i=n[2]-t[2],s=e[0]-t[0],l=e[1]-t[1],c=e[2]-t[2];r[0]=a*c-i*l,r[1]=i*s-o*c,r[2]=o*l-a*s}function iu(e,t,n,r){au(e,t,n,r);const o=Math.sqrt(r[0]*r[0]+r[1]*r[1]+r[2]*r[2]);0!==o&&(r[0]/=o,r[1]/=o,r[2]/=o)}const su={computeNormalDirection:au,computeNormal:iu,intersectWithTriangle:function(e,t,n,r,o,a){let i=arguments.length>6&&void 0!==arguments[6]?arguments[6]:1e-6,s=!1;const l=[],c=[],u=[],d=[],p=[];iu(e,t,n,d),iu(r,o,a,p);const f=-jo(d,e),g=-jo(p,r),m=[jo(p,e)+g,jo(p,t)+g,jo(p,n)+g];if(m[0]*m[1]>i&&m[0]*m[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible buffer.",!1),e.bind=()=>!!o&&(t.context.bindBuffer(n(r),o),!0),e.release=()=>!!o&&(t.context.bindBuffer(n(r),null),!0),e.releaseGraphicsResources=()=>{null!==o&&(t.context.bindBuffer(n(r),null),t.context.deleteBuffer(o),o=null,t.allocatedGPUMemoryInBytes=0)},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getError=()=>i}const Zu={objectType:Xu.ARRAY_BUFFER,context:null,allocatedGPUMemoryInBytes:0};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),jt.obj(e,t),jt.get(e,t,["_openGLRenderWindow","allocatedGPUMemoryInBytes"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu,...qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkOpenGLCellArrayBufferObject"),e.setType($u.ARRAY_BUFFER),e.createVBO=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * 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vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=mg(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.oglObject?.getHandle()&&m?.hash===g)t.colorTexture=m.oglObject;else{t.colorTexture=Ud.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(n*p*3);if(c===og.NEAREST?(t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST)):(t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR)),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader 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Image;n.crossOrigin&&(o.crossOrigin=n.crossOrigin),o.onload=()=>e(o),o.onerror=r,o.src=t}))}};zm("http",(e=>kh));const{vtkDebugMacro:Gh}=jt;function Uh(e,t){t.classHierarchy.push("vtkCamera");const n=new Float64Array(3),r=new Float64Array([0,0,-1]),o=new Float64Array([0,1,0]),a=g(new Float64Array(16)),i=g(new Float64Array(16)),s=new Float64Array(3),l=new Float64Array(3),c=new Float64Array(3),u=g(new Float64Array(16)),p=g(new Float64Array(16)),f=new Float64Array(3),v=new Float64Array(3);function y(){t.viewPlaneNormal[0]=-t.directionOfProjection[0],t.viewPlaneNormal[1]=-t.directionOfProjection[1],t.viewPlaneNormal[2]=-t.directionOfProjection[2]}e.orthogonalizeViewUp=()=>{const n=e.getViewMatrix();t.viewUp[0]=n[4],t.viewUp[1]=n[5],t.viewUp[2]=n[6],e.modified()},e.setPosition=(n,r,o)=>{n===t.position[0]&&r===t.position[1]&&o===t.position[2]||(t.position[0]=n,t.position[1]=r,t.position[2]=o,e.computeDistance(),e.modified())},e.setFocalPoint=(n,r,o)=>{n===t.focalPoint[0]&&r===t.focalPoint[1]&&o===t.focalPoint[2]||(t.focalPoint[0]=n,t.focalPoint[1]=r,t.focalPoint[2]=o,e.computeDistance(),e.modified())},e.setDistance=n=>{if(t.distance===n)return;t.distance=n,t.distance<1e-20&&(t.distance=1e-20,Gh("Distance is set to minimum."));const r=t.directionOfProjection;t.focalPoint[0]=t.position[0]+r[0]*t.distance,t.focalPoint[1]=t.position[1]+r[1]*t.distance,t.focalPoint[2]=t.position[2]+r[2]*t.distance,e.modified()},e.computeDistance=()=>{const e=t.focalPoint[0]-t.position[0],n=t.focalPoint[1]-t.position[1],r=t.focalPoint[2]-t.position[2];if(t.distance=Math.sqrt(e*e+n*n+r*r),t.distance<1e-20){t.distance=1e-20,Gh("Distance is set to minimum.");const e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let 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Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId 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pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an 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t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const 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o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const Mv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Ev(e,t){let n=arguments.length>2&&void 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let 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n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = 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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],D=r[2],E=Math.hypot(R,M,D);return E<a?null:(R*=E=1/E,M*=E,D*=E,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+D*o,C=D*R*s-M*o,S=R*M*s-D*o,A=M*M*s+i,I=D*M*s+R*o,w=R*D*s+M*o,P=M*D*s-R*o,O=D*D*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),E(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,D=(1-(p+v))*C,E=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=D,e[6]=E,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+D*I+L*w),e[14]=n[2]+w-(R*A+E*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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Qi(e,t){t.classHierarchy.push("vtkProp3D"),e.addPosition=n=>{t.position=t.position.map(((e,t)=>e+n[t])),e.modified()},e.getOrientationWXYZ=()=>{const e=Ui();N(e,t.rotation);const n=new Float64Array(3),r=function(e,t){var n=2*Math.acos(t[3]),r=Math.sin(n/2);return r>a?(e[0]=t[0]/r,e[1]=t[1]/r,e[2]=t[2]/r):(e[0]=1,e[1]=0,e[2]=0),n}(n,e);return[Ro(r),n[0],n[1],n[2]]},e.rotateX=n=>{0!==n&&(S(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateY=n=>{0!==n&&(A(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateZ=n=>{0!==n&&(I(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateWXYZ=(n,r,o,a)=>{if(0===n||0===r&&0===o&&0===a)return;const i=Oo(n),s=Ui();zi(s,[r,o,a],i);const l=new 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Es,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&t.getDataType()===Tl.UNSIGNED_CHAR||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Es.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Es.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Es.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function Dl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var El={newInstance:jt.newInstance(Dl,"vtkLookupTable"),extend:Dl};let Vl=1,Ll=0;const Bl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function Nl(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;Ll=e}var _l={getResolveCoincidentTopologyAsString:function(){return Bl[Ll]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Vl},getResolveCoincidentTopology:function(){return Ll},setResolveCoincidentTopology:Nl,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){Vl=e},setResolveCoincidentTopologyToDefault:function(){Nl(0)},setResolveCoincidentTopologyToOff:function(){Nl(0)},setResolveCoincidentTopologyToPolygonOffset:function(){Nl(1)}};function Fl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=(e,r)=>{t[n.key]={factor:e,offset:r}}}))}const kl=["Polygon","Line","Point"],Gl={};Fl(Gl,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},kl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var Ul={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(_l).forEach((t=>{e[t]=_l[t]})),Object.keys(Gl).forEach((t=>{e[t]=Gl[t]})),Fl(e,t.topologyOffset,kl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Gl.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Gl,otherStaticMethods:_l,CATEGORIES:kl};const zl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Wl={PassTypes:zl};const{FieldAssociations:Hl}=ol,{staticOffsetAPI:jl,otherStaticMethods:Kl}=Ul,{ColorMode:$l,ScalarMode:ql,GetArray:Xl}=hl,{VectorMode:Yl}=ml,{VtkDataTypes:Zl}=Es;function Ql(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function Jl(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString($l,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(ql,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===ql.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===ql.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===ql.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===ql.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_FIELD_DATA){const t=e.getFieldData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==a){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(n))e.mapScalarsToTexture(o,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},e.scalarToTextureCoordinate=(e,t,n)=>{let r=.5,o=1;return _a(e)||(o=.49,r=(e-t)*n,r>1e3?r=1e3:r<-1e3&&(r=-1e3)),{texCoordS:r,texCoordT:o}},e.createColorTextureCoordinates=(t,n,r,o,a,i,s,l,c)=>{const u=(i[1]-i[0])/l,d=[];d[0]=i[0]-u,d[1]=i[1]+u;const p=1/(d[1]-d[0]),f=n.getData(),g=t.getData();let m=0,h=0;if(a<0||a>=o)for(let t=0;t<r;++t){let t=0;for(let e=0;e<o;++e)t+=g[m]*g[m],m++;let n=Math.sqrt(t);c&&(n=El.applyLogScale(n,s,i));const r=e.scalarToTextureCoordinate(n,d[0],p);f[h]=r.texCoordS,f[h+1]=r.texCoordT,h+=2}else{m+=a;for(let t=0;t<r;++t){let t=g[m];c&&(t=El.applyLogScale(t,s,i));const n=e.scalarToTextureCoordinate(t,d[0],p);f[h]=n.texCoordS,f[h+1]=n.texCoordT,h+=2,m+=o}}},e.mapScalarsToTexture=(n,r)=>{const o=t.lookupTable.getRange(),a=t.lookupTable.usingLogScale();a&&El.getLogRange(o,o);const i=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==r){t.lookupTable.setAlpha(r),t.colorTextureMap=null,t.lookupTable.build();let e=t.lookupTable.getNumberOfAvailableColors();e>4094&&(e=4094),e<64&&(e=64),e+=2;const n=(o[1]-o[0])/(e-2),s=new Float64Array(2*e);for(let t=0;t<e;++t)s[t]=o[0]+t*n-n/2,a&&(s[t]=10**s[t]);for(let t=0;t<e;++t)s[t+e]=NaN;t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setExtent(0,e-1,0,1,0,0);const l=Es.newInstance({numberOfComponents:1,values:s});t.colorTextureMap.getPointData().setScalars(t.lookupTable.mapScalars(l,t.colorMode,0)),t.lookupTable.setAlpha(i)}if(!t.colorCoordinates||e.getMTime()>t.colorCoordinates.getMTime()||e.getInputData(0).getMTime()>t.colorCoordinates.getMTime()||t.lookupTable.getMTime()>t.colorCoordinates.getMTime()){t.colorCoordinates=null;const r=n.getNumberOfComponents(),i=n.getNumberOfTuples();t.colorCoordinates=Es.newInstance({numberOfComponents:2,values:new Float32Array(2*i)});let s=t.lookupTable.getVectorComponent();t.lookupTable.getVectorMode()===Yl.MAGNITUDE&&n.getNumberOfComponents()>1&&(s=-1),e.createColorTextureCoordinates(n,t.colorCoordinates,i,r,s,o,t.lookupTable.getRange(),t.colorTextureMap.getPointData().getScalars().getNumberOfTuples()/2-2,a)}},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=n=>{if(!t.interpolateScalarsBeforeMapping)return!1;if(t.lookupTable&&t.lookupTable.getIndexedLookup())return!1;const r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName),o=r.scalars;return!(!o||r.cellFlag||t.colorMode===$l.DEFAULT&&o.getDataType()===Zl.UNSIGNED_CHAR||t.colorMode===$l.DIRECT_SCALARS)},e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Ql("AcquireInvertibleLookupTable"),e.valueToColor=Ql("ValueToColor"),e.colorToValue=Ql("ColorToValue"),e.useInvertibleColorFor=Ql("UseInvertibleColorFor"),e.clearInvertibleColor=Ql("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(zl.ID_LOW24),o=e.getRawPixelBuffer(zl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===Hl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===Hl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===zl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(zl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===zl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(zl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const ec={colorMapColors:null,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function tc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ec,n),Fs(e,t,n),jt.get(e,t,["colorCoordinates","colorMapColors","colorTextureMap","selectionWebGLIdsToVTKIds"]),jt.setGet(e,t,["colorByArrayName","arrayAccessMode","colorMode","fieldDataTupleId","interpolateScalarsBeforeMapping","lookupTable","populateSelectionSettings","renderTime","scalarMode","scalarVisibility","static","useLookupTableScalarRange","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),Ul.implementCoincidentTopologyMethods(e,t),Jl(e,t)}var nc={newInstance:jt.newInstance(tc,"vtkMapper"),extend:tc,...jl,...Kl,...hl};function rc(e){let t=0;return e.filter(((e,n)=>n===t&&(t+=e+1,!0)))}function oc(e){let t=0;for(let n=0;n<e.length;)n+=e[n]+1,t++;return t}const ac={extractCellSizes:rc,getNumberOfCells:oc};function ic(e,t){t.classHierarchy.push("vtkCellArray");const n={...e};e.getNumberOfCells=n=>void 0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=oc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=rc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function sc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function lc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Es.extend(e,t,sc(n)),ic(e,t)}var cc={newInstance:jt.newInstance(lc,"vtkCellArray"),extend:lc,...ac};const{vtkErrorMacro:uc}=jt,dc=[1,-1,1,-1,1,-1];function pc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return uc(`getBounds called on an array with components of\n        ${e.getNumberOfComponents()}`),dc;const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);return t.bounds[2]=r[0],t.bounds[3]=r[1],t.bounds[4]=0,t.bounds[5]=0,t.bounds},e.computeBounds=e.getBounds,e.setNumberOfComponents(t.numberOfComponents<2?3:t.numberOfComponents)}const fc={empty:!0,numberOfComponents:3,dataType:ys.FLOAT,bounds:[1,-1,1,-1,1,-1]};function gc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fc,n),Es.extend(e,t,n),pc(e,t)}var mc={newInstance:jt.newInstance(gc,"vtkPoints"),extend:gc};function hc(e,t){t.classHierarchy.push("vtkCell"),e.initialize=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n){t.pointsIds=n;let r=t.points.getData();r.length!==3*t.pointsIds.length&&(r=jt.newTypedArray(e.getDataType(),3*t.pointsIds.length));const o=e.getData();t.pointsIds.forEach(((e,t)=>{let n=3*e,a=3*t;r[a]=o[n],r[++a]=o[++n],r[++a]=o[++n]})),t.points.setData(r)}else{t.points=e,t.pointsIds=new 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let 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Ac=0,Ic=1,wc=2,Pc=3,Oc=4,Rc=5,Mc=6,Dc=7,Ec=9,Vc=21,Lc=41,Bc=42,Nc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],_c={getClassNameFromTypeId:function(e){return e<Nc.length?Nc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Nc.findIndex(e)},isLinear:function(e){return e<Vc||e===Lc||e===Bc},hasSubCells:function(e){return e===Mc||e===Oc||e===wc}};function Fc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Ac},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let 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e=P[2];P[2]=P[D],P[D]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function ou(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(nu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=Yc.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),tu(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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su={newInstance:jt.newInstance(iu,"vtkTriangle"),extend:iu,...ru};const lu=["verts","lines","polys","strips"],{vtkWarningMacro:cu}=jt,uu={[Pc]:Yc,[Oc]:Yc,[Rc]:su};function du(e,t){t.classHierarchy.push("vtkPolyData"),lu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=cc.newInstance()})),e.getNumberOfCells=()=>lu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),lu.forEach((n=>{t[n]=cc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?wc:Ic,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Oc:Pc,1===t&&cu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. 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o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||uu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const pu={};function fu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pu,n),eu.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),du(e,t)}var gu={newInstance:jt.newInstance(fu,"vtkPolyData"),extend:fu};function mu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const hu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function vu(e,t){let n=arguments.length>2&&void 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Tu={newInstance:jt.newInstance(vu,"vtkTexture"),extend:vu,...yu};const bu=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],xu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],Cu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Su=[0,1,0,1,0,1,0,1,2,2,2,2],Au=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Iu=new Float64Array(3),wu=new Float64Array(3),Pu=new Float64Array(3),Ou=new Float64Array(3),Ru=new Float64Array(3),Mu=new Float64Array(3),Du=new Float64Array(16);function Eu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function Vu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],Vn(Pu,Iu,r),Pu[0]+=.1,Vn(wu,Pu,o),Pn(Ru,wu,Iu),Pu[0]-=.1,Pu[1]+=.1,Vn(wu,Pu,o),Pn(Mu,wu,Iu);for(let e=0;e<3;e++)Ru[e]/=.05*u[0],Mu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],a[0]<-.5?On(Pu,Ru,a[0]*i-l.width):a[0]>.5?On(Pu,Ru,a[0]*i):On(Pu,Ru,a[0]*i-l.width/2),wn(Iu,Iu,Pu),On(Pu,Mu,a[1]*i-l.height/2),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Pu,Ru,l.width),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Pu,Mu,l.height),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Pu,Ru,l.width),Pn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Du,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Iu[0]=f[3*u],Iu[1]=f[3*u+1],Iu[2]=f[3*u+2],Vn(Pu,Iu,n),Iu[0]=f[3*u+3],Iu[1]=f[3*u+4],Iu[2]=f[3*u+5],Vn(Ou,Iu,n),Pn(Pu,Pu,Ou);const r=[Pu[0],Pu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const Lu=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Vu(e,t)}),"vtkCubeAxesActorHelper");function Bu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Iu[s]=t.dataBounds[a]-.1*r*bu[a][s],Iu[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Iu[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Pu,Iu,e),Iu[s]=t.dataBounds[a],Vn(Ou,Iu,e),Pn(Pu,Ou,Pu),Mn(Pu,Pu),i=Pu[2]>o,t.camera.getParallelProjection()||(Mn(Ou,Ou),i=Dn(Ou,Pu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Au[t][0]].length+2*r[Au[t][1]].length,a+=r[Au[t][0]].length+r[Au[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let 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e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[xu[e][t]]++;const a=[],i=[];for(let e=0;e<3;e++){const n=go().domain([t.dataBounds[2*e],t.dataBounds[2*e+1]]);a[e]=n.ticks(5);const r=n.tickFormat(5);i[e]=a[e].map(r)}e.updatePolyData(r,n,a),e.updateTextData(r,n,a,i),(o||t.forceUpdate)&&e.updateTextureAtlas(i)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Eu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Eu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Eu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Nu(e){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:12,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},...e}}function _u(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nu(n)),hs.extend(e,t,n),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Tu.newInstance(),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=nc.newInstance(),t.polyData=gu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=gu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Bu(e,t)}var Fu={newInstance:jt.newInstance(_u,"vtkCubeAxesActor"),extend:_u,newCubeAxesActorHelper:Lu};function ku(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Gu={};const Uu=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gu,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Fu.newCubeAxesActorHelper(),ku(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Uu);const zu={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var Wu={ObjectType:zu};const{ObjectType:Hu}=Wu;function ju(e,t){function n(e){switch(e){case Hu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Hu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Hu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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L=a.vertexOffset;f=function(e){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;)C(A[e],A,e+1),e+=A[e]+1,T++;return t.elementCount=w,e.upload(O,zu.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,zu.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):Xu("Wrong type for coordScale, expected vec3 or null"):Xu("Wrong type for coordShift, expected vec3 or null")}}const Zu={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),qu.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ed(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const nd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),td(e,t)}var od={newInstance:jt.newInstance(rd,"vtkShader"),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ad(`Links failed: ${t.error}`),0):(ad(t.error),0):(ad(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.fragmentShader.getError()),0):(ad(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ad(`Error linking 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No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . 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No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ad("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ad("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const sd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=od.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=od.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=od.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShaderProgram"),extend:ld,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function ud(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==zu.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const dd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function pd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),ud(e,t)}var fd={newInstance:jt.newInstance(pd,"vtkOpenGLVertexArrayObject"),extend:pd};const gd={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function md(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===gd.Points?t.context.POINTS:e===rs.WIREFRAME||n===gd.Lines||n===gd.TrisEdges||n===gd.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===gd.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===gd.Points?2:t.primitiveType===gd.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const hd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=cd.newInstance(),t.VAO=fd.newInstance(),t.CABO=Ju.newInstance(),md(e,t)}var yd={newInstance:jt.newInstance(vd),extend:vd,primTypes:gd};const Td={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},bd={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var xd={Wrap:Td,Filter:bd};const Cd=new Float32Array(1),Sd=new Int32Array(Cd.buffer);var Ad={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Cd[0]=e;const t=Sd[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Id,Filter:wd}=xd,{VtkDataTypes:Pd}=Es,{vtkDebugMacro:Od,vtkErrorMacro:Rd,vtkWarningMacro:Md}=Kt,{toHalf:Dd}=Ad;function Ed(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Pd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Pd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Dd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Ad.toHalf(Ad.fromHalf(e[u][l+s+t])*h*(1-v)+Ad.fromHalf(e[u][l+p+t])*h*v+Ad.fromHalf(e[u][c+s+t])*m*(1-v)+Ad.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Pd.UNSIGNED_SHORT&&e!==Pd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent()),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(wd.LINEAR),e.setMagnificationFilter(wd.LINEAR)):(e.setMinificationFilter(wd.NEAREST),e.setMagnificationFilter(wd.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Id.REPEAT),e.setWrapS(Id.REPEAT),e.setWrapT(Id.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Md("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Od(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Md("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Od("Unsupported internal texture type!"),Od(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Pd.SHORT:case t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Pd.FLOAT:case Pd.VOID:default:return t.context.FLOAT}switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Pd.FLOAT:case Pd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case wd.NEAREST:return t.context.NEAREST;case wd.LINEAR:return t.context.LINEAR;case wd.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case wd.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case wd.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case wd.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Id.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Id.REPEAT:return t.context.REPEAT;case Id.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Pd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Rd("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Pd.UNSIGNED_CHAR),e.getInternalFormat(Pd.UNSIGNED_CHAR,4),e.getFormat(Pd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Pd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Pd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Es.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Es.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Pd.FLOAT||t.useHalfFloat&&(u===Pd.SHORT||u===Pd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Pd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Pd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Pd.UNSIGNED_CHAR;if(u===Pd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Pd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Pd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Pd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Pd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Vd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Id.CLAMP_TO_EDGE,wrapT:Id.CLAMP_TO_EDGE,wrapR:Id.CLAMP_TO_EDGE,minificationFilter:wd.NEAREST,magnificationFilter:wd.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function Ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),Ed(e,t)}const Bd=Et(Ld,"vtkOpenGLTexture");var Nd={newInstance:Bd,extend:Ld,...xd};rn("vtkTexture",Bd);var _d="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Fd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",kd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=cd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},Gd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=cd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Ud}=ol,{primTypes:zd}=yd,{Representation:Wd,Shading:Hd}=ds,{ScalarMode:jd}=nc,{Filter:Kd,Wrap:$d}=Nd,{vtkErrorMacro:qd}=Kt,Xd={type:"StartEvent"},Yd={type:"EndEvent"},{CoordinateSystem:Zd}=Zi;function Qd(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=zd.Start;e<zd.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=cd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?t.renderable.getInterpolateScalarsBeforeMapping()&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=cd.substitute(i,"//VTK::Color::Impl",c).result):i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=cd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=cd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=cd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:qd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=cd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=cd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=cd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=cd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=cd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=cd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=cd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()||o.getEdgeVisibility()&&o.getRepresentation()===Wd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===zd.Points||o.getRepresentation()===Wd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===zd.Lines||o.getRepresentation()===Wd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==zd.Tris&&e!==zd.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==zd.TrisEdges&&e!==zd.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=cd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==zl.ID_LOW24&&t.lastSelectionState!==zl.ID_HIGH24||(a=cd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=cd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=cd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=cd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case zl.ID_LOW24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case zl.ID_HIGH24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=cd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Hd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||qd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||qd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||qd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===Zd.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():zl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===Wd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===zl.ID_LOW24||t.lastSelectionState===zl.ID_HIGH24);for(let i=zd.Start;i<zd.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===zd.TrisEdges||i===zd.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(Xd),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(Yd),!t.currentInput)return void qd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==jd.USE_CELL_DATA&&i!==jd.USE_CELL_FIELD_DATA&&i!==jd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===jd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Hd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();if(t.openGLActor.getActiveTextures()||(c=null),t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),t.internalColorTexture||(t.internalColorTexture=Nd.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Kd.NEAREST),e.setMagnificationFilter(Kd.NEAREST),e.setWrapS($d.CLAMP_TO_EDGE),e.setWrapT($d.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const u=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==u){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],d=r.getProperty().getEdgeVisibility()&&l===Wd.SURFACE;for(let e=zd.Start;e<zd.End;e++)e!==zd.TrisEdges&&e!==zd.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):d?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,Wd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Jd={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:zl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ep(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jd,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=zd,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=zd.Start;e<zd.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),Qd(e,t)}const tp=Et(ep,"vtkOpenGLPolyDataMapper");var np={newInstance:tp,extend:ep};rn("vtkMapper",tp);const{ColorMode:rp,ScalarMode:op,GetArray:ap}=hl;function ip(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString(rp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(op,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===op.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===op.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===op.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===op.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_FIELD_DATA){const t=e.getFieldData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const sp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function lp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),ip(e,t)}var cp={newInstance:jt.newInstance(lp,"vtkMapper2D"),extend:lp};const up=1,{primTypes:dp}=yd,{ScalarMode:pp}=cp,{vtkErrorMacro:fp}=Kt,gp={type:"StartEvent"},mp={type:"EndEvent"};function hp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=dp.Start;e<dp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(gp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(mp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==pp.USE_CELL_DATA&&a!==pp.USE_CELL_FIELD_DATA&&a!==pp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===pp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=mc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[dp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[dp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[dp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[dp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=dp.Start;a<dp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=cd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=cd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=cd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=cd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=cd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=cd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=cd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=cd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=cd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||fp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||fp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||fp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===up?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const vp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const yp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vp,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=dp,t.tmpMat4=g(new Float64Array(16));for(let e=dp.Start;e<dp.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),hp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",yp);const{VectorMode:Tp}=Pl;function bp(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function xp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Cp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Sp(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;bp(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,bp(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{bp(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Tp.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Es.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Ap=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=nc.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=gu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Tu.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Sp(e,t)}),"vtkScalarBarActorHelper");function Ip(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(xp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Cp())}}function wp(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Pp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wp(n)),t.autoLayout||(t.autoLayout=xp(0,t)),t.generateTicks||(t.generateTicks=Cp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Ip(e,t)}var Op={newInstance:jt.newInstance(Pp,"vtkScalarBarActor"),extend:Pp,newScalarBarActorHelper:Ap};function Rp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Mp={};const Dp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Mp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Op.newScalarBarActorHelper(),Rp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Dp);const{vtkErrorMacro:Ep}=Kt;function Vp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Es.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ep("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||Ep("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const Lp={context:null};const Bp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lp,n),Zt.extend(e,t,n),t.openGLTexture=Nd.newInstance(),t.tris=yd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Vp(e,t)}));rn("vtkSkybox",Bp);const{FieldAssociations:Np}=ol;function _p(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Fp={fieldAssociation:Np.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function kp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),_p(e,t)}var Gp={newInstance:jt.newInstance(kp,"vtkHardwareSelector"),extend:kp};function Up(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>{const e=[0,0];return null!==t.glFramebuffer&&(e[0]=t.glFramebuffer.width,e[1]=t.glFramebuffer.height),e},e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Nd.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(bd.LINEAR),r.setMagnificationFilter(bd.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const zp={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function Wp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zp,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Up(e,t)}var Hp={newInstance:Et(Wp,"vtkFramebuffer"),extend:Wp};function jp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Kp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function $p(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),jp(e,t)}var qp={newInstance:jt.newInstance($p,"vtkSelectionNode"),extend:$p,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:Xp}=Wl,{SelectionContent:Yp,SelectionField:Zp}=qp,{FieldAssociations:Qp}=ol,{vtkErrorMacro:Jp}=jt;function ef(e){return`${e.propID} ${e.compositeID}`}function tf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function nf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function rf(e,t){let n=t;return n<<=24,n|=e,n}function of(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=nf(n[0],n[1],e.pixBuffer[Xp.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=nf(n[0],n[1],e.pixBuffer[Xp.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[Xp.ID_LOW24]&&0===tf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area))return a;const s=nf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area),l=nf(n[0],n[1],e.pixBuffer[Xp.ID_HIGH24],e.area);return a.attributeID=rf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=of(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=of(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=of(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function af(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=qp.newInstance();switch(l.setContentType(Yp.INDICES),e){case Qp.FIELD_ASSOCIATION_CELLS:l.setFieldType(Zp.CELL);break;case Qp.FIELD_ASSOCIATION_POINTS:l.setFieldType(Zp.POINT);break;default:Jp("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function sf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=of(e,[n,t],0,u);if(r&&r.valid){const t=ef(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return af(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function lf(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e[0]!==n[0]||e[1]!==n[1]?(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer()):t.framebuffer.bind()}else t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return sf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return Jp("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=Xp.MIN_KNOWN_PASS;t.currentPass<=Xp.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===Xp.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===Xp.ID_HIGH24){if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===Xp.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=nf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===Xp.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===Xp.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(Xp,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=nf(e[0],e[1],t.pixBuffer[Xp.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=nf(e[0],e[1],t.pixBuffer[Xp.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[Xp.ID_LOW24]&&0===tf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area))return a;const s=nf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area),l=nf(e[0],e[1],t.pixBuffer[Xp.ID_HIGH24],t.area);return a.attributeID=rf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=ef(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return af(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const cf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cf,n),Gp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),lf(e,t)}var df={newInstance:jt.newInstance(uf,"vtkOpenGLHardwareSelector"),extend:uf,...Wl};const{vtkErrorMacro:pf}=Kt,{Representation:ff}=ds,{ObjectType:gf}=qu,{PassTypes:mf}=df,hf={type:"StartEvent"},vf={type:"EndEvent"};function yf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(hf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(vf),!t.currentInput)return void pf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=cd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=cd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=cd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=cd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=cd.substitute(o,"//VTK::Color::Impl",s).result,o=cd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=cd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=cd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=cd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=cd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===ff.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===mf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||pf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||pf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=qu.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=qu.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=qu.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=qu.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,gf.ARRAY_BUFFER),t.normalBuffer.upload(n,gf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),gf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,gf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Tf={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const bf=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tf,n),np.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),yf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",bf);const{vtkErrorMacro:xf}=jt;class Cf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Sf(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void xf("Invalid or missing input");const r=gu.newInstance();r.shallowCopy(n);const o=new Cf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(cc.newInstance({values:c,name:"faces"})),t[0]=r}}const Af={};function If(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),jt.obj(e,t),jt.algo(e,t,1,1),Sf(e,t)}var wf={newInstance:jt.newInstance(If,"vtkClosedPolyLineToSurfaceFilter"),extend:If};const{vtkErrorMacro:Pf}=Kt;function Of(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Pf("Invalid or missing input");if(!t.cutFunction)return void Pf("Missing cut function");const o=gu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const 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o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Rf={cutFunction:null,cutScalars:null,cutValue:0};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Of(e,t)}var Df={newInstance:Et(Mf,"vtkCutter"),extend:Mf};const Ef=e=>e,Vf=1e-6;class Lf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:Ef}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=Dn(n,r);return a>=1||(En(this.tmp,n,r),Sn(this.tmp)<Vf&&(En(this.tmp,[1,0,0],e),Sn(this.tmp)<Vf&&En(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Bf=function(){return new Lf(!0)},Nf=function(){return new Lf(!1)};const _f=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Ff=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function kf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=gu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Es.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Es.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Bf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Nf().translate(...t.center).apply(o),t.matrix){Nf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Nf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const Gf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=cc.newInstance({values:Uint16Array.from(Ff)}),t._lineCells=cc.newInstance({values:Uint16Array.from(_f)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),kf(e,t)}var zf={newInstance:jt.newInstance(Uf,"vtkCubeSource"),extend:Uf};const{vtkErrorMacro:Wf}=jt;function Hf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void Wf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):Wf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const jf={};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=zf.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkImageDataOutlineFilter"),extend:Kf};let qf;function Xf(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>qf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const Yf={preMultiplyFlag:!1,matrix:[...vo]};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),Xf(e,t)}qf=jt.newInstance(Zf,"vtkTransform");var Qf={newInstance:qf,extend:Zf};const Jf={NEAREST:0,LINEAR:1};var eg={InterpolationType:Jf};const{vtkErrorMacro:tg}=Kt;function ng(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function rg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function og(e,t){t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const n=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):tg("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Jf.NEAREST?(t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.openGLTexture.setMinificationFilter(bd.LINEAR),t.openGLTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime(),e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(!r)return;const o=r.getPointData()?.getScalars();if(!o)return;t._scalars!==o&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=o);const a=o.getNumberOfComponents();let i=`${r.getMTime()}A${o.getMTime()}`;const s=t._openGLRenderWindow.getGraphicsResourceForObject(o);if(s?.vtkObj&&s?.hash===i&&t.openGLTextureString===i)t.openGLTexture=s.vtkObj,t.openGLTextureString=s.hash;else{t.openGLTexture||(t.openGLTexture=Nd.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const e=r.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],o),t.openGLTextureString=i,o&&t._openGLRenderWindow.setGraphicsResourceForObject(o,t.openGLTexture,t.openGLTextureString)}const l=n.getProperty(),c=l.getIndependentComponents(),u=c?a:1,d=c?2*u:1,p=l.getRGBTransferFunction();i=ng(l,p,u);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===i&&t.colorTextureString===i)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(e*d*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),p){const r=new Float32Array(3*e);for(let t=0;t<u;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),c)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=i,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}const g=l.getPiecewiseFunction();i=ng(l,g,u);const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);if(m?.vtkObj&&m?.hash===i&&t.pwfTextureString===i)t.pwfTexture=m.vtkObj,t.pwfTextureString=m.hash;else{const e=1024,n=e*d,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),g){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<u;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=i,g&&t._openGLRenderWindow.setGraphicsResourceForObject(g,t.pwfTexture,t.pwfTextureString)}const h=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==h){const e=Es.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Es.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:tg("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=h,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",rg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",rg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=cd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:tg("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=cd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:tg("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=cd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=cd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=cd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`));const t=ue(n?.getDirection());ge(t,t);const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getDimensions(),l=[0,i[0]-1,0,i[1]-1,0,i[2]-1];let c=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[c+a]=r[a],e[c+o[0]]=l[2*o[0]+n],e[c+o[1]]=l[2*o[1]+t],c+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const u=new Uint16Array(8);u[0]=3,u[1]=0,u[2]=1,u[3]=3,u[4]=3,u[5]=0,u[6]=3,u[7]=2;const d=s.getNormal();za.normalize(d);const p=new Float32Array(12);for(let e=0;e<4;++e)p[3*e]=d[0],p[3*e+1]=d[1],p[3*e+2]=d[2];t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(u,1);const f=Es.newInstance({numberOfComponents:3,values:p,name:"Normals"});t.resliceGeom.getPointData().setNormals(f)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=gu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Es.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else tg("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)}}const ag={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,openGLTextureString:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1,_scalars:null};const ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ag,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=$f.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=gu.newInstance(),t.cutter=Df.newInstance(),t.lineToSurfaceFilter=wf.newInstance(),t.transform=Qf.newInstance(),bt(e,t,["openGLTexture"]),og(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",ig);var sg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:lg}=Kt,{SlicingMode:cg}=sg;function ug(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function dg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function pg(e,t){t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(n.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=cd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=cd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:lg("Unsupported number of independent coordinates.")}}if(i=cd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=cd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=cd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=cd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:lg("Unsupported number of independent coordinates.")}i=cd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=cd.substitute(i,"//VTK::TCoord::Impl",[...dg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); \n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (actualThickness == 0) {\n                    gl_FragData[0] = vec4(0.0, 0.0, 1.0, 1.0);\n                    return;\n                  }\n                  if (opacityToUse > 0.01) {\n                    for (int i = -actualThickness; i <= actualThickness; i++) {\n                      for (int j = -actualThickness; j <= actualThickness; j++) {\n                        if (i == 0 || j == 0) {\n                          continue;\n                        }\n                        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                          gl_FragCoord.y + float(j),\n                          gl_FragCoord.z, gl_FragCoord.w);\n                        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                        float value = texture2D(texture1, neighborPosIS.xy).r;\n                        if (value != centerValue) {\n                          pixelOnBorder = true;\n                          break;\n                        }\n                      }\n                      if (pixelOnBorder == true) {\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      gl_FragData[0] = vec4(tColor, outlineOpacity);\n                    }\n                    else {\n                      gl_FragData[0] = vec4(tColor, opacityToUse);\n                    }\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||lg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||lg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):lg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=ug(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.vtkObj&&m?.hash===g&&t.colorTextureString===g)t.colorTexture=m.vtkObj,t.colorTextureString=m.hash;else{const e=1024,n=new Uint8ClampedArray(e*p*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST)):(t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR)),f){const r=new Float32Array(3*e);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),u)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,p,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=g,f&&t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,t.colorTextureString)}const h=l.getPiecewiseFunction(),v=ug(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.vtkObj&&y?.hash===v&&t.pwfTextureString===v)t.pwfTexture=y.vtkObj,t.pwfTextureString=y.hash;else{const e=1024,n=e*p,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),h){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<d;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),u)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,p,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=v,h&&t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,t.pwfTextureString)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===cg.I&&(S=x-C[0]),T===cg.J&&(S=x-C[2]),T!==cg.K&&T!==cg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Nd.newInstance({resizable:!0}),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===Jf.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.NEAREST)):t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(bd.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(bd.LINEAR)),t.openGLTexture.setWrapS(Td.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Td.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[cg.X,cg.Y,cg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===cg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===cg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===cg.K||T===cg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):lg("Reformat slicing not yet supported.");const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);m?.vtkObj?(t.openGLTexture=m.vtkObj,t.VBOBuildString=m.hash):(t._scalars!==g&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=g),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t._openGLRenderWindow.setGraphicsResourceForObject(g,t.openGLTexture,t.VBOBuildString)),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const h=Es.newInstance({numberOfComponents:3,values:r});h.setName("points");const v=Es.newInstance({numberOfComponents:2,values:l});v.setName("tcoords");const y=new Uint16Array(8);y[0]=3,y[1]=0,y[2]=1,y[3]=3,y[4]=3,y[5]=0,y[6]=3,y[7]=2;const C=Es.newInstance({numberOfComponents:1,values:y});t.tris.getCABO().createVBO(C,"polys",rs.SURFACE,{points:h,tcoords:v,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=e=>{t.labelOutlineThicknessTexture||(t.labelOutlineThicknessTexture=Nd.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]}}const fg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,_scalars:null};const gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fg,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),pg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",gg);const{vtkErrorMacro:mg}=jt;function hg(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function vg(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===Jf.NEAREST?(t.volumeTexture.setMinificationFilter(bd.NEAREST),t.volumeTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.volumeTexture.setMinificationFilter(bd.LINEAR),t.volumeTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=hg(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=hg(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Es.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Es.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Es.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Es.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getCenterlineTangentDirections(),r=new Float32Array(3*i),o=new Float32Array(3*i);for(let t=0,a=0;t<n;++t){const n=3*t;for(let t=0;t<4;++t)r[a+0]=e[n+0],r[a+1]=e[n+1],r[a+2]=e[n+2],o[a+0]=e[n+3],o[a+1]=e[n+4],o[a+2]=e[n+5],a+=3}const a=Es.newInstance({numberOfComponents:3,values:r,name:"centerlineTopDirection"}),s=Es.newInstance({numberOfComponents:3,values:o,name:"centerlineBotDirection"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const s=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],l=t.renderable.getUseUniformOrientation();l?s.push("out vec3 centerlineDirVSOutput;","uniform vec3 centerlineDirection;"):s.push("out vec3 centerlineTopDirVSOutput;","out vec3 centerlineBotDirVSOutput;","out float centerlineAngleVSOutput;","attribute vec3 centerlineTopDirection;","attribute vec3 centerlineBotDirection;"),a=cd.substitute(a,"//VTK::Color::Dec",s).result;const c=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];l?c.push("centerlineDirVSOutput = centerlineDirection;"):c.push("vec3 sumVec = centerlineTopDirection + centerlineBotDirection;","float sumLen2 = dot(sumVec, sumVec);","float diffLen2 = 4.0 - sumLen2;","if (diffLen2 < 0.001) {","  // vectors are too close to each other, use lerp","  centerlineAngleVSOutput = -1.0; // use negative angle as a flag for lerp","  centerlineTopDirVSOutput = centerlineTopDirection;","  centerlineBotDirVSOutput = centerlineBotDirection;","} else if (sumLen2 == 0.0) {","  // vector are opposite to each other, don't make a choice for the user","  // use slerp without direction, it will display the centerline color on each row of pixel","  centerlineAngleVSOutput = 0.0;","  centerlineTopDirVSOutput = vec3(0.0);","  centerlineBotDirVSOutput = vec3(0.0);","} else {","  // use slerp","  centerlineAngleVSOutput = 2.0 * atan(sqrt(diffLen2/sumLen2));","  float sinAngle = sin(centerlineAngleVSOutput);","  centerlineTopDirVSOutput = centerlineTopDirection / sinAngle;","  centerlineBotDirVSOutput = centerlineBotDirection / sinAngle;","}"),a=cd.substitute(a,"//VTK::Color::Impl",c).result;const u=t.volumeTexture.getComponents(),d=o.getProperty().getIndependentComponents();let p=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];l?p.push("in vec3 centerlineDirVSOutput;"):p.push("in vec3 centerlineTopDirVSOutput;","in vec3 centerlineBotDirVSOutput;","in float centerlineAngleVSOutput;");const f=t.renderable.getCenterPoint();if(f&&p.push("uniform vec3 globalCenterPoint;"),d){for(let e=1;e<u;e++)p=p.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(u){case 1:p=p.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:p=p.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:mg("Unsupported number of independent coordinates.")}}i=cd.substitute(i,"//VTK::TCoord::Dec",p).result;let g=[];if(l?g.push("vec3 interpolatedCenterlineDir = centerlineDirVSOutput;"):g.push("vec3 interpolatedCenterlineDir;","if (centerlineAngleVSOutput < 0.0) {","  // Lerp","  interpolatedCenterlineDir = quadOffsetVSOutput.y * centerlineTopDirVSOutput + (1.0 - quadOffsetVSOutput.y) * centerlineBotDirVSOutput;","} else {","  // Slerp","  float topInterpolationAngle = quadOffsetVSOutput.y * centerlineAngleVSOutput;","  float botInterpolationAngle = centerlineAngleVSOutput - topInterpolationAngle;","  interpolatedCenterlineDir = sin(topInterpolationAngle) * centerlineTopDirVSOutput + sin(botInterpolationAngle) * centerlineBotDirVSOutput;","}","// Slerp should give a normalized vector but when sin(angle) is small, rounding error occurs","// Normalize for both lerp and slerp","interpolatedCenterlineDir = normalize(interpolatedCenterlineDir);"),f?g.push("float baseOffset = dot(interpolatedCenterlineDir, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):g.push("float horizontalOffset = quadOffsetVSOutput.x;"),g.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * interpolatedCenterlineDir;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(volumeTexture, volumePosTC);"),d){const e=["r","g","b","a"];for(let t=0;t<u;++t)g=g.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(u){case 1:g=g.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:g=g.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:mg("Unsupported number of independent coordinates.")}}else switch(u){case 1:g=g.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:g=g.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",g).result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||mg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&n.getProgram().isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),e.name,e.offset,n.getCABO().getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&mg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const a=t.volumeTexture.getTextureUnit();if(n.getProgram().setUniformi("volumeTexture",a),n.getProgram().setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4f("backgroundColor",...t.renderable.getBackgroundColor()),n.getProgram().isUniformUsed("centerlineDirection")){const e=t.renderable.getUniformDirection();n.getProgram().setUniform3fArray("centerlineDirection",e)}if(n.getProgram().isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();n.getProgram().setUniform3fArray("globalCenterPoint",e)}const i=t.currentImageDataInput,s=i.getWorldToIndex(),l=P(new Float32Array(16),Rn([],i.getDimensions())),c=oe(l,l,s);if(n.getProgram().setUniformMatrix("MCTCMatrix",c),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const yg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Tg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),kd(e,t,n),jt.algo(e,t,2,0),t.tris=yd.newInstance(),t.volumeTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.pwfTexture=Nd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),vg(e,t)}),"vtkOpenGLImageCPRMapper");function bg(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Tg);const xg={context:null,keyMatrixTime:null,keyMatrices:null};const Cg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),bg(e,t)}),"vtkOpenGLImageSlice");function Sg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Cg);const Ag={};const Ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ag,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Sg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Ig);const wg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Pg={FRACTIONAL:0,PROPORTIONAL:1};var Og={InterpolationType:wg,OpacityMode:Pg};const Rg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5};var Mg={BlendMode:Rg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Dg,vtkErrorMacro:Eg}=Kt;function Vg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function Lg(e,t){t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(n,r)=>{if(n){t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const n=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera()),e.renderPiece(r,n)}},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define vtkIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n#ifdef vtkImageLabelOutlineOn\n\nuniform float outlineOpacity;\nuniform float vpWidth;\nuniform float vpHeight;\nuniform float vpOffsetX;\nuniform float vpOffsetY;\nuniform mat4 PCWCMatrix;\nuniform mat4 vWCtoIDX;\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents >= 3\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#endif\n#if vtkNumComponents >= 4\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef vtkIndependentComponentsOn\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.a = tmp.g;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n#if defined(vtkGradientOpacityOn)\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n#else\n  return 1.0;\n#endif\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n#ifdef vtkClippingPlanesOn\n  void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n  {\n    vec3 g1VC[3];\n    for (int i = 0; i < 3; ++i)\n    {\n      g1VC[i] = IStoVC(texPos[i]);\n    }\n    vec3 posVC = IStoVC(pos);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      for (int j = 0; j < 3; ++j)\n      {\n        if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n        {\n          g1[j] = 0.0;\n        }\n      }\n    }\n  }\n#endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    #ifdef vtkGradientOpacityOn\n      vec4 computeDensityNormal(float gradientMag, vec3 scalarInterp[2])\n      {\n    #else\n      //if gradient opacity not on but using density gradient\n      vec4 computeDensityNormal(vec3 scalarInterp[2])\n      {\n    #endif\n        vec3 opacityG1, opacityG2;\n        opacityG1.x = texture2D(otexture, vec2(scalarInterp[0].x * oscale0 + oshift0, 0.5)).r;\n        opacityG1.y = texture2D(otexture, vec2(scalarInterp[0].y * oscale0 + oshift0, 0.5)).r;\n        opacityG1.z = texture2D(otexture, vec2(scalarInterp[0].z * oscale0 + oshift0, 0.5)).r;\n        opacityG2.x = texture2D(otexture, vec2(scalarInterp[1].x * oscale0 + oshift0, 0.5)).r;\n        opacityG2.y = texture2D(otexture, vec2(scalarInterp[1].y * oscale0 + oshift0, 0.5)).r;\n        opacityG2.z = texture2D(otexture, vec2(scalarInterp[1].z * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n        float gradOpacityFactor = 1.0f;\n        if (gradientMag >= 0.0){\n          gradOpacityFactor = computeGradientOpacityFactor(gradientMag, goscale0, goshift0, gomin0, gomax0);\n        }\n        opacityG1.xyz *= gradOpacityFactor;\n        opacityG2.xyz *= gradOpacityFactor;\n    #endif\n\n        vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n        // divide by spacing\n        opacityG.xyz /= vSpacing;\n        opacityG.w = length(opacityG.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(opacityG.xyz);\n        if (length(opacityG.xyz) > 0.0) {\n          return vec4(normalize(opacityG.xyz),opacityG.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n\n      vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2])\n      {\n        vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n        vec3 yvec = vec3(0.0, tstep.y, 0.0);\n        vec3 zvec = vec3(0.0, 0.0, tstep.z);\n        vec3 texPosPVec[3];\n        texPosPVec[0] = pos + xvec;\n        texPosPVec[1] = pos + yvec;\n        texPosPVec[2] = pos + zvec;\n        vec3 texPosNVec[3];\n        texPosNVec[0] = pos - xvec;\n        texPosNVec[1] = pos - yvec;\n        texPosNVec[2] = pos - zvec;\n        vec3 g1, g2;\n\n        scalarInterp[0].x = getTextureValue(texPosPVec[0]).a;\n        scalarInterp[0].y = getTextureValue(texPosPVec[1]).a;\n        scalarInterp[0].z = getTextureValue(texPosPVec[2]).a;\n        scalarInterp[1].x = getTextureValue(texPosNVec[0]).a;\n        scalarInterp[1].y = getTextureValue(texPosNVec[1]).a;\n        scalarInterp[1].z = getTextureValue(texPosNVec[2]).a;\n\n        #ifdef vtkClippingPlanesOn\n          adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n          adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n        #endif\n        vec4 result;\n        result.x = scalarInterp[0].x - scalarInterp[1].x;\n        result.y = scalarInterp[0].y - scalarInterp[1].y;\n        result.z = scalarInterp[0].z - scalarInterp[1].z;\n        // divide by spacing\n        result.xyz /= vSpacing;\n        result.w = length(result.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(result.xyz);\n        if (length(result.xyz) > 0.0) {\n          return vec4(normalize(result.xyz),result.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n  #endif\n\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n#ifdef vtkImageLabelOutlineOn\nvec3 fragCoordToIndexSpace(vec4 fragCoord) {\n  vec4 pcPos = vec4(\n    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n    (fragCoord.z - 0.5) * 2.0,\n    1.0);\n\n  vec4 worldCoord = PCWCMatrix * pcPos;\n  vec4 vertex = (worldCoord/worldCoord.w);\n\n  vec3 index = (vWCtoIDX * vertex).xyz;\n\n  // half voxel fix for labelmapOutline\n  return (index + vec3(0.5)) / vec3(volumeDimensions);\n}\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  // support gradient opacity\n  #ifdef vtkGradientOpacityOn\n    vec4 normal;\n  #endif\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n      normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n//=======================================================================\n// Given a texture value compute the color and opacity\n//\nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n#ifdef vtkImageLabelOutlineOn\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n  if (actualThickness == 0) {\n    return vec4(0, 0, 1, 1);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  if (float(tColor.a) > 0.01) {\n    for (int i = -actualThickness; i <= actualThickness; i++) {\n      for (int j = -actualThickness; j <= actualThickness; j++) {\n        if (i == 0 || j == 0) {\n          continue;\n        }\n\n        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n          gl_FragCoord.y + float(j),\n          gl_FragCoord.z, gl_FragCoord.w);\n\n        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n        vec4 value = getTextureValue(neighborPosIS);\n\n        // If any of my neighbours are not the same value as I\n        // am, this means I am on the border of the segment.\n        // We can break the loops\n        if (any(notEqual(value, centerValue))) {\n          pixelOnBorder = true;\n          break;\n        }\n      }\n\n      if (pixelOnBorder == true) {\n        break;\n      }\n    }\n\n    // If I am on the border, I am displayed at full opacity\n    if (pixelOnBorder == true) {\n      tColor.a = outlineOpacity;\n    }\n  }\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n  //\n  vec4 goFactor = vec4(1.0,1.0,1.0,1.0);\n\n  // compute the normal vectors as needed\n  #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #if !defined(vtkComponent0Proportional)\n        vec4 normal0 = normalMat[0];\n      #endif\n      #if !defined(vtkComponent1Proportional)\n        vec4 normal1 = normalMat[1];\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          vec4 normal2 = normalMat[2];\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            vec4 normal3 = normalMat[3];\n          #endif\n        #endif\n      #endif\n    #else\n      vec4 normalLight;\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp);\n        if (length(normal0)>0.0){\n          #ifdef vtkGradientOpacityOn\n            normalLight = computeDensityNormal(normal0.w, scalarInterp);\n          #else\n            normalLight = computeDensityNormal(scalarInterp);\n          #endif\n          if (length(normalLight) == 0.0){\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n        normalLight = normal0;\n      #endif\n    #endif\n  #endif\n\n  // compute gradient opacity factors as needed\n  #if defined(vtkGradientOpacityOn)\n    #if !defined(vtkComponent0Proportional)\n      goFactor.x =\n        computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      #if !defined(vtkComponent1Proportional)\n        goFactor.y =\n          computeGradientOpacityFactor(normal1.a, goscale1, goshift1, gomin1, gomax1);\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          goFactor.z =\n            computeGradientOpacityFactor(normal2.a, goscale2, goshift2, gomin2, gomax2);\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            goFactor.w =\n              computeGradientOpacityFactor(normal3.a, goscale3, goshift3, gomin3, gomax3);\n          #endif\n        #endif\n      #endif\n    #endif\n  #endif\n\n  // single component is always independent\n  #if vtkNumComponents == 1\n    vec4 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5));\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n    if (tColor.a < EPSILON){\n      return vec4(0.0);\n    }\n  #endif\n\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n    vec4 tColor = mix0*texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0));\n    #if !defined(vtkComponent0Proportional)\n      tColor.a = goFactor.x*mix0*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n      tColor *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix0));\n    #endif\n\n    vec3 tColor1 = mix1*texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n    #if !defined(vtkComponent1Proportional)\n      tColor.a += goFactor.y*mix1*texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n      tColor1 *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix1));\n    #endif\n\n    #if vtkNumComponents >= 3\n      vec3 tColor2 = mix2*texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n      #if !defined(vtkComponent2Proportional)\n        tColor.a += goFactor.z*mix2*texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n      #else\n        float pwfValue = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n        tColor2 *= pwfValue;\n        tColor.a *= mix(pwfValue, 1.0, (1.0 - mix2));\n      #endif\n\n      #if vtkNumComponents >= 4\n        vec3 tColor3 = mix3*texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n        #if !defined(vtkComponent3Proportional)\n          tColor.a += goFactor.w*mix3*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #else\n          float pwfValue = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n          tColor3 *= pwfValue;\n          tColor.a *= mix(pwfValue, 1.0, (1.0 - mix3));\n        #endif\n      #endif\n    #endif\n  #else // then not independent\n\n  #if vtkNumComponents == 2\n    float lum = tValue.r * cscale0 + cshift0;\n    float alpha = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    vec4 tColor = vec4(lum, lum, lum, alpha);\n  #endif\n  #if vtkNumComponents == 3\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n  #endif\n  #if vtkNumComponents == 4\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n  #endif\n  #endif // dependent\n\n  // apply lighting if requested as appropriate\n  #if vtkLightComplexity > 0\n    #if !defined(vtkComponent0Proportional)\n      #if vtkNumComponents == 1\n        #ifdef SurfaceShadowOn\n            #if vtkLightComplexity < 3\n                vec3 tColorS = applyLightingDirectional(posIS, tColor, normalLight);\n            #else\n                vec3 tColorS = applyLightingPositional(posIS, tColor, normalLight, IStoVC(posIS));\n            #endif\n        #endif\n\n        #ifdef VolumeShadowOn\n          vec3 tColorVS = applyShadowRay(tColor.rgb, posIS, rayDirVC);\n          #ifdef SurfaceShadowOn\n            float vol_coef = volumetricScatteringBlending * (1.0 - tColor.a / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n            tColor.rgb = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n          #else\n            tColor.rgb = tColorVS;\n          #endif\n        #else\n            tColor.rgb = tColorS;\n        #endif\n\n      #else\n        applyLighting(tColor.rgb, normal0);\n      #endif\n    #endif\n\n    #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n      #if !defined(vtkComponent1Proportional)\n        applyLighting(tColor1, normal1);\n      #endif\n    #if vtkNumComponents >= 3\n      #if !defined(vtkComponent2Proportional)\n        applyLighting(tColor2, normal2);\n      #endif\n    #if vtkNumComponents >= 4\n      #if !defined(vtkComponent3Proportional)\n        applyLighting(tColor3, normal3);\n      #endif\n    #endif\n    #endif\n    #endif\n  #endif\n\n// perform final independent blend as needed\n#if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n  tColor.rgb += tColor1;\n#if vtkNumComponents >= 3\n  tColor.rgb += tColor2;\n#if vtkNumComponents >= 4\n  tColor.rgb += tColor3;\n#endif\n#endif\n#endif\n\n#endif\nreturn tColor;\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents == 2\n     if (val.r >= min.r && val.r <= max.r &&\n        val.g >= min.g && val.g <= max.g) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 3\n    if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n        all(lessThanEqual(val, ipScalarRangeMax))) {\n      withinRange = true;\n    }\n  #endif\n  return withinRange;\n}\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Fragment;o.getProperty().getInterpolationType()===wg.LINEAR&&(a=cd.substitute(a,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===o.getProperty().getUseLabelOutline()&&(a=cd.substitute(a,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result);const i=t.scalarTexture.getComponents();a=cd.substitute(a,"//VTK::NumComponents",`#define vtkNumComponents ${i}`).result;const s=o.getProperty().getIndependentComponents();if(s){a=cd.substitute(a,"//VTK::IndependentComponentsOn","#define vtkIndependentComponentsOn").result;const e=[];for(let t=0;t<i;t++)o.getProperty().getOpacityMode(t)===Pg.PROPORTIONAL&&e.push(`#define vtkComponent${t}Proportional`);e.length>0&&(a=cd.substitute(a,"//VTK::vtkProportionalComponents",e.join("\n")).result)}const l=t.currentInput.getSpatialExtent(),c=t.currentInput.getSpacing(),u=new Float64Array(3);In(u,(l[1]-l[0])*c[0],(l[3]-l[2])*c[1],(l[5]-l[4])*c[2]);const d=Sn(u)/e.getCurrentSampleDistance(r);a=cd.substitute(a,"//VTK::MaximumSamplesValue",`${Math.ceil(d)}`).result,a=cd.substitute(a,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lastLightComplexity}`).result,t.lastLightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(a=cd.substitute(a,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(a=cd.substitute(a,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0&&(a=cd.substitute(a,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result)),t.gopacity=o.getProperty().getUseGradientOpacity(0);for(let e=1;s&&!t.gopacity&&e<i;++e)o.getProperty().getUseGradientOpacity(e)&&(t.gopacity=!0);t.gopacity&&(a=cd.substitute(a,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(a=cd.substitute(a,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(a=cd.substitute(a,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,a=cd.substitute(a,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),a=cd.substitute(a,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=a,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lastLightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=cd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lastLightComplexity&&(o=cd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=cd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=cd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=cd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=cd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{let a=0;o.getProperty().getShade()&&t.renderable.getBlendMode()===Rg.COMPOSITE_BLEND&&(a=0,t.numberOfLights=0,r.getLights().forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===a&&(a=1)),1===a&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(a=2),a<3&&e.getPositional()&&(a=3)})));let i=!1;t.lastLightComplexity!==a&&(t.lastLightComplexity=a,i=!0);const s=t.scalarTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=!1;const u=[];if(l){for(let e=0;e<s;e++)u.push(o.getProperty().getOpacityMode(e));u.length>0&&(c=!0)}const d=t.currentInput.getSpatialExtent(),p=t.currentInput.getSpacing(),f=new Float64Array(3);In(f,(d[1]-d[0])*p[0],(d[3]-d[2])*p[1],(d[5]-d[4])*p[2]);const g=Sn(f)/e.getCurrentSampleDistance(r),m={interpolationType:o.getProperty().getInterpolationType(),useLabelOutline:o.getProperty().getUseLabelOutline(),numComp:s,usesProportionalComponents:c,iComps:l,maxSamples:g,useGradientOpacity:o.getProperty().getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),proportionalComponents:u};return t.previousState&&t.previousState.interpolationType===m.interpolationType&&t.previousState.useLabelOutline===m.useLabelOutline&&t.previousState.numComp===m.numComp&&t.previousState.usesProportionalComponents===m.usesProportionalComponents&&t.previousState.iComps===m.iComps&&t.previousState.maxSamples===m.maxSamples&&t.previousState.useGradientOpacity===m.useGradientOpacity&&t.previousState.blendMode===m.blendMode&&function(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;++n)if(e[n]!==t[n])return!1;return!0}(t.previousState.proportionalComponents,m.proportionalComponents)?!!(0===n.getProgram()?.getHandle()||i||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||!!t.lastZBufferTexture!=!!t.zBufferTexture||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime())&&(t.lastZBufferTexture=t.zBufferTexture,!0):(t.previousState={...m},!0)},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Dg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*Dn(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(o.getProperty().getUseLabelOutline()){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lastLightComplexity)return;let O=0;const R=[],M=[],D=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],D[0+3*O]=-.5*w[0],D[1+3*O]=-.5*w[1],D[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",D),3===t.lastLightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();o.setUniformi("ctexture",t.colorTexture.getTextureUnit()),o.setUniformi("otexture",t.opacityTexture.getTextureUnit()),o.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),o.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const a=t.scalarTexture.getVolumeInfo(),i=r.getProperty(),s=t.scalarTexture.getComponents(),l=r.getProperty().getIndependentComponents();if(l&&s>=2)for(let e=0;e<s;e++)o.setUniformf(`mix${e}`,r.getProperty().getComponentWeight(e));for(let e=0;e<s;e++){const t=l?e:0,n=a.scale[e],r=i.getScalarOpacity(t).getRange(),s=n/(r[1]-r[0]),c=(a.offset[e]-r[0])/(r[1]-r[0]);o.setUniformf(`oshift${e}`,c),o.setUniformf(`oscale${e}`,s);const u=i.getRGBTransferFunction(t).getRange(),d=(a.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);o.setUniformf(`cshift${e}`,d),o.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(l)for(let e=0;e<s;++e){const t=a.scale[e];if(i.getUseGradientOpacity(e)){const n=i.getGradientOpacityMinimumOpacity(e),r=i.getGradientOpacityMaximumOpacity(e);o.setUniformf(`gomin${e}`,n),o.setUniformf(`gomax${e}`,r);const a=[i.getGradientOpacityMinimumValue(e),i.getGradientOpacityMaximumValue(e)];o.setUniformf(`goscale${e}`,t*(r-n)/(a[1]-a[0])),o.setUniformf(`goshift${e}`,-a[0]*(r-n)/(a[1]-a[0])+n)}else o.setUniformf(`gomin${e}`,1),o.setUniformf(`gomax${e}`,1),o.setUniformf(`goscale${e}`,0),o.setUniformf(`goshift${e}`,1)}else{const e=a.scale[s-1],t=i.getGradientOpacityMinimumOpacity(0),n=i.getGradientOpacityMaximumOpacity(0);o.setUniformf("gomin0",t),o.setUniformf("gomax0",n);const r=[i.getGradientOpacityMinimumValue(0),i.getGradientOpacityMaximumValue(0)];o.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),o.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===r.getProperty().getUseLabelOutline()){const e=r.getProperty().getLabelOutlineOpacity();o.setUniformf("outlineOpacity",e)}t.lastLightComplexity>0&&(o.setUniformf("vAmbient",i.getAmbient()),o.setUniformf("vDiffuse",i.getDiffuse()),o.setUniformf("vSpecular",i.getSpecular()),o.setUniformf("vSpecularPower",i.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*Dn(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===wg.NEAREST?(t.scalarTexture.setMinificationFilter(bd.NEAREST),t.scalarTexture.setMagnificationFilter(bd.NEAREST)):(t.scalarTexture.setMinificationFilter(bd.LINEAR),t.scalarTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null,null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=fd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Eg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;t._scalars!==a&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=a);const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(bd.LINEAR),t.jitterTexture.setMagnificationFilter(bd.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=i.getIndependentComponents()?s:1,c=i.getScalarOpacity(),u=t._openGLRenderWindow.getGraphicsResourceForObject(c);let d=Vg(i,c,l);if(u.vtkObj&&u.hash===d&&t.opacityTextureString===d)t.opacityTexture=u.vtkObj,t.opacityTextureString=u.hash;else{const r=1024,o=2*r*l,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<l;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.releaseGraphicsResources(t._openGLRenderWindow),t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(bd.LINEAR),t.opacityTexture.setMagnificationFilter(bd.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*l,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*l,1,ys.UNSIGNED_CHAR,e)}t.opacityTextureString=d,c&&t._openGLRenderWindow.setGraphicsResourceForObject(c,t.opacityTexture,t.opacityTextureString)}const p=i.getRGBTransferFunction();d=Vg(i,p,l);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===d&&t.colorTextureString===d)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(2*e*l*3),r=new Float32Array(3*e);for(let t=0;t<l;++t){const o=i.getRGBTransferFunction(t),a=o.getRange();o.getTable(a[0],a[1],e,r,1);for(let o=0;o<3*e;++o)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.create2DFromRaw(e,2*l,3,ys.UNSIGNED_CHAR,n),t.colorTextureString=d,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}e.updateLabelOutlineThicknessTexture(r);const g=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(d=`${o.getMTime()}A${a.getMTime()}`,g?.vtkObj&&g?.hash===d&&t.scalarTextureString===d)t.scalarTexture=g.vtkObj,t.scalarTextureString=g.hash;else{const e=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.releaseGraphicsResources(t._openGLRenderWindow),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],a,t.renderable.getPreferSizeOverAccuracy()),t.scalarTextureString=d,a&&t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,t.scalarTextureString)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Es.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Es.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=e=>{const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}}}const Bg={context:null,VBOBuildTime:null,scalarTexture:null,scalarTextureString:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lastLightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0,_scalars:null};const Ng=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bg,n),Zt.extend(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=yd.newInstance(),t.scalarTexture=Nd.newInstance(),t.opacityTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.jitterTexture=Nd.newInstance(),t.jitterTexture.setWrapS(Td.REPEAT),t.jitterTexture.setWrapT(Td.REPEAT),t.labelOutlineThicknessTexture=Nd.newInstance(),t.framebuffer=Hp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Lg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",Ng);const{vtkDebugMacro:_g}=Kt;function Fg(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent();const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():_g("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const kg={};const Gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kg,n),Zt.extend(e,t,n),Fg(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Gg);var Ug="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:zg}=ds;function Wg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Nd.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Hg(e,t){return Wg(e,t,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE)}function jg(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Hg(t._openGLRenderWindow,t.size),t.seedTexture0=Hg(t._openGLRenderWindow,t.size),t.licTexture1=Hg(t._openGLRenderWindow,t.size),t.seedTexture1=Hg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Wg(t._openGLRenderWindow,t.size,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Wg(e,t,Nd.Filter.LINEAR,Nd.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const Kg={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function $g(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kg,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),jg(e,t)}var qg={newInstance:jt.newInstance($g,"vtkLICPingPongBufferManager"),extend:$g};const Xg=0,Yg=1,Zg=2,Qg=3,Jg=1;function em(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Hp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;f&&o[0]===f.getSize()[0]&&o[1]===f.getSize()[1]||(f=Hp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=qg.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const g=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],m=1/o[0],h=1/o[1],v=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;v.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:y}=t;v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",0),y.setUniformf("uMaskThreshold",t.maskThreshold),y.setUniform2f("uNoiseBoundsPt1",...g),y.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),y.setUniformi("texLIC",t.bufs.getLICTextureUnit()),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,y);const{LICIShaderProgram:T}=t;v.readyShaderProgram(T),T.setUniformi("uPassNo",0),T.setUniformf("uStepSize",-d),T.setUniform2f("uNoiseBoundsPt1",...g),T.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:b}=t;if(v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,b),t.enhancedLIC){t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",m),a.setUniformf("uDy",h),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",1),n(y,t.bufs),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformi("uPassNo",1),T.setUniformf("uStepSize",-d),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),b.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,b)}if(t.antiAlias){const e=t.AAHProgram;v.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",m);const a=t.AAVProgram;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",h);for(let i=0;i<t.antiAlias;++i)v.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),v.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const tm={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLineIntegralConvolution2D"),extend:nm};function om(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const am={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:Xg,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:Jg,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function im(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,am,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),om(0,t)}var sm={newInstance:jt.newInstance(im,"vtkSurfaceLICInterface"),extend:im};const{Representation:lm}=ds;function cm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===Jg?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Nd.newInstance({wrapS:Nd.Wrap.REPEAT,wrapT:Nd.Wrap.REPEAT,minificationFilter:Nd.Filter.NEAREST,magnificationFilter:Nd.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.allocateTextures=()=>{const n=Nd.Filter.NEAREST,r=Nd.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Nd.newInstance({wrapS:Nd.Wrap.CLAMP_TO_EDGE,wrapT:Nd.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Nd.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Hp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",lm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=rm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===Zg||u===Qg){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const um={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),sm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkSurfaceLICInterface"),extend:dm};const{vtkErrorMacro:fm}=Kt;function gm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=cd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=cd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=cd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=cd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=cd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(fm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(fm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(fm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=sm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=pm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(fm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const mm={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const hm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mm,n),np.extend(e,t,n),gm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",hm);const{vtkErrorMacro:vm}=Kt;function ym(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||vm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,zu.ARRAY_BUFFER),o&&a.getColorBO().upload(f,zu.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Tm={};const bm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),np.extend(e,t,n),ym(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",bm);const{vtkErrorMacro:xm}=Kt;function Cm(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=cd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||xm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||xm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||xm("Error setting 'radiusMC' in shader 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o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():xm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let 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e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const Vh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Lh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Eh(e,t)}var Bh={newInstance:jt.newInstance(Lh,"vtkCamera"),extend:Lh};function Nh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const _h={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function Fh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_h,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),Nh(e,t)}var kh={newInstance:jt.newInstance(Fh,"vtkLight"),extend:Fh,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Gh}=jt;function Uh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Gh("call displayToView on your view instead"),e.viewToDisplay=()=>Gh("callviewtodisplay on your view instead"),e.getSize=()=>Gh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Gh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkViewport"),extend:Wh};const{vtkDebugMacro:jh,vtkErrorMacro:Kh,vtkWarningMacro:$h}=Kt;function qh(e){return()=>Kh(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function Xh(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(jh("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):Kh("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=qh("allocateTime"),e.updateGeometry=qh("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{jh(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Bh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=kh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return Kh("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return Kh("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),jh("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return jh("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return Kh("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&($h("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return jh("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return Kh("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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Yh={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function Zh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,Yh,n),Hh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),Xh(e,t)}var Qh={newInstance:Et(Zh,"vtkRenderer"),extend:Zh};const Jh=Object.create(null);function ev(e,t){Jh[e]=t}function tv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Jh[e]&&Jh[e](t)}function nv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return tv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))}}const rv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1};function ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),nv(e,t)}var av={newInstance:jt.newInstance(ov,"vtkRenderWindow"),extend:ov,registerViewConstructor:ev,listViewAPIs:function(){return Object.keys(Jh)},newAPISpecificView:tv};const iv={Unknown:0,LeftController:1,RightController:2},sv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var lv={Device:iv,Input:sv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:cv,Input:uv}=lv,{vtkWarningMacro:dv,vtkErrorMacro:pv,normalizeWheel:fv,vtkOnceErrorMacro:gv}=jt,mv={ctrlKey:!1,altKey:!1,shiftKey:!1},hv={"xr-standard":[uv.Trigger,uv.Grip,uv.TrackPad,uv.Thumbstick,uv.A,uv.B]},vv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function yv(e){e.cancelable&&e.preventDefault()}function Tv(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function bv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{pv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>dv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",yv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",yv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&yv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&dv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&yv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:pv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?dv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):pv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;dv(`${e} did not request an 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t.lastGamepadValues[i.index][s].buttons||(t.lastGamepadValues[i.index][s].buttons[r]=!1),t.lastGamepadValues[i.index][s].buttons[r]!==i.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,pressed:i.buttons[r].pressed,device:"left"===n.handedness?cv.LeftController:cv.RightController,input:hv[i.mapping]&&hv[i.mapping][r]?hv[i.mapping][r]:uv.Trigger}),t.lastGamepadValues[i.index][s].buttons[r]=i.buttons[r].pressed),t.lastGamepadValues[i.index][s].buttons[r]&&null!=a&&e.move3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,device:"left"===n.handedness?cv.LeftController:cv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{yv(n);const r={...fv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:pv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);if(2===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Tv(o);e.recognizeGesture("TouchEnd",t);const a={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},vv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void gv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const xv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Cv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xv,n),jt.obj(e,t),t._animationExtendedEnd=0,jt.event(e,t,"RenderEvent"),vv.forEach((n=>jt.event(e,t,n))),jt.get(e,t,["initialized","interactorStyle","lastFrameTime","recentAnimationFrameRate","_view"]),jt.setGet(e,t,["container","lightFollowCamera","enabled","enableRender","recognizeGestures","desiredUpdateRate","stillUpdateRate","picker","preventDefaultOnPointerDown","preventDefaultOnPointerUp","mouseScrollDebounceByPass"]),jt.moveToProtected(e,t,["view"]),bv(e,t)}var Sv={newInstance:jt.newInstance(Cv,"vtkRenderWindowInteractor"),extend:Cv,handledEvents:vv,...lv};const{vtkErrorMacro:Av,VOID:Iv}=jt;function wv(e,t){t.classHierarchy.push("vtkInteractorObserver");const n={...e};function r(){for(;t.subscribedEvents.length;)t.subscribedEvents.pop().unsubscribe()}function o(){Sv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Iv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Av("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Pv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function Ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),wv(e,t)}var Rv={newInstance:jt.newInstance(Ov,"vtkInteractorObserver"),extend:Ov,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},Mv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Dv}=Mv,Ev={Rotate:Dv.IS_ROTATE,Pan:Dv.IS_PAN,Spin:Dv.IS_SPIN,Dolly:Dv.IS_DOLLY,CameraPose:Dv.IS_CAMERA_POSE,WindowLevel:Dv.IS_WINDOW_LEVEL,Slice:Dv.IS_SLICE};function Vv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Ev).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Dv.IS_NONE&&(t.state=Ev[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Ev[n]&&(t.state=Dv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":e.pokedRenderer.resetCamera(),n.render();break;case"w":case"W":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Lv={state:Dv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Bv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lv,n),Rv.extend(e,t,n),Vv(e,t)}var Nv={newInstance:jt.newInstance(Bv,"vtkInteractorStyle"),extend:Bv,...Mv};const{States:_v}=Mv;function Fv(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=n.pokedRenderer;switch(t.state){case _v.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad?!n||n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad||t.state!==_v.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===_v.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const t=e.pokedRenderer.getActiveCamera(),n=t.getPhysicalTranslation(),r=.025*t.getPhysicalScale(),o=t.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);t.setPhysicalTranslation(n[0]+o[0]*r,n[1]+o[1]*r,n[2]+o[2]*r)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case _v.IS_DOLLY:e.endDolly();break;case _v.IS_PAN:e.endPan();break;case _v.IS_SPIN:e.endSpin();break;case _v.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(n.pokedRenderer,n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=n.pokedRenderer.getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(n.pokedRenderer,o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(n.pokedRenderer,o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(n.pokedRenderer,o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&n.pokedRenderer.updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=e.pokedRenderer.getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(n.pokedRenderer,r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const kv={motionFactor:10,zoomFactor:10};function Gv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kv,n),Nv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),Fv(e,t)}var Uv={newInstance:jt.newInstance(Gv,"vtkInteractorStyleTrackballCamera"),extend:Gv};function zv(e){return e}function Wv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Wv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Hv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Wv:zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function jv(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const Kv={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function $v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kv,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),jv(e,t)}var qv={newInstance:jt.newInstance($v,"vtkRenderPass"),extend:$v};const{Representation:Xv}=ds,{vtkErrorMacro:Yv}=jt;function Zv(e){const t=cd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function Qv(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Es.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Es.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",Xv.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Nd.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Nd.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Nd.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||Yv("Error setting vertexDC in copy shader 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CT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ST(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group 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0!==arguments[2]?arguments[2]:{};Object.assign(t,AT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ST(e,t)}var wT={newInstance:Et(IT,"vtkWebGPURenderEncoder"),extend:IT},PT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const OT=["getMappedRange","mapAsync","unmap"];function RT(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<OT.length;n++)e[OT[n]]=function(){return t.handle[OT[n]](...arguments)}}const MT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function DT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,MT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),RT(e,t)}var ET={newInstance:jt.newInstance(DT),extend:DT,...PT};const{Representation:VT}=ds,{PrimitiveTypes:LT}=PT;class BT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function NT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function _T(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=NT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=NT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=NT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=NT(n,o,t),n.ibo[n.iboId++]=a}function FT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let kT;const GT=new Uint32Array(1),UT=new Uint32Array(2),zT=new Uint32Array(3),WT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)GT[0]=t[n+a],kT(GT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)UT[0]=t[n+a],UT[1]=t[n+(a+1)%e],kT(UT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o);for(let a=0;a<e-2;a++)UT[0]=t[n+a],UT[1]=t[n+a+2],kT(UT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n],zT[1]=t[n+a+1],zT[2]=t[n+a+2],kT(zT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n+a],zT[1]=t[n+a+1+a%2],zT[2]=t[n+a+1+(a+1)%2],kT(zT,r,o)}};function HT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case LT.Points:return"points";case LT.Lines:return"lines";case LT.Triangles:case LT.TriangleEdges:return"polys";case LT.TriangleStripEdges:case LT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new BT};let d=null;d=o===VT.POINTS||r===LT.Points?WT.anythingToPoints:o===VT.WIREFRAME||r===LT.Lines?WT[`${l}ToWireframe`]:WT[`${l}ToSurface`],kT=FT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),kT=_T,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const jT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function KT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jT,n),ET.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),HT(e,t)}var $T={newInstance:jt.newInstance(KT),extend:KT,...PT};const{BufferUsage:qT}=PT,{vtkErrorMacro:XT}=Kt,{VtkDataTypes:YT}=Es;function ZT(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function QT(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function JT(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===qT.Index&&(n=$T.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=ET.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===qT.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===qT.PointArray){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=ZT(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===qT.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=QT(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ZT(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===qT.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case YT.UNSIGNED_CHAR:t="uint8";break;case YT.FLOAT:t="float32";break;case YT.UNSIGNED_INT:t="uint32";break;case YT.INT:t="sint32";break;case YT.DOUBLE:t="float32";break;case YT.UNSIGNED_SHORT:t="uint16";break;case YT.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||XT(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:qT.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=ET.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const eb={device:null,fullScreenQuadBuffer:null};function tb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eb,n),yt(e,t),At(e,t,["device"]),JT(e,t)}var nb={newInstance:Et(tb),extend:tb,...PT};function rb(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const ob={device:null,handle:null,label:null,options:null};function ab(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ob,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),rb(e,t)}var ib={newInstance:jt.newInstance(ab),extend:ab};function sb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(t.texture.getFormat());t.bindGroupLayoutEntry.texture.sampleType=r.sampleType},e.createFromTextureHandle=(e,n)=>{t.texture=null,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e,t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(n.format);t.bindGroupLayoutEntry.texture.sampleType=r.sampleType,t.bindGroupTime.modified()},e.getBindGroupEntry=()=>({resource:e.getHandle()}),e.getShaderCode=(e,n)=>{let r="f32";"sint"===t.bindGroupLayoutEntry.texture.sampleType?r="i32":"uint"===t.bindGroupLayoutEntry.texture.sampleType&&(r="u32");let o=`@binding(${e}) @group(${n}) var ${t.label}: texture_${t.options.dimension}<${r}>;`;return"depth"===t.bindGroupLayoutEntry.texture.sampleType&&(o=`@binding(${e}) @group(${n}) var ${t.label}: texture_depth_${t.options.dimension};`),o},e.addSampler=(n,r)=>{const o=ib.newInstance({label:`${t.label}Sampler`});o.create(n,r),e.setSampler(o)},e.getBindGroupTime=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.bindGroupTime),e.getHandle=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.handle)}const lb={texture:null,handle:null,sampler:null,label:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),jt.obj(e,t),t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,texture:{sampleType:"float",viewDimension:"2d"}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","texture"]),jt.setGet(e,t,["bindGroupLayoutEntry","label","sampler"]),sb(e,t)}var ub={newInstance:jt.newInstance(cb),extend:cb};const{BufferUsage:db}=nb;function pb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return t.device.getHandle().queue.copyExternalImageToTexture({source:n.canvas,flipY:n.flip},{texture:t.handle,premultipliedAlpha:!0},[t.width,t.height,t.depth]),void(t.ready=!0);n.jsImageData&&!n.nativeArray&&(n.width=n.jsImageData.width,n.height=n.jsImageData.height,n.depth=1,n.format="rgba8unorm",n.flip=!0,n.nativeArray=n.jsImageData.data);const o=tT(t.format);let a=t.width*o.stride;const i=(e,t,n)=>{const r=e.length/(t*n)*e.BYTES_PER_ELEMENT,a=2===o.elementSize&&"float"===o.sampleType;if(a||r%256){const i=e,s=r/i.BYTES_PER_ELEMENT,l=o.elementSize,c=256*Math.floor((s*l+255)/256),u=c/l,d=jt.newTypedArray(a?"Uint16Array":i.constructor.name,u*t*n);for(let e=0;e<t*n;e++)if(a)for(let t=0;t<s;t++)d[e*u+t]=Ad.toHalf(i[e*s+t]);else d.set(i.subarray(e*s,(e+1)*s),e*u);return[d,c]}return[e,r]};if(n.nativeArray&&(r=n.nativeArray),n.image){const e=document.createElement("canvas");e.width=n.image.width,e.height=n.image.height;const t=e.getContext("2d");t.translate(0,e.height),t.scale(1,-1),t.drawImage(n.image,0,0,n.image.width,n.image.height,0,0,e.width,e.height),r=t.getImageData(0,0,n.image.width,n.image.height).data}const s=t.device.createCommandEncoder();if(3!==e.getDimensionality()){const e=Tu.generateMipmaps(r,t.width,t.height,t.mipLevel);let o=t.width,l=t.height;for(let r=0;r<=t.mipLevel;r++){const c=i(e[r],l,1);a=c[1];const u={dataArray:n.dataArray?n.dataArray:null,nativeArray:c[0],usage:db.Texture},d=t.device.getBufferManager().getBuffer(u);s.copyBufferToTexture({buffer:d.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:l},{texture:t.handle,mipLevel:r},[o,l,1]),o/=2,l/=2}t.device.submitCommandEncoder(s),t.ready=!0}else{const e=i(r,t.height,t.depth);a=e[1];const o={dataArray:n.dataArray?n.dataArray:null,usage:db.Texture};o.nativeArray=e[0];const l=t.device.getBufferManager().getBuffer(o);s.copyBufferToTexture({buffer:l.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:t.height},{texture:t.handle},[t.width,t.height,t.depth]),t.device.submitCommandEncoder(s),t.ready=!0}},e.getScale=()=>{const e=tT(t.format);return 2===e.elementSize&&"float"===e.sampleType?1:255},e.getNumberOfComponents=()=>tT(t.format).numComponents,e.getDimensionality=()=>{let e=0;return t.width>1&&e++,t.height>1&&e++,t.depth>1&&e++,e},e.resizeToMatch=e=>{e.getWidth()===t.width&&e.getHeight()===t.height&&e.getDepth()===t.depth||(t.width=e.getWidth(),t.height=e.getHeight(),t.depth=e.getDepth(),t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.resize=function(e,n){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;e===t.width&&n===t.height&&r===t.depth||(t.width=e,t.height=n,t.depth=r,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.createView=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};r.dimension||(r.dimension=1===t.depth?"2d":"3d");const o=ub.newInstance({label:n});return o.create(e,r),o}}const fb={device:null,handle:null,buffer:null,ready:!1,label:null};function gb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fb,n),jt.obj(e,t),jt.get(e,t,["handle","ready","width","height","depth","format","usage"]),jt.setGet(e,t,["device","label"]),pb(e,t)}var mb={newInstance:jt.newInstance(gb),extend:gb};function hb(e,t){t.classHierarchy.push("vtkWebGPUOpaquePass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.renderEncoder)t.colorTexture.resize(r.getCanvas().width,r.getCanvas().height),t.depthTexture.resize(r.getCanvas().width,r.getCanvas().height);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"opaquePassColor"}),t.colorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const n=t.colorTexture.createView("opaquePassColorTexture");t.renderEncoder.setColorTextureView(0,n),t.depthFormat="depth32float",t.depthTexture=mb.newInstance({label:"opaquePassDepth"}),t.depthTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:t.depthFormat,usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("opaquePassDepthTexture");t.renderEncoder.setDepthTextureView(a)}t.renderEncoder.attachTextureViews(),e.setCurrentOperation("opaquePass"),n.setRenderEncoder(t.renderEncoder),n.traverse(e)},e.getColorTextureView=()=>t.renderEncoder.getColorTextureViews()[0],e.getDepthTextureView=()=>t.renderEncoder.getDepthTextureView(),e.createRenderEncoder=()=>{t.renderEncoder=wT.newInstance({label:"OpaquePass"}),t.renderEncoder.setPipelineHash("op")}}const vb={renderEncoder:null,colorTexture:null,depthTexture:null};function yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vb,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),hb(e,t)}var Tb={newInstance:jt.newInstance(yb,"vtkWebGPUOpaquePass"),extend:yb};function bb(e,t){t.classHierarchy.push("vtkWebGPUOrderIndependentTranslucentPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.translucentRenderEncoder)t.translucentColorTexture.resizeToMatch(t.colorTextureView.getTexture()),t.translucentAccumulateTexture.resizeToMatch(t.colorTextureView.getTexture());else{e.createRenderEncoder(),e.createFinalEncoder(),t.translucentColorTexture=mb.newInstance({label:"translucentPassColor"}),t.translucentColorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const n=t.translucentColorTexture.createView("oitpColorTexture");t.translucentRenderEncoder.setColorTextureView(0,n),t.translucentAccumulateTexture=mb.newInstance({label:"translucentPassAccumulate"}),t.translucentAccumulateTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=xT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n")}t.translucentRenderEncoder.setDepthTextureView(t.depthTextureView),t.translucentRenderEncoder.attachTextureViews(),e.setCurrentOperation("translucentPass"),n.setRenderEncoder(t.translucentRenderEncoder),n.traverse(e),e.finalPass(r,n)},e.finalPass=(e,n)=>{t.translucentFinalEncoder.setColorTextureView(0,t.colorTextureView),t.translucentFinalEncoder.attachTextureViews(),t.translucentFinalEncoder.begin(e.getCommandEncoder()),n.scissorAndViewport(t.translucentFinalEncoder),t.fullScreenQuad.prepareAndDraw(t.translucentFinalEncoder),t.translucentFinalEncoder.end()},e.getTextures=()=>[t.translucentColorTexture,t.translucentAccumulateTexture],e.createRenderEncoder=()=>{t.translucentRenderEncoder=wT.newInstance({label:"translucentRender"});const e=t.translucentRenderEncoder.getDescription();e.colorAttachments=[{view:void 0,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:void 0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = computedColor.a;"]).result,t.setCode(n)})),t.translucentRenderEncoder.setPipelineHash("oitpr"),t.translucentRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!1,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one"},alpha:{srcfactor:"one",dstFactor:"one"}}},{format:"r16float",blend:{color:{srcFactor:"zero",dstFactor:"one-minus-src"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createFinalEncoder=()=>{t.translucentFinalEncoder=wT.newInstance({label:"translucentFinal"}),t.translucentFinalEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t.translucentFinalEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t.translucentFinalEncoder.setPipelineHash("oitpf"),t.translucentFinalEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})}}const xb={colorTextureView:null,depthTextureView:null};function Cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xb,n),qv.extend(e,t,n),jt.setGet(e,t,["colorTextureView","depthTextureView"]),bb(e,t)}var Sb={newInstance:jt.newInstance(Cb,"vtkWebGPUOrderIndependentTranslucentPass"),extend:Cb};const{BufferUsage:Ab}=nb,{vtkErrorMacro:Ib}=jt;function wb(e,t){t.classHierarchy.push("vtkWebGPUUniformBuffer"),e.addEntry=(e,n)=>{t._bufferEntryNames.has(e)?Ib(`entry named ${e} already 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o=t.bufferEntries[r];o.packed||(o.packed=!0,o.offset=e,n.push(o),e+=o.sizeInBytes)}t.bufferEntries=n,t._bufferEntryNames.clear();for(let e=0;e<t.bufferEntries.length;e++)t._bufferEntryNames.set(t.bufferEntries[e].name,e);t.sizeInBytes=e,t.sizeInBytes=r*Math.ceil(t.sizeInBytes/r),t.sortDirty=!1},e.sendIfNeeded=e=>{if(!t.UBO){const n={nativeArray:t.Float32Array,usage:Ab.UniformArray,label:t.label};t.UBO=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),t.sendDirty=!1}t.sendDirty&&(e.getHandle().queue.writeBuffer(t.UBO.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes),t.sendDirty=!1),t.sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];a.lastValue!==r&&(i[a.offset/i.BYTES_PER_ELEMENT]=r,t.sendDirty=!0),a.lastValue=r},e.setArray=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];let s=!1;for(let e=0;e<r.length;e++)a.lastValue&&a.lastValue[e]===r[e]||(i[a.offset/i.BYTES_PER_ELEMENT+e]=r[e],s=!0);s&&(t.sendDirty=!0,a.lastValue=[...r])},e.getBindGroupEntry=()=>({resource:{buffer:t.UBO.getHandle()}}),e.getSendTime=()=>t.sendTime.getMTime(),e.getShaderCode=(n,r)=>{e.sortBufferEntries();const o=[`struct ${t.label}Struct\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];o.push(`  ${n.name}: ${n.type},`)}return o.push(`};\n@binding(${n}) @group(${r}) var<uniform> ${t.label}: ${t.label}Struct;`),o.join("\n")}}const Pb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Ob(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),wb(e,t)}var Rb={newInstance:jt.newInstance(Ob,"vtkWebGPUUniformBuffer"),extend:Ob};const{BufferUsage:Mb}=nb,{vtkErrorMacro:Db}=jt;function Eb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void Db(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void Db(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Vb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function Lb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Eb(e,t)}var Bb={newInstance:jt.newInstance(Lb,"vtkWebGPUStorageBuffer"),extend:Lb};const Nb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",_b=new Float64Array(16),Fb=new Float64Array(16);function kb(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Uy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Rg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Uy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Uy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Uy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Rg.COMPOSITE_BLEND?l=!0:n===Rg.MAXIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.MINIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.AVERAGE_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.ADDITIVE_INTENSITY_BLEND&&(a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Uy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(_b),b(_b,_b,o);const p=n.getMatrix();m(Fb,p),h(Fb,Fb),T(_b,Fb,_b);const f=d.getWorldToIndex();T(_b,f,_b);const v=d.getDimensions();g(Fb),x(Fb,Fb,[1/v[0],1/v[1],1/v[2]]),T(_b,Fb,_b);for(let t=0;t<16;t++)a[16*e+t]=_b[t];h(_b,_b);for(let t=0;t<4;t++)i[16*e+4*t]=_b[4*t],i[16*e+4*t+1]=_b[4*t+1],i[16*e+4*t+2]=_b[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=tT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=ib.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const Gb={volumes:null,rowLength:1024,lastVolumeLength:0};function Ub(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gb,n),xT.extend(e,t,n),t.fragmentShaderTemplate=Nb,t.UBO=Rb.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Bb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Bb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),kb(e,t)}var zb={newInstance:jt.newInstance(Ub,"vtkWebGPUVolumePassFSQ"),extend:Ub};const{Representation:Wb}=ds,{BufferUsage:Hb,PrimitiveTypes:jb}=nb,Kb=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],$b="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function qb(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=zb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=xT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=Rb.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Hb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:jb.Triangles,representation:Wb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Hb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+Kb[e][0],l[n++]=r+Kb[e][1],l[n++]=r+Kb[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=wT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=mb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=mb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=mb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=wT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=wT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=wT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const Xb={colorTextureView:null,depthTextureView:null,volumes:null};function Yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Xb,n),qv.extend(e,t,n),t._mapper=vT.newInstance(),t._mapper.setFragmentShaderTemplate($b),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=gu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),qb(e,t)}var Zb={newInstance:jt.newInstance(Yb,"vtkWebGPUVolumePass"),extend:Yb};function Qb(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Tb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Sb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=Zb.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=wT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=ib.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=xT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=ub.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const Jb={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function ex(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jb,n),qv.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),Qb(e,t)}var tx={newInstance:jt.newInstance(ex,"vtkForwardPass"),extend:ex};const{VtkDataTypes:nx}=Es;function rx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case nx.UNSIGNED_CHAR:e.format+="8unorm";break;case nx.FLOAT:case nx.UNSIGNED_INT:case nx.INT:case nx.DOUBLE:case nx.UNSIGNED_SHORT:case nx.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=mb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const ox={handle:null,device:null};function ax(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ox,n),jt.obj(e,t),jt.setGet(e,t,["device"]),rx(e,t)}var ix={newInstance:jt.newInstance(ax),extend:ax};class sx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function lx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const cx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function ux(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new sx,t.shaderCache=Uy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=nb.newInstance(),t.bufferManager.setDevice(e),t.textureManager=ix.newInstance(),t.textureManager.setDevice(e),t.pipelines={},lx(e,t)}var dx={newInstance:Et(ux,"vtkWebGPUDevice"),extend:ux};function px(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=mb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=wT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const fx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function gx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fx,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),px(e,t)}var mx={newInstance:jt.newInstance(gx,"vtkWebGPUHardwareSelectionPass"),extend:gx};const{SelectionContent:hx,SelectionField:vx}=qp,{FieldAssociations:yx}=ol,{vtkErrorMacro:Tx}=jt;function bx(e){return`${e.propID} ${e.compositeID}`}function xx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Cx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=xx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=xx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Cx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Cx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Cx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Cx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Cx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Sx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Tx("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=ET.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=ET.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Cx(e,[n,t],0,u);if(r){const t=bx(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=qp.newInstance();switch(i.setContentType(hx.INDICES),e){case yx.FIELD_ASSOCIATION_CELLS:i.setFieldType(vx.CELL);break;case yx.FIELD_ASSOCIATION_POINTS:i.setFieldType(vx.POINT);break;default:Tx("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Ax={};function Ix(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ax,n),Gp.extend(e,t,n),t._selectionPass=mx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Sx(e,t)}var wx={newInstance:jt.newInstance(Ix,"vtkWebGPUHardwareSelector"),extend:Ix};const Px=Object.create(null);function Ox(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const Rx={};function Mx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rx,n),t.overrides=Px,tn.extend(e,t,n),Ox(0,t)}var Dx={newInstance:jt.newInstance(Mx,"vtkWebGPUViewNodeFactory"),extend:Mx};const{vtkErrorMacro:Ex}=jt,Vx={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function Lx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Ex("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Ex("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=dx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=qv.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Vx,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=ET.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Ad.fromHalf(r.colorValues[o]),s[n+1]=255*Ad.fromHalf(r.colorValues[o+1]),s[n+2]=255*Ad.fromHalf(r.colorValues[o+2]),s[n+3]=255*Ad.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=wx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Bx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Nx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",xy.extend(e,t,n),t.myFactory=Dx.newInstance(),t.myFactory.registerOverride("vtkRenderWindow",Nx),t.renderPasses[0]=tx.newInstance(),t.selector||(t.selector=wx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),Lx(e,t)}),"vtkWebGPURenderWindow");ev("WebGPU",Nx);const _x=Hv(),Fx={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},kx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Gx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Ux(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Gx(t.container,t.containerStyle||Fx),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=av.newInstance(),t.renderer=Qh.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(_x.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Sv.newInstance(),t.interactor.setInteractorStyle(Uv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Gx(t.controlContainer,t.controlPanelStyle||kx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",(t=>{"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}))},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Wx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Ux(e,t)}var Hx={newInstance:jt.newInstance(Wx),extend:Wx},jx={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:Kx,Scale:$x}=jx,{ScalarMappingTarget:qx}=Pl,{vtkDebugMacro:Xx,vtkErrorMacro:Yx,vtkWarningMacro:Zx}=jt;function Qx(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function Jx(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function eC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];Qx(o,i),Qx(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=Jx(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=Jx(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function tC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return Yx("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return Yx("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===$x.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Do(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===Kx.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===Kx.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===Kx.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===Kx.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return Yx("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(Yx("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return Yx("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>t.indexedLookup&&e.getSize()?e.getSize():t.tableSize?t.tableSize:16777216,e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void Yx("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):Xx("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void Zx("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===qx.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===qx.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===qx.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===qx.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=Kx[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(Yx(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=Kx.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const nC={clamping:!0,colorSpace:Kx.RGB,hSVWrap:!0,scale:$x.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function rC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","colorSpace","discretize","numberOfValues"]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),tC(e,t)}var oC={newInstance:jt.newInstance(rC,"vtkColorTransferFunction"),extend:rC,...jx},aC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:iC,ScaleModes:sC}=aC,{vtkErrorMacro:lC}=jt;function cC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(iC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(iC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(iC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(iC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(sC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(sC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(sC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(sC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===sC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(lC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case iC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case iC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case iC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case sC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case sC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case sC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const uC={orient:!0,orientationMode:iC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:sC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function dC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,uC,n),nc.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),cC(e,t)}var pC={newInstance:jt.newInstance(dC,"vtkGlyph3DMapper"),extend:dC,...aC};const{vtkErrorMacro:fC}=jt;function gC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(fC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return fC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return fC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return fC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const i={x:n,y:r,midpoint:o,sharpness:a};let s;for(t.nodes.push(i),e.sortAndUpdateRange(),s=0;s<t.nodes.length&&t.nodes[s].x!==n;s++);return s<t.nodes.length?s:-1},e.setNodes=n=>{t.nodes!==n&&(t.nodes=n,e.sortAndUpdateRange())},e.sortAndUpdateRange=()=>{t.nodes.sort(((e,t)=>e.x-t.x)),e.updateRange()||e.modified()},e.updateRange=()=>{const n=t.range.slice(),r=t.nodes.length;return r?(t.range[0]=t.nodes[0].x,t.range[1]=t.nodes[r-1].x):(t.range[0]=0,t.range[1]=0),(n[0]!==t.range[0]||n[1]!==t.range[1])&&(e.modified(),!0)},e.removePoint=n=>{let r;for(r=0;r<t.nodes.length&&t.nodes[r].x!==n;r++);if(r>=t.nodes.length)return-1;const o=r;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addSegment=(n,r,o,a)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=o?t.nodes.splice(e,1):e++;e.addPoint(n,r,.5,0),e.addPoint(o,a,.5,0)},e.getValue=t=>{const n=[];return e.getTable(t,t,1,n),n[0]},e.adjustRange=n=>{if(n.length<2)return 0;const r=e.getRange();r[0]<n[0]?e.addPoint(n[0],e.getValue(n[0])):e.addPoint(n[0],e.getValue(r[0])),r[1]>n[1]?e.addPoint(n[1],e.getValue(n[1])):e.addPoint(n[1],e.getValue(r[1])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return e.sortAndUpdateRange(),1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{const e=t.nodes.length;if(e<2)return-1;let n=t.nodes[1].x-t.nodes[0].x;for(let r=0;r<e-1;r++){const e=t.nodes[r+1].x-t.nodes[r].x;e<n&&(n=e)}return n},e.getTable=function(e,n,r,o){let a,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=0;const l=t.nodes.length;let c=0;0!==l&&(c=t.nodes[l-1].y);let u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const mC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function hC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),gC(e,t)}var vC={newInstance:jt.newInstance(hC,"vtkPiecewiseFunction"),extend:hC};const{InterpolationType:yC,OpacityMode:TC}=Og,{vtkErrorMacro:bC}=jt;function xC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(bC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=vC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=oC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=vC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return bC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(bC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(yC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(yC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(yC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(yC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const CC={independentComponents:!0,interpolationType:yC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function SC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,CC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:TC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),xC(e,t)}var AC={newInstance:jt.newInstance(SC,"vtkVolumeProperty"),extend:SC,...Og};const{vtkDebugMacro:IC}=jt;function wC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=AC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){IC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const PC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function OC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),wC(e,t)}var RC={newInstance:jt.newInstance(OC,"vtkVolume"),extend:OC};const{BlendMode:MC,FilterMode:DC}=Mg,EC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=vC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function VC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(MC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(MC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(MC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(MC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(MC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(MC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(MC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(DC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(DC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(DC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(DC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Do(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const LC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:MC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:DC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function BC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LC,n),Bs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),VC(e,t)}var NC={newInstance:jt.newInstance(BC,"vtkVolumeMapper"),extend:BC,...EC};const{InterpolationType:_C}=eg,{vtkErrorMacro:FC}=jt;function kC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return FC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(FC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(_C.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(_C.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(_C,t.interpolationType)}const GC={independentComponents:!1,interpolationType:_C.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function UC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,GC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),kC(e,t)}var zC={newInstance:jt.newInstance(UC,"vtkImageProperty"),extend:UC};const{vtkDebugMacro:WC}=jt;function HC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=zC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){WC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const jC={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function KC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),HC(e,t)}var $C={newInstance:jt.newInstance(KC,"vtkImageSlice"),extend:KC};function qC(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const XC={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var YC=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,XC,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),qC(e,t)};function ZC(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:QC,otherStaticMethods:JC}=Ul,{SlicingMode:eS}=sg;function tS(e,t){function n(){let n;switch(t.slicingMode){case eS.X:n=0;break;case eS.Y:n=1;break;case eS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case eS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case eS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case eS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case eS.I:l=Ma(a[0],i[0],i[1]);break;case eS.J:l=Ma(a[1],i[2],i[3]);break;case eS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case eS.I:case eS.J:case eS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case eS.X:e.setSlice(r[0]);break;case eS.Y:e.setSlice(r[1]);break;case eS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(eS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(eS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(eS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(eS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(eS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(eS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case eS.X:n[0]=1;break;case eS.Y:n[1]=1;break;case eS.Z:n[2]=1;break;case eS.I:ea(r,[1,0,0],n);break;case eS.J:ea(r,[0,1,0],n);break;case eS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==eS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case eS.I:r[0]=a,r[1]=a;break;case eS.J:r[2]=a,r[3]=a;break;case eS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case eS.I:a[0]=s-r,a[1]=s+r;break;case eS.J:a[2]=s-r,a[3]=s+r;break;case eS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const nS={slicingMode:eS.NONE,closestIJKAxis:{ijkMode:eS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function rS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nS,n),YC(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),Ul.implementCoincidentTopologyMethods(e,t),tS(e,t)}var oS={newInstance:jt.newInstance(rS,"vtkImageMapper"),extend:rS,...QC,...JC,...sg};const{vtkErrorMacro:aS}=jt;function iS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function sS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void aS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=gu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=mc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Es.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Es.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Es.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),iS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),iS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),iS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),iS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const lS={outputPointsPrecision:js.DEFAULT};function cS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),sS(e,t)}var uS={newInstance:jt.newInstance(cS,"vtkAppendPolyData"),extend:cS};function dS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Nf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=gu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const pS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function fS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),dS(e,t)}var gS={newInstance:jt.newInstance(fS,"vtkConeSource"),extend:fS};function mS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Es.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Es.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Nf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=gu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const hS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function vS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),mS(e,t)}var yS={newInstance:jt.newInstance(vS,"vtkCylinderSource"),extend:vS};function TS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=yS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Bf().rotateZ(-90).apply(a).apply(i);const s=gS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Nf().translate(1-.5*t.tipLength,0,0).apply(c);const u=uS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Nf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Nf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Nf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const bS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function xS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),TS(e,t)}var CS={newInstance:jt.newInstance(xS,"vtkArrowSource"),extend:xS};function SS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Bf().translate(...n).apply(e.getPoints().getData())}function AS(e,t){const n=e.getPoints().getBounds(),r=[0,0,0];r[t]=-n[2*t],Bf().translate(...r).apply(e.getPoints().getData())}function IS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Es.newInstance({name:"color",numberOfComponents:3,values:a}))}function wS(e,t){t.classHierarchy.push("vtkAxesActor");const n=nc.newInstance();e.setMapper(n),e.update=()=>{const e=CS.newInstance({direction:[1,0,0],...t.config}).getOutputData();t.config.recenter?SS(e):AS(e,0),IS(e,...t.xAxisColor);const r=CS.newInstance({direction:[0,1,0],...t.config}).getOutputData();t.config.recenter?SS(r):AS(r,1),IS(r,...t.yAxisColor);const o=CS.newInstance({direction:[0,0,1],...t.config}).getOutputData();t.config.recenter?SS(o):AS(o,2),IS(o,...t.zAxisColor);const a=uS.newInstance();a.setInputData(e),a.addInputData(r),a.addInputData(o),n.setInputConnection(a.getOutputPort())},e.update();const r=jt.debounce(e.update,0),{setConfig:o,setXAxisColor:a,setYAxisColor:i,setZAxisColor:s}=e;e.setConfig=e=>!!o(e)&&(r(),!0),e.setXAxisColor=e=>!!a(e)&&(r(),!0),e.setYAxisColor=e=>!!i(e)&&(r(),!0),e.setZAxisColor=e=>!!s(e)&&(r(),!0)}const PS={config:{recenter:!0,tipResolution:60,tipRadius:.1,tipLength:.2,shaftResolution:60,shaftRadius:.03,invert:!1},xAxisColor:[255,0,0],yAxisColor:[255,255,0],zAxisColor:[0,128,0]};function OS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PS,n),hs.extend(e,t,n),jt.setGet(e,t,["config"]),jt.setGetArray(e,t,["xAxisColor","yAxisColor","zAxisColor"],3,255),wS(e,t)}var RS={newInstance:jt.newInstance(OS,"vtkAxesActor"),extend:OS};const MS="resetcamera",DS="orientation",ES={MODE_RESET_CAMERA:MS,MODE_ORIENTATION:DS,MODE_SAME:"same"};function 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i,s,l;a&&(t.mode===DS?(r[0]=a[0]-a[3],r[1]=a[1]-a[4],r[2]=a[2]-a[5],qo(r),o.setPosition(t.focalPoint[0]+t.distance*r[0],t.focalPoint[1]+t.distance*r[1],t.focalPoint[2]+t.distance*r[2]),o.setFocalPoint(t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),o.setViewUp(a[6],a[7],a[8])):(o.setPosition(a[0],a[1],a[2]),o.setFocalPoint(a[3],a[4],a[5]),o.setViewUp(a[6],a[7],a[8])),t.mode===MS&&t.dstRenderer.resetCamera())},e.delete=Vt((()=>e.setSrcRenderer(null)),e.delete),a()}const LS={mode:DS,focalPoint:[0,0,0],distance:6.8,active:!0};function BS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LS,n),yt(e,t),At(e,t,["mode","active","srcRenderer","dstRenderer","distance"]),Pt(e,t,["focalPoint"],3,0),VS(e,t)}var NS={newInstance:Et(BS,"vtkCameraSynchronizer"),extend:BS,SynchronizationMode:ES};const _S={},FS={CameraSync:class{constructor(e,t){this.ctx=e,this.behavior=NS.newInstance(this.getProperties(t)),this.behavior.update()}getProperties(e){let{actorBounds:t,srcRenderer:n,dstRenderer:r}=e;return{distance:3.4*Math.max(t[1]-t[0],t[3]-t[2],t[5]-t[4]),focalPoint:[.5*(t[0]+t[1]),.5*(t[2]+t[3]),.5*(t[4]+t[5])],mode:NS.SynchronizationMode.MODE_ORIENTATION,srcRenderer:this.ctx.getInstance(n),dstRenderer:this.ctx.getInstance(r)}}update(e){this.behavior.set(this.getProperties(e)),this.behavior.update()}delete(){this.behavior.delete()}}};var kS=function(e,t,n){const r=e.get("synchronizedViewId").synchronizedViewId;if(!t.behaviors||!r)return;_S[r]||(_S[r]={});const o=_S[r];if(t.behaviors.autoOrientation){const t=e.getRenderers();if(!o.autoOrientationAxes&&2===t.length){let e=null,n=null;for(let r=0;r<t.length;r++){const o=t[r];o.getInteractive()?e=o:n=o}e&&n&&(o.autoOrientationAxes=NS.newInstance({srcRenderer:e,dstRenderer:n}))}o.autoOrientationAxes&&2!==t.length&&(o.autoOrientationAxes.delete(),delete o.autoOrientationAxes)}const a=Object.keys(t.behaviors),i=Object.keys(o);for(let e=0;e<a.length;e++){const r=a[e];if(o[r])o[r].update(t.behaviors[r]);else{const e=t.behaviors[r];FS[e.type]?o[r]=new FS[e.type](n,e):console.log("No mapping for",e)}}for(let e=0;e<i.length;e++){const n=a[e];t.behaviors[n]||(o[n].delete(),delete o[n])}};const GS={},US=/instance:\${([^}]+)}/,zS={},WS=[],HS={},jS={vtkPoints:mc,vtkCellArray:cc,vtkDataArray:Es};function KS(e){return e.map((e=>US.exec(e))).filter((e=>e)).map((e=>e[1]))}function $S(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return e.dependencies&&e.dependencies.forEach((e=>{t.push(e.id),$S(e,t)})),t}function qS(e,t,n,r){if(!t)return Promise.reject(new Error("No instance provided."));const o=GS[e];return 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+var base64Str = 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e[0]=s*(p*v-f*h)-d*(l*v-c*h)+m*(l*f-c*p),e[1]=-(r*(p*v-f*h)-d*(o*v-a*h)+m*(o*f-a*p)),e[2]=r*(l*v-c*h)-s*(o*v-a*h)+m*(o*c-a*l),e[3]=-(r*(l*f-c*p)-s*(o*f-a*p)+d*(o*c-a*l)),e[4]=-(i*(p*v-f*h)-u*(l*v-c*h)+g*(l*f-c*p)),e[5]=n*(p*v-f*h)-u*(o*v-a*h)+g*(o*f-a*p),e[6]=-(n*(l*v-c*h)-i*(o*v-a*h)+g*(o*c-a*l)),e[7]=n*(l*f-c*p)-i*(o*f-a*p)+u*(o*c-a*l),e[8]=i*(d*v-f*m)-u*(s*v-c*m)+g*(s*f-c*d),e[9]=-(n*(d*v-f*m)-u*(r*v-a*m)+g*(r*f-a*d)),e[10]=n*(s*v-c*m)-i*(r*v-a*m)+g*(r*c-a*s),e[11]=-(n*(s*f-c*d)-i*(r*f-a*d)+u*(r*c-a*s)),e[12]=-(i*(d*h-p*m)-u*(s*h-l*m)+g*(s*p-l*d)),e[13]=n*(d*h-p*m)-u*(r*h-o*m)+g*(r*p-o*d),e[14]=-(n*(s*h-l*m)-i*(r*h-o*m)+g*(r*l-o*s)),e[15]=n*(s*p-l*d)-i*(r*p-o*d)+u*(r*l-o*s),e}function y(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8],u=e[9],d=e[10],p=e[11],f=e[12],g=e[13],m=e[14],h=e[15];return(t*i-n*a)*(d*h-p*m)-(t*s-r*a)*(u*h-p*g)+(t*l-o*a)*(u*m-d*g)+(n*s-r*i)*(c*h-p*f)-(n*l-o*i)*(c*m-d*f)+(r*l-o*s)*(c*g-u*f)}function T(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return e[0]===t[0]&&e[1]===t[1]&&e[2]===t[2]&&e[3]===t[3]&&e[4]===t[4]&&e[5]===t[5]&&e[6]===t[6]&&e[7]===t[7]&&e[8]===t[8]&&e[9]===t[9]&&e[10]===t[10]&&e[11]===t[11]&&e[12]===t[12]&&e[13]===t[13]&&e[14]===t[14]&&e[15]===t[15]}function re(e,t){var n=e[0],r=e[1],o=e[2],i=e[3],s=e[4],l=e[5],c=e[6],u=e[7],d=e[8],p=e[9],f=e[10],g=e[11],m=e[12],h=e[13],v=e[14],y=e[15],T=t[0],b=t[1],x=t[2],C=t[3],S=t[4],A=t[5],I=t[6],w=t[7],P=t[8],O=t[9],R=t[10],M=t[11],E=t[12],D=t[13],V=t[14],L=t[15];return Math.abs(n-T)<=a*Math.max(1,Math.abs(n),Math.abs(T))&&Math.abs(r-b)<=a*Math.max(1,Math.abs(r),Math.abs(b))&&Math.abs(o-x)<=a*Math.max(1,Math.abs(o),Math.abs(x))&&Math.abs(i-C)<=a*Math.max(1,Math.abs(i),Math.abs(C))&&Math.abs(s-S)<=a*Math.max(1,Math.abs(s),Math.abs(S))&&Math.abs(l-A)<=a*Math.max(1,Math.abs(l),Math.abs(A))&&Math.abs(c-I)<=a*Math.max(1,Math.abs(c),Math.abs(I))&&Math.abs(u-w)<=a*Math.max(1,Math.abs(u),Math.abs(w))&&Math.abs(d-P)<=a*Math.max(1,Math.abs(d),Math.abs(P))&&Math.abs(p-O)<=a*Math.max(1,Math.abs(p),Math.abs(O))&&Math.abs(f-R)<=a*Math.max(1,Math.abs(f),Math.abs(R))&&Math.abs(g-M)<=a*Math.max(1,Math.abs(g),Math.abs(M))&&Math.abs(m-E)<=a*Math.max(1,Math.abs(m),Math.abs(E))&&Math.abs(h-D)<=a*Math.max(1,Math.abs(h),Math.abs(D))&&Math.abs(v-V)<=a*Math.max(1,Math.abs(v),Math.abs(V))&&Math.abs(y-L)<=a*Math.max(1,Math.abs(y),Math.abs(L))}var oe=T,ae=J;function ie(){var e=new i(9);return i!=Float32Array&&(e[1]=0,e[2]=0,e[3]=0,e[5]=0,e[6]=0,e[7]=0),e[0]=1,e[4]=1,e[8]=1,e}function se(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[4],e[4]=t[5],e[5]=t[6],e[6]=t[8],e[7]=t[9],e[8]=t[10],e}function le(e){var t=new i(9);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t}function ce(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function ue(e,t,n,r,o,a,s,l,c){var u=new i(9);return u[0]=e,u[1]=t,u[2]=n,u[3]=r,u[4]=o,u[5]=a,u[6]=s,u[7]=l,u[8]=c,u}function de(e,t,n,r,o,a,i,s,l,c){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e}function pe(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function fe(e,t){if(e===t){var n=t[1],r=t[2],o=t[5];e[1]=t[3],e[2]=t[6],e[3]=n,e[5]=t[7],e[6]=r,e[7]=o}else e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8];return e}function ge(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=u*i-s*c,p=-u*a+s*l,f=c*a-i*l,g=n*d+r*p+o*f;return g?(g=1/g,e[0]=d*g,e[1]=(-u*r+o*c)*g,e[2]=(s*r-o*i)*g,e[3]=p*g,e[4]=(u*n-o*l)*g,e[5]=(-s*n+o*a)*g,e[6]=f*g,e[7]=(-c*n+r*l)*g,e[8]=(i*n-r*a)*g,e):null}function me(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8];return e[0]=i*u-s*c,e[1]=o*c-r*u,e[2]=r*s-o*i,e[3]=s*l-a*u,e[4]=n*u-o*l,e[5]=o*a-n*s,e[6]=a*c-i*l,e[7]=r*l-n*c,e[8]=n*i-r*a,e}function he(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8];return t*(c*a-i*l)+n*(-c*o+i*s)+r*(l*o-a*s)}function ve(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=n[0],f=n[1],g=n[2],m=n[3],h=n[4],v=n[5],y=n[6],T=n[7],b=n[8];return e[0]=p*r+f*i+g*c,e[1]=p*o+f*s+g*u,e[2]=p*a+f*l+g*d,e[3]=m*r+h*i+v*c,e[4]=m*o+h*s+v*u,e[5]=m*a+h*l+v*d,e[6]=y*r+T*i+b*c,e[7]=y*o+T*s+b*u,e[8]=y*a+T*l+b*d,e}function ye(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=n[0],f=n[1];return e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=p*r+f*i+c,e[7]=p*o+f*s+u,e[8]=p*a+f*l+d,e}function Te(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=Math.sin(n),f=Math.cos(n);return e[0]=f*r+p*i,e[1]=f*o+p*s,e[2]=f*a+p*l,e[3]=f*i-p*r,e[4]=f*s-p*o,e[5]=f*l-p*a,e[6]=c,e[7]=u,e[8]=d,e}function be(e,t,n){var r=n[0],o=n[1];return e[0]=r*t[0],e[1]=r*t[1],e[2]=r*t[2],e[3]=o*t[3],e[4]=o*t[4],e[5]=o*t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function xe(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=t[0],e[7]=t[1],e[8]=1,e}function Ce(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=-n,e[4]=r,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function Se(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=t[1],e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function Ae(e,t){return e[0]=t[0],e[1]=t[1],e[2]=0,e[3]=t[2],e[4]=t[3],e[5]=0,e[6]=t[4],e[7]=t[5],e[8]=1,e}function Ie(e,t){var 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||n[0]!==t.mapperBounds[0]||n[1]!==t.mapperBounds[1]||n[2]!==t.mapperBounds[2]||n[3]!==t.mapperBounds[3]||n[4]!==t.mapperBounds[4]||n[5]!==t.mapperBounds[5]||e.getMTime()>t.boundsMTime.getMTime()){ps("Recomputing bounds..."),t.mapperBounds=n.concat();const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}return t.bounds},e.getMTime=()=>{let e=n.getMTime();if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}if(null!==t.backfaceProperty){const n=t.backfaceProperty.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection(),e.processSelectorPixelBuffers=(e,n)=>{t.mapper&&t.mapper.processSelectorPixelBuffers&&t.mapper.processSelectorPixelBuffers(e,n)}}const gs={mapper:null,property:null,backfaceProperty:null,forceOpaque:!1,forceTranslucent:!1,bounds:[1,-1,1,-1,1,-1]};function ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gs,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["backfaceProperty","forceOpaque","forceTranslucent","mapper"]),fs(e,t)}var hs={newInstance:jt.newInstance(ms,"vtkActor"),extend:ms};const 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t.size>n*r&&(t.size=n*r,e.dataChange()),!0}t.classHierarchy.push("vtkDataArray"),e.dataChange=()=>{t.ranges=null,e.modified()},e.resize=r=>{n(r);const o=r*e.getNumberOfComponents();return t.size!==o&&(t.size=o,e.dataChange(),!0)},e.initialize=()=>{e.resize(0)},e.getElementComponentSize=()=>t.values.BYTES_PER_ELEMENT,e.getComponent=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return t.values[e*t.numberOfComponents+n]},e.setComponent=(n,r,o)=>{o!==t.values[n*t.numberOfComponents+r]&&(t.values[n*t.numberOfComponents+r]=o,e.dataChange())},e.getValue=n=>{const r=n/t.numberOfComponents,o=n%t.numberOfComponents;return e.getComponent(r,o)},e.setValue=(n,r)=>{const o=n/t.numberOfComponents,a=n%t.numberOfComponents;e.setComponent(o,a,r)},e.getData=()=>t.size===t.values.length?t.values:t.values.subarray(0,t.size),e.getRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,r=n;r<0&&(r=1===t.numberOfComponents?0:t.numberOfComponents);let o=null;return t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents)),o=t.ranges[r],o?(t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple):(o=As(e.getData(),n,t.numberOfComponents),t.ranges[r]=o,t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple)},e.setRange=(e,n)=>{t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents));const r={min:e.min,max:e.max};return t.ranges[n]=r,t.rangeTuple[0]=r.min,t.rangeTuple[1]=r.max,t.rangeTuple},e.setTuple=(e,n)=>{const r=e*t.numberOfComponents;for(let e=0;e<t.numberOfComponents;e++)t.values[r+e]=n[e]},e.setTuples=(e,n)=>{let r=e*t.numberOfComponents;const o=Math.min(n.length,t.size-r);for(let e=0;e<o;)t.values[r++]=n[e++]},e.insertTuple=(r,o)=>(t.size<=r*t.numberOfComponents&&(t.size=(r+1)*t.numberOfComponents,n(r+1)),e.setTuple(r,o),r),e.insertTuples=(r,o)=>{const a=r+o.length/t.numberOfComponents;return t.size<a*t.numberOfComponents&&(t.size=a*t.numberOfComponents,n(a)),e.setTuples(r,o),a},e.insertNextTuple=n=>{const r=t.size/t.numberOfComponents;return e.insertTuple(r,n)},e.insertNextTuples=n=>{const r=t.size/t.numberOfComponents;return e.insertTuples(r,n)},e.findTuple=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Cs;for(let r=0;r<t.size;r+=t.numberOfComponents)if(Math.abs(e[0]-t.values[r])<=n){let o=!0;for(let a=1;a<t.numberOfComponents;++a)if(Math.abs(e[a]-t.values[r+a])>n){o=!1;break}if(o)return r/t.numberOfComponents}return-1},e.getTuple=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];const r=t.numberOfComponents||1,o=e*r;switch(r){case 4:n[3]=t.values[o+3];case 3:n[2]=t.values[o+2];case 2:n[1]=t.values[o+1];case 1:n[0]=t.values[o];break;default:for(let e=r-1;e>=0;--e)n[e]=t.values[o+e]}return n},e.getTuples=(n,r)=>{const o=(n??0)*t.numberOfComponents,a=(r??e.getNumberOfTuples())*t.numberOfComponents,i=e.getData().subarray(o,a);return i.length>0?i:null},e.getTupleLocation=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1;return e*t.numberOfComponents},e.getNumberOfComponents=()=>t.numberOfComponents,e.getNumberOfValues=()=>t.size,e.getNumberOfTuples=()=>t.size/t.numberOfComponents,e.getDataType=()=>t.dataType,e.newClone=()=>Es({empty:!0,name:t.name,dataType:t.dataType,numberOfComponents:t.numberOfComponents}),e.getName=()=>(t.name||(e.modified(),t.name=`vtkDataArray${e.getMTime()}`),t.name),e.setData=(n,r)=>{t.values=n,t.size=n.length,t.dataType=ws(n),r&&(t.numberOfComponents=r),t.size%t.numberOfComponents!=0&&(t.numberOfComponents=1),e.dataChange()},e.getState=()=>{if(t.deleted)return null;const n={...t,vtkClass:e.getClassName()};n.values=Array.from(n.values),delete n.buffer,Object.keys(n).forEach((e=>{n[e]||delete n[e]}));const r={};return Object.keys(n).sort().forEach((e=>{r[e]=n[e]})),r.mtime&&delete r.mtime,r},e.deepCopy=n=>{const r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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r=t.clippingPlanes.indexOf(n);return-1!==r&&(t.clippingPlanes.splice(r,1),e.modified(),!0)},e.getClippingPlanes=()=>t.clippingPlanes,e.setClippingPlanes=t=>{if(t)if(Array.isArray(t)){const n=t.length;for(let r=0;r<n&&r<6;r++)e.addClippingPlane(t[r])}else e.addClippingPlane(t)},e.getClippingPlaneInDataCoords=(e,n,r)=>{const o=t.clippingPlanes,a=e;if(o){const e=o.length;if(n>=0&&n<e){const e=o[n],t=e.getNormal(),i=e.getOrigin(),s=t[0],l=t[1],c=t[2],u=-(s*i[0]+l*i[1]+c*i[2]);return r[0]=s*a[0]+l*a[4]+c*a[8]+u*a[12],r[1]=s*a[1]+l*a[5]+c*a[9]+u*a[13],r[2]=s*a[2]+l*a[6]+c*a[10]+u*a[14],void(r[3]=s*a[3]+l*a[7]+c*a[11]+u*a[15])}}jt.vtkErrorMacro(`Clipping plane index ${n} is out of range.`)}}const Ls={clippingPlanes:[]};var Bs=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ls,n),jt.obj(e,t),jt.algo(e,t,1,0),t.clippingPlanes||(t.clippingPlanes=[]),Vs(e,t)};function Ns(e,t){e.getBounds=()=>(jt.vtkErrorMacro("vtkAbstractMapper3D.getBounds - NOT IMPLEMENTED"),Fa()),e.getCenter=()=>{const n=e.getBounds();return t.center=ki.isValid(n)?ki.getCenter(n):null,t.center?.slice()},e.getLength=()=>{const t=e.getBounds();return ki.getDiagonalLength(t)}}const _s=e=>({bounds:[...ki.INIT_BOUNDS],center:[0,0,0],viewSpecificProperties:{},...e});var Fs=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_s(n)),Bs(e,t,n),jt.setGet(e,t,["viewSpecificProperties"]),Ns(e,t)};const{vtkErrorMacro:ks,vtkWarningMacro:Gs}=jt;function Us(e,t){t.classHierarchy.push("vtkFieldData");const n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case in passData");else if(r<0||r>a.getNumberOfTuples())e.addArray(a),n.getAttributes(a).forEach((t=>{e.setAttribute(a,t)}));else{const i=a.getNumberOfComponents();let s=a.getNumberOfValues();const l=o>-1?o:r;s<=l*i&&(s=(l+1)*i),t=Ds.newInstance({name:a.getName(),dataType:a.getDataType(),numberOfComponents:i,values:jt.newTypedArray(a.getDataType(),s),size:0}),t.insertTuple(l,a.getTuple(r)),e.addArray(t),n.getAttributes(a).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.interpolateData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:-1,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5;n.getArrays().forEach((s=>{const l=e.getFlag(s.getName());if(!1!==l&&(!t.doCopyAllOff||!0===l)&&s){let t=e.getArrayByName(s.getName());if(t)if(s.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<s.getNumberOfTuples()){const e=a>-1?a:r;t.interpolateTuple(e,s,r,s,o,i),Gs("Unexpected case in interpolateData")}else t.insertTuples(s.getTuples());else ks("Unhandled case in interpolateData");else if(r<0||o<0||r>s.getNumberOfTuples())e.addArray(s),n.getAttributes(s).forEach((t=>{e.setAttribute(s,t)}));else{const l=s.getNumberOfComponents();let c=s.getNumberOfValues();const u=a>-1?a:r;c<=u*l&&(c=(u+1)*l),t=Ds.newInstance({name:s.getName(),dataType:s.getDataType(),numberOfComponents:l,values:jt.newTypedArray(s.getDataType(),c),size:0}),t.interpolateTuple(u,s,r,s,o,i),e.addArray(t),n.getAttributes(s).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.copyFieldOn=e=>{t.copyFieldFlags[e]=!0},e.copyFieldOff=e=>{t.copyFieldFlags[e]=!1},e.copyAllOn=()=>{t.doCopyAllOn&&!t.doCopyAllOff||(t.doCopyAllOn=!0,t.doCopyAllOff=!1,e.modified())},e.copyAllOff=()=>{!t.doCopyAllOn&&t.doCopyAllOff||(t.doCopyAllOn=!1,t.doCopyAllOff=!0,e.modified())},e.clearFieldFlags=()=>{t.copyFieldFlags={}},e.deepCopy=e=>{t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. 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Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const g=t.numberOfColorsInRange+3,m=t.numberOfColorsInRange+2,h=r?[Math.min(Math.ceil(g/n**0),n),Math.min(Math.ceil(g/n**1),n),Math.min(Math.ceil(g/n**2),n)]:[m,2,1],v=h[0]*h[1]*h[2],y=new Float64Array(v);y.fill(NaN);const T=t.numberOfColorsInRange,b=T+2,x=[0,0,0],C=a[0],S=a[1]-a[0];for(let e=0;e<b;++e){const t=C+S*(e-1)/(T-1);y[(c=h,(l=x)[0]+c[0]*(l[1]+c[1]*l[2]))]=i?10**t:t,ec(x,h)}const A=Ds.newInstance({numberOfComponents:1,values:y}),I=t.lookupTable.mapScalars(A,t.colorMode,0);t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setDimensions(h),t.colorTextureMap.getPointData().setScalars(I),t.lookupTable.setAlpha(s)}var l,c;const u=t.lookupTable.getVectorMode()===Zl.MAGNITUDE&&n.getNumberOfComponents()>1?-1:t.lookupTable.getVectorComponent();t.colorCoordinates=function(e,t,n,r,o,a,i){const s=new Array(arguments.length);for(let e=0;e<arguments.length;++e){const t=arguments[e];s[e]=t.getMTime?.()??t}const l=s.join("/"),c=nc.get(e);if(c&&c.stringHash===l)return c.textureCoordinates;const u=(n[1]-n[0])/(r-1),[d,p]=[n[0]-u,n[1]+u],f=d-.5*u,g=1/(p-d+u),m=d,h=(r+1)/(p-d),v=e.getData(),y=e.getNumberOfTuples(),T=e.getNumberOfComponents(),b=t<0||t>=T,x=o[2]<=1?2:3,C=Ds.newInstance({numberOfComponents:x,values:new Float32Array(y*x)}),S=C.getData(),A=[0,0,0];tc(A,r+2,o);let I=0,w=0;const P=[.5,.5,.5];for(let e=0;e<y;++e){let e;if(b){let t=0;for(let e=0;e<T;++e){const n=v[I+e];t+=n*n}e=Math.sqrt(t)}else e=v[I+t];if(I+=T,a&&(e=Dl.applyLogScale(e,n,n)),_a(e))P[0]=A[0],P[1]=A[1],P[2]=A[2];else if(i){let t=(e-m)*h;t<1?t=0:t>r&&(t=r+1),tc(P,t,o)}else{P[1]=.49;const t=(e-f)*g;P[0]=t>1e3?1e3:t<-1e3?-1e3:t}for(let e=0;e<x;++e)S[w++]=P[e]}return nc.set(e,{stringHash:l,textureCoordinates:C}),C}(n,u,a,t.numberOfColorsInRange,t.colorTextureMap.getDimensions(),i,r)},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=(e,n)=>!((!n||t.colorMode===ql.DIRECT_SCALARS)&&(!t.interpolateScalarsBeforeMapping||t.lookupTable&&t.lookupTable.getIndexedLookup()||!e||t.colorMode===ql.DEFAULT&&e.getDataType()===Ql.UNSIGNED_CHAR||t.colorMode===ql.DIRECT_SCALARS)),e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Jl("AcquireInvertibleLookupTable"),e.valueToColor=Jl("ValueToColor"),e.colorToValue=Jl("ColorToValue"),e.useInvertibleColorFor=Jl("UseInvertibleColorFor"),e.clearInvertibleColor=Jl("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(Wl.ID_LOW24),o=e.getRawPixelBuffer(Wl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===jl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===jl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===Wl.ID_LOW24){let 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let 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Oc=0,Rc=1,Mc=2,Ec=3,Dc=4,Vc=5,Lc=6,Bc=7,Nc=9,_c=21,Fc=41,kc=42,Gc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],Uc={getClassNameFromTypeId:function(e){return e<Gc.length?Gc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Gc.findIndex(e)},isLinear:function(e){return e<_c||e===Fc||e===kc},hasSubCells:function(e){return e===Lc||e===Dc||e===Mc}};function zc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Oc},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let r=0;r<n;++r)if(t===e.getCellType(r))return!0;return!1},e.insertNextType=t=>e.insertNextCell(t,-1),e.getCellType=e=>t.typeArray[e],e.reset=()=>{t.maxId=-1},e.deepCopy=n=>{e.allocate(n.getSize(),n.getExtend()),t.typeArray.set(n.getTypeArray()),t.locationArray.set(n.getLocationArray()),t.maxId=n.getMaxId()}}const Wc={size:0,maxId:-1,extend:1e3};function Hc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wc,n),jt.obj(e,t),jt.get(e,t,["size","maxId","extend"]),jt.getArray(e,t,["typeArray","locationArray"]),zc(e,t)}var jc={newInstance:jt.newInstance(Hc,"vtkCellTypes"),extend:Hc,...Uc},Kc={IntersectionState:{NO_INTERSECTION:0,YES_INTERSECTION:1,ON_LINE:2}};const{IntersectionState:$c}=Kc;function qc(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;const o={t:Number.MIN_VALUE,distance:0},a=[];let i;a[0]=n[0]-t[0],a[1]=n[1]-t[1],a[2]=n[2]-t[2];const s=a[0]*(e[0]-t[0])+a[1]*(e[1]-t[1])+a[2]*(e[2]-t[2]),l=jo(a,a);let c=1e-5*s;return 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t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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du={newInstance:jt.newInstance(uu,"vtkTriangle"),extend:uu,...su};const pu=["verts","lines","polys","strips"],{vtkWarningMacro:fu}=jt,gu={[Ec]:eu,[Dc]:eu,[Vc]:du};function mu(e,t){t.classHierarchy.push("vtkPolyData"),pu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=fc.newInstance()})),e.getNumberOfCells=()=>pu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),pu.forEach((n=>{t[n]=fc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let 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o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||gu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const hu={};function vu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hu,n),ou.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),mu(e,t)}var yu={newInstance:jt.newInstance(vu,"vtkPolyData"),extend:vu};function Tu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const bu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function xu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bu,n),jt.obj(e,t),jt.algo(e,t,6,0),jt.get(e,t,["canvas","image","jsImageData","imageLoaded","resizable"]),jt.setGet(e,t,["repeat","edgeClamp","interpolate","mipLevel"]),Tu(e,t)}const Cu={generateMipmaps:(e,t,n,r)=>{const o=[1,2,1],a=e.length/(t*n);let i=t,s=n,l=e;const c=[l];for(let e=0;e<r;e++){const e=[...l];i/=2,s/=2,l=new Uint8ClampedArray(i*s*a);const t=a*i;let n=0;for(let r=0;r<l.length;r+=a){r%t==0&&(n+=2*a*i);for(let o=0;o<a;o++){let i=e[n+o];i+=e[n+a+o],i+=e[n-2*t+o],i+=e[n-2*t+a+o],i/=4,l[r+o]=i}n+=2*a}let r=[...l];for(let e=0;e<l.length;e+=a)for(let n=0;n<a;n++){let i=-(o.length-1)/2,s=4,c=0;for(let l=0;l<o.length;l++){let u=e+n+i*a;const d=u%t-(e+n)%t;d>a&&(u+=t),d<-a&&(u-=t),r[u]?c+=r[u]*o[l]:s-=o[l],i+=1}l[e+n]=c/s}r=[...l];for(let e=0;e<l.length;e+=a)for(let n=0;n<a;n++){let a=-(o.length-1)/2,i=4,s=0;for(let l=0;l<o.length;l++){const c=e+n+a*t;r[c]?s+=r[c]*o[l]:i-=o[l],a+=1}l[e+n]=s/i}c.push(l)}return c}};var Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=gd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const 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input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=mg(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.oglObject?.getHandle()&&m?.hash===g)t.colorTexture=m.oglObject;else{t.colorTexture=Ud.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(n*p*3);if(c===og.NEAREST?(t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST)):(t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR)),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader VAO."))),n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),r.getIsIdentity())o.isUniformUsed("MCVCMatrix")&&o.setUniformMatrix("MCVCMatrix",i.wcvc),o.isUniformUsed("normalMatrix")&&o.setUniformMatrix3x3("normalMatrix",i.normalMatrix);else{const e=t.openGLActor.getKeyMatrices();if(o.isUniformUsed("MCVCMatrix")){const t=new Float64Array(16);T(t,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",t)}if(o.isUniformUsed("normalMatrix")){const t=new Float64Array(9);ve(t,i.normalMatrix,e.normalMatrix),o.setUniformMatrix3x3("normalMatrix",t)}}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():Mm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let n=t.renderable.getLength(),r=t.renderable.getRadius();m&&(n=m[2*e],r=m[2*e+1]);for(let t=0;t<v.length;++t)y=3*e,f[b++]=c[y++],f[b++]=c[y++],f[b++]=c[y++],y=3*e,f[b++]=h[y++]*n,f[b++]=h[y++]*n,f[b++]=h[y++]*n,f[b++]=r,g[x++]=v[t]%2*255,g[x++]=v[t]>=4?255:0,g[x++]=v[t]>=2?255:0,g[x++]=255,T=e*p,d&&(g[x++]=d[T],g[x++]=d[T+1],g[x++]=d[T+2],g[x++]=d[T+3])}a.setElementCount(b/7),a.upload(f,$u.ARRAY_BUFFER),a.getColorBO().upload(g,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Dm={};const Vm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dm,n),sp.extend(e,t,n),Em(e,t)}),"vtkOpenGLStickMapper");rn("vtkStickMapper",Vm);const Lm=[];Lm["-".charCodeAt(0)]=62,Lm["_".charCodeAt(0)]=63;const Bm="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";for(let e=0;e<Bm.length;e++)Lm[Bm.charCodeAt(e)]=e;function Nm(e){return void 0!==Lm[e.charCodeAt(0)]}function _m(e,t,n,r){const{start:o,count:a}=t,i=a%4,s=Math.floor(a/4);let l=o,c=null,u=n;for(let t=0;t<s;t++){for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<18;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<12;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<6;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)],r[u++]=c>>16&255,r[u++]=c>>8&255,r[u++]=255&c}switch(i){case 3:for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<10;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<4;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)]>>2,r[u++]=c>>8&255,r[u++]=255&c;break;case 2:for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<2;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)]>>4,r[u++]=255&c;break;case 1:throw new Error("BASE64: remain 1 should not happen")}return u}function Fm(e,t,n){const r=(e<<16)+(t<<8)+n;return Bm[r>>18]+Bm[r>>12&63]+Bm[r>>6&63]+Bm[63&r]}function km(e){const t=new Uint8Array(e),n=e.byteLength%3,r=e.byteLength-n,o=Array(r/3);for(let e=0;e<o.length;e++){const n=3*e;o[e]=Fm(t[n],t[n+1],t[n+2])}if(n>0){const 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r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return tv("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return tv("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return tv("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return tv("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),ev("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return ev("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return tv("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return a[1]-a[0]<i&&(i=i-a[1]+a[0],a[1]+=i/2,a[0]-=i/2),a[0]<0&&(a[0]=0),a[0]=.99*a[0]-(a[1]-a[0])*t.clippingRangeExpansion,a[1]=1.01*a[1]+(a[1]-a[0])*t.clippingRangeExpansion,a[0]=a[0]>=a[1]?.01*a[1]:a[0],t.nearClippingPlaneTolerance||(t.nearClippingPlaneTolerance=.01),a[0]<t.nearClippingPlaneTolerance*a[1]&&(a[0]=t.nearClippingPlaneTolerance*a[1]),t.activeCamera.setClippingRange(a[0],a[1]),e.invokeEvent(r),!1},e.setRenderWindow=e=>{e!==t._renderWindow&&(t._vtkWindow=e,t._renderWindow=e)},e.visibleActorCount=()=>t.props.filter((e=>e.getVisibility())).length,e.visibleVolumeCount=e.visibleActorCount,e.getMTime=()=>{let e=t.mtime;const n=t.activeCamera?t.activeCamera.getMTime():0;n>e&&(e=n);const r=t._createdLight?t._createdLight.getMTime():0;return r>e&&(e=r),e},e.getTransparent=()=>!!t.preserveColorBuffer,e.isActiveCameraCreated=()=>!!t.activeCamera}const av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&Pv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:xv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an animation`)}},e.switchToXRAnimation=()=>{t.animationRequest&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null),t.xrAnimation=!0},e.returnFromXRAnimation=()=>{t.xrAnimation=!1,0!==r.size&&(t.recentAnimationFrameRate=10,t.animationRequest=requestAnimationFrame(e.handleAnimation))},e.updateXRGamepads=(n,r,o)=>{n.inputSources.forEach((n=>{const a=null==n.gripSpace?null:r.getPose(n.gripSpace,o),i=null==n.gripSpace?null:r.getPose(n.targetRaySpace,o),s=n.gamepad,l=n.handedness;if(s){s.index in t.lastGamepadValues||(t.lastGamepadValues[s.index]={left:{buttons:{}},right:{buttons:{}},none:{buttons:{}}});for(let r=0;r<s.buttons.length;++r)r in t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const r={...Cv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:xv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Ov(o);if(2===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(Av),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Ov(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(Av),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Ov(o);e.recognizeGesture("TouchEnd",t);const a={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Ov(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},wv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void Sv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const ty={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function ny(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ty,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),ey(e,t)}var ry={newInstance:jt.newInstance(ny,"vtkRenderPass"),extend:ny};const{Representation:oy}=ds,{vtkErrorMacro:ay}=jt;function iy(e){const t=gd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function sy(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Ds.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Ds.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",oy.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||ay("Error setting vertexDC in copy shader 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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a;e.onModified((function(){t.renderable&&(t.size[0]===o[0]&&t.size[1]===o[1]||(o[0]=t.size[0],o[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]))),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.addMissingNodes(t.renderable.getChildRenderWindowsByReference()),e.removeUnusedNodes(),e.initialize(),t.children.forEach((t=>{t.setOpenGLRenderWindow?.(e)}))}},e.initialize=()=>{if(!t.initialized){if(t.rootOpenGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.rootOpenGLRenderWindow)t.context2D=e.get2DContext();else{t.context=e.get3DContext(),e.resizeFromChildRenderWindows(),t.context&&(Ny++,_y.forEach((e=>e(Ny)))),t.textureUnitManager=wy.newInstance(),t.textureUnitManager.setContext(t.context),t.shaderCache.setContext(t.context);const 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rendererUBO.SCPCMatrix*vertexBC;"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderTCoord=(e,t,n)=>{t.getShaderDescription("vertex").addOutput("vec2<f32>","tcoordVS")},t.shaderReplacements.set("replaceShaderTCoord",e.replaceShaderTCoord),e.addTextureView=e=>{t.textureViews.includes(e)||t.textureViews.push(e)},e.prepareToDraw=n=>{t.renderEncoder=n,e.updateInput(),e.updateBuffers(),e.updateBindings(),e.updatePipeline()},e.updateInput=()=>{},e.updateBuffers=()=>{},e.updateBindings=()=>{t.bindGroup.setBindables(e.getBindables())},e.computePipelineHash=()=>{},e.registerDrawCallback=n=>{n.registerDrawCallback(t.pipeline,e.draw)},e.prepareAndDraw=n=>{e.prepareToDraw(n),n.setPipeline(t.pipeline),e.draw(n)},e.draw=e=>{const n=e.getBoundPipeline();e.activateBindGroup(t.bindGroup),t.WebGPURenderer&&t.WebGPURenderer.bindUBO(e),n.bindVertexInput(e,t.vertexInput);const r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pb,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),db(e,t)}var gb={newInstance:jt.newInstance(fb),extend:fb};function mb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const r=fT(n);t.bufferEntries.push({name:e,type:n,sizeInBytes:r,offset:t.sizeInBytes,nativeType:gT(n)}),t.sizeInBytes+=r},e.send=e=>{if(!t._buffer){const n={nativeArray:t.Float32Array,usage:Fb.Storage,label:t.label};return t._buffer=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),void t._sendTime.modified()}e.getHandle().queue.writeBuffer(t._buffer.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes*t.numberOfInstances),t._sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes*t.numberOfInstances)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r,o)=>{const a=t._bufferEntryNames.get(n);if(void 0===a)return void kb(`entry named ${n} not found in UBO`);const i=t.bufferEntries[a];e.createView(i.nativeType);const s=t[i.nativeType];s[(i.offset+r*t.sizeInBytes)/s.BYTES_PER_ELEMENT]=o},e.setArray=(n,r,o)=>{const a=t._bufferEntryNames.get(n);if(void 0===a)return void kb(`entry named ${n} not found in UBO`);const i=t.bufferEntries[a];e.createView(i.nativeType);const s=t[i.nativeType],l=(i.offset+r*t.sizeInBytes)/s.BYTES_PER_ELEMENT;for(let e=0;e<o.length;e++)s[l+e]=o[e]},e.setAllInstancesFromArray=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType],s=r.length/t.numberOfInstances;for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<s;t++)i[n+t]=r[e*s+t]}},e.setAllInstancesFromArrayColorToFloat=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType],s=r.length/t.numberOfInstances;for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<s;t++)i[n+t]=r[e*s+t]/255}},e.setAllInstancesFromArray3x3To4x4=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],D=r[2],E=Math.hypot(R,M,D);return E<a?null:(R*=E=1/E,M*=E,D*=E,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+D*o,C=D*R*s-M*o,S=R*M*s-D*o,A=M*M*s+i,I=D*M*s+R*o,w=R*D*s+M*o,P=M*D*s-R*o,O=D*D*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),E(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,D=(1-(p+v))*C,E=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=D,e[6]=E,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+D*I+L*w),e[14]=n[2]+w-(R*A+E*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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Qi(e,t){t.classHierarchy.push("vtkProp3D"),e.addPosition=n=>{t.position=t.position.map(((e,t)=>e+n[t])),e.modified()},e.getOrientationWXYZ=()=>{const e=Ui();N(e,t.rotation);const n=new Float64Array(3),r=function(e,t){var n=2*Math.acos(t[3]),r=Math.sin(n/2);return r>a?(e[0]=t[0]/r,e[1]=t[1]/r,e[2]=t[2]/r):(e[0]=1,e[1]=0,e[2]=0),n}(n,e);return[Ro(r),n[0],n[1],n[2]]},e.rotateX=n=>{0!==n&&(S(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateY=n=>{0!==n&&(A(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateZ=n=>{0!==n&&(I(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateWXYZ=(n,r,o,a)=>{if(0===n||0===r&&0===o&&0===a)return;const i=Oo(n),s=Ui();zi(s,[r,o,a],i);const l=new 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Es,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&t.getDataType()===Tl.UNSIGNED_CHAR||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Es.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Es.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Es.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function Dl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var El={newInstance:jt.newInstance(Dl,"vtkLookupTable"),extend:Dl};let Vl=1,Ll=0;const Bl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function Nl(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;Ll=e}var _l={getResolveCoincidentTopologyAsString:function(){return Bl[Ll]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Vl},getResolveCoincidentTopology:function(){return Ll},setResolveCoincidentTopology:Nl,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){Vl=e},setResolveCoincidentTopologyToDefault:function(){Nl(0)},setResolveCoincidentTopologyToOff:function(){Nl(0)},setResolveCoincidentTopologyToPolygonOffset:function(){Nl(1)}};function Fl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=(e,r)=>{t[n.key]={factor:e,offset:r}}}))}const kl=["Polygon","Line","Point"],Gl={};Fl(Gl,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},kl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var Ul={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(_l).forEach((t=>{e[t]=_l[t]})),Object.keys(Gl).forEach((t=>{e[t]=Gl[t]})),Fl(e,t.topologyOffset,kl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Gl.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Gl,otherStaticMethods:_l,CATEGORIES:kl};const zl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Wl={PassTypes:zl};const{FieldAssociations:Hl}=ol,{staticOffsetAPI:jl,otherStaticMethods:Kl}=Ul,{ColorMode:$l,ScalarMode:ql,GetArray:Xl}=hl,{VectorMode:Yl}=ml,{VtkDataTypes:Zl}=Es;function Ql(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function Jl(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString($l,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(ql,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===ql.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===ql.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===ql.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===ql.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_FIELD_DATA){const t=e.getFieldData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==a){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(n))e.mapScalarsToTexture(o,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},e.scalarToTextureCoordinate=(e,t,n)=>{let r=.5,o=1;return _a(e)||(o=.49,r=(e-t)*n,r>1e3?r=1e3:r<-1e3&&(r=-1e3)),{texCoordS:r,texCoordT:o}},e.createColorTextureCoordinates=(t,n,r,o,a,i,s,l,c)=>{const u=(i[1]-i[0])/l,d=[];d[0]=i[0]-u,d[1]=i[1]+u;const p=1/(d[1]-d[0]),f=n.getData(),g=t.getData();let m=0,h=0;if(a<0||a>=o)for(let t=0;t<r;++t){let t=0;for(let e=0;e<o;++e)t+=g[m]*g[m],m++;let n=Math.sqrt(t);c&&(n=El.applyLogScale(n,s,i));const r=e.scalarToTextureCoordinate(n,d[0],p);f[h]=r.texCoordS,f[h+1]=r.texCoordT,h+=2}else{m+=a;for(let t=0;t<r;++t){let t=g[m];c&&(t=El.applyLogScale(t,s,i));const n=e.scalarToTextureCoordinate(t,d[0],p);f[h]=n.texCoordS,f[h+1]=n.texCoordT,h+=2,m+=o}}},e.mapScalarsToTexture=(n,r)=>{const o=t.lookupTable.getRange(),a=t.lookupTable.usingLogScale();a&&El.getLogRange(o,o);const i=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==r){t.lookupTable.setAlpha(r),t.colorTextureMap=null,t.lookupTable.build();let e=t.lookupTable.getNumberOfAvailableColors();e>4094&&(e=4094),e<64&&(e=64),e+=2;const n=(o[1]-o[0])/(e-2),s=new Float64Array(2*e);for(let t=0;t<e;++t)s[t]=o[0]+t*n-n/2,a&&(s[t]=10**s[t]);for(let t=0;t<e;++t)s[t+e]=NaN;t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setExtent(0,e-1,0,1,0,0);const l=Es.newInstance({numberOfComponents:1,values:s});t.colorTextureMap.getPointData().setScalars(t.lookupTable.mapScalars(l,t.colorMode,0)),t.lookupTable.setAlpha(i)}if(!t.colorCoordinates||e.getMTime()>t.colorCoordinates.getMTime()||e.getInputData(0).getMTime()>t.colorCoordinates.getMTime()||t.lookupTable.getMTime()>t.colorCoordinates.getMTime()){t.colorCoordinates=null;const r=n.getNumberOfComponents(),i=n.getNumberOfTuples();t.colorCoordinates=Es.newInstance({numberOfComponents:2,values:new Float32Array(2*i)});let s=t.lookupTable.getVectorComponent();t.lookupTable.getVectorMode()===Yl.MAGNITUDE&&n.getNumberOfComponents()>1&&(s=-1),e.createColorTextureCoordinates(n,t.colorCoordinates,i,r,s,o,t.lookupTable.getRange(),t.colorTextureMap.getPointData().getScalars().getNumberOfTuples()/2-2,a)}},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=n=>{if(!t.interpolateScalarsBeforeMapping)return!1;if(t.lookupTable&&t.lookupTable.getIndexedLookup())return!1;const r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName),o=r.scalars;return!(!o||r.cellFlag||t.colorMode===$l.DEFAULT&&o.getDataType()===Zl.UNSIGNED_CHAR||t.colorMode===$l.DIRECT_SCALARS)},e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Ql("AcquireInvertibleLookupTable"),e.valueToColor=Ql("ValueToColor"),e.colorToValue=Ql("ColorToValue"),e.useInvertibleColorFor=Ql("UseInvertibleColorFor"),e.clearInvertibleColor=Ql("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(zl.ID_LOW24),o=e.getRawPixelBuffer(zl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===Hl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===Hl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===zl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(zl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===zl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(zl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const ec={colorMapColors:null,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function tc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ec,n),Fs(e,t,n),jt.get(e,t,["colorCoordinates","colorMapColors","colorTextureMap","selectionWebGLIdsToVTKIds"]),jt.setGet(e,t,["colorByArrayName","arrayAccessMode","colorMode","fieldDataTupleId","interpolateScalarsBeforeMapping","lookupTable","populateSelectionSettings","renderTime","scalarMode","scalarVisibility","static","useLookupTableScalarRange","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),Ul.implementCoincidentTopologyMethods(e,t),Jl(e,t)}var nc={newInstance:jt.newInstance(tc,"vtkMapper"),extend:tc,...jl,...Kl,...hl};function rc(e){let t=0;return e.filter(((e,n)=>n===t&&(t+=e+1,!0)))}function oc(e){let t=0;for(let n=0;n<e.length;)n+=e[n]+1,t++;return t}const ac={extractCellSizes:rc,getNumberOfCells:oc};function ic(e,t){t.classHierarchy.push("vtkCellArray");const n={...e};e.getNumberOfCells=n=>void 0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=oc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=rc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function sc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function lc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Es.extend(e,t,sc(n)),ic(e,t)}var cc={newInstance:jt.newInstance(lc,"vtkCellArray"),extend:lc,...ac};const{vtkErrorMacro:uc}=jt,dc=[1,-1,1,-1,1,-1];function pc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return uc(`getBounds called on an array with components of\n        ${e.getNumberOfComponents()}`),dc;const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);return t.bounds[2]=r[0],t.bounds[3]=r[1],t.bounds[4]=0,t.bounds[5]=0,t.bounds},e.computeBounds=e.getBounds,e.setNumberOfComponents(t.numberOfComponents<2?3:t.numberOfComponents)}const fc={empty:!0,numberOfComponents:3,dataType:ys.FLOAT,bounds:[1,-1,1,-1,1,-1]};function gc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fc,n),Es.extend(e,t,n),pc(e,t)}var mc={newInstance:jt.newInstance(gc,"vtkPoints"),extend:gc};function hc(e,t){t.classHierarchy.push("vtkCell"),e.initialize=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n){t.pointsIds=n;let r=t.points.getData();r.length!==3*t.pointsIds.length&&(r=jt.newTypedArray(e.getDataType(),3*t.pointsIds.length));const o=e.getData();t.pointsIds.forEach(((e,t)=>{let n=3*e,a=3*t;r[a]=o[n],r[++a]=o[++n],r[++a]=o[++n]})),t.points.setData(r)}else{t.points=e,t.pointsIds=new 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let 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Ac=0,Ic=1,wc=2,Pc=3,Oc=4,Rc=5,Mc=6,Dc=7,Ec=9,Vc=21,Lc=41,Bc=42,Nc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],_c={getClassNameFromTypeId:function(e){return e<Nc.length?Nc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Nc.findIndex(e)},isLinear:function(e){return e<Vc||e===Lc||e===Bc},hasSubCells:function(e){return e===Mc||e===Oc||e===wc}};function Fc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Ac},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let 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e=P[2];P[2]=P[D],P[D]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function ou(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(nu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=Yc.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),tu(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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su={newInstance:jt.newInstance(iu,"vtkTriangle"),extend:iu,...ru};const lu=["verts","lines","polys","strips"],{vtkWarningMacro:cu}=jt,uu={[Pc]:Yc,[Oc]:Yc,[Rc]:su};function du(e,t){t.classHierarchy.push("vtkPolyData"),lu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=cc.newInstance()})),e.getNumberOfCells=()=>lu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),lu.forEach((n=>{t[n]=cc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?wc:Ic,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Oc:Pc,1===t&&cu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. 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o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||uu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const pu={};function fu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pu,n),eu.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),du(e,t)}var gu={newInstance:jt.newInstance(fu,"vtkPolyData"),extend:fu};function mu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const hu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function vu(e,t){let n=arguments.length>2&&void 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Tu={newInstance:jt.newInstance(vu,"vtkTexture"),extend:vu,...yu};const bu=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],xu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],Cu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Su=[0,1,0,1,0,1,0,1,2,2,2,2],Au=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Iu=new Float64Array(3),wu=new Float64Array(3),Pu=new Float64Array(3),Ou=new Float64Array(3),Ru=new Float64Array(3),Mu=new Float64Array(3),Du=new Float64Array(16);function Eu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function Vu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],Vn(Pu,Iu,r),Pu[0]+=.1,Vn(wu,Pu,o),Pn(Ru,wu,Iu),Pu[0]-=.1,Pu[1]+=.1,Vn(wu,Pu,o),Pn(Mu,wu,Iu);for(let e=0;e<3;e++)Ru[e]/=.05*u[0],Mu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],a[0]<-.5?On(Pu,Ru,a[0]*i-l.width):a[0]>.5?On(Pu,Ru,a[0]*i):On(Pu,Ru,a[0]*i-l.width/2),wn(Iu,Iu,Pu),On(Pu,Mu,a[1]*i-l.height/2),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Pu,Ru,l.width),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Pu,Mu,l.height),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Pu,Ru,l.width),Pn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Du,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Iu[0]=f[3*u],Iu[1]=f[3*u+1],Iu[2]=f[3*u+2],Vn(Pu,Iu,n),Iu[0]=f[3*u+3],Iu[1]=f[3*u+4],Iu[2]=f[3*u+5],Vn(Ou,Iu,n),Pn(Pu,Pu,Ou);const r=[Pu[0],Pu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const Lu=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Vu(e,t)}),"vtkCubeAxesActorHelper");function Bu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Iu[s]=t.dataBounds[a]-.1*r*bu[a][s],Iu[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Iu[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Pu,Iu,e),Iu[s]=t.dataBounds[a],Vn(Ou,Iu,e),Pn(Pu,Ou,Pu),Mn(Pu,Pu),i=Pu[2]>o,t.camera.getParallelProjection()||(Mn(Ou,Ou),i=Dn(Ou,Pu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Au[t][0]].length+2*r[Au[t][1]].length,a+=r[Au[t][0]].length+r[Au[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let 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e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[xu[e][t]]++;const a=[],i=[];for(let e=0;e<3;e++){const n=go().domain([t.dataBounds[2*e],t.dataBounds[2*e+1]]);a[e]=n.ticks(5);const r=n.tickFormat(5);i[e]=a[e].map(r)}e.updatePolyData(r,n,a),e.updateTextData(r,n,a,i),(o||t.forceUpdate)&&e.updateTextureAtlas(i)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Eu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Eu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Eu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Nu(e){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:12,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},...e}}function _u(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nu(n)),hs.extend(e,t,n),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Tu.newInstance(),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=nc.newInstance(),t.polyData=gu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=gu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Bu(e,t)}var Fu={newInstance:jt.newInstance(_u,"vtkCubeAxesActor"),extend:_u,newCubeAxesActorHelper:Lu};function ku(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Gu={};const Uu=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gu,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Fu.newCubeAxesActorHelper(),ku(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Uu);const zu={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var Wu={ObjectType:zu};const{ObjectType:Hu}=Wu;function ju(e,t){function n(e){switch(e){case Hu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Hu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Hu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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L=a.vertexOffset;f=function(e){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;)C(A[e],A,e+1),e+=A[e]+1,T++;return t.elementCount=w,e.upload(O,zu.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,zu.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):Xu("Wrong type for coordScale, expected vec3 or null"):Xu("Wrong type for coordShift, expected vec3 or null")}}const Zu={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),qu.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ed(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const nd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),td(e,t)}var od={newInstance:jt.newInstance(rd,"vtkShader"),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ad(`Links failed: ${t.error}`),0):(ad(t.error),0):(ad(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.fragmentShader.getError()),0):(ad(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ad(`Error linking 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No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . 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No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ad("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ad("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const sd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=od.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=od.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=od.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShaderProgram"),extend:ld,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function ud(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==zu.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const dd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function pd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),ud(e,t)}var fd={newInstance:jt.newInstance(pd,"vtkOpenGLVertexArrayObject"),extend:pd};const gd={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function md(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===gd.Points?t.context.POINTS:e===rs.WIREFRAME||n===gd.Lines||n===gd.TrisEdges||n===gd.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===gd.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===gd.Points?2:t.primitiveType===gd.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const hd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=cd.newInstance(),t.VAO=fd.newInstance(),t.CABO=Ju.newInstance(),md(e,t)}var yd={newInstance:jt.newInstance(vd),extend:vd,primTypes:gd};const Td={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},bd={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var xd={Wrap:Td,Filter:bd};const Cd=new Float32Array(1),Sd=new Int32Array(Cd.buffer);var Ad={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Cd[0]=e;const t=Sd[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Id,Filter:wd}=xd,{VtkDataTypes:Pd}=Es,{vtkDebugMacro:Od,vtkErrorMacro:Rd,vtkWarningMacro:Md}=Kt,{toHalf:Dd}=Ad;function Ed(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Pd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Pd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Dd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Ad.toHalf(Ad.fromHalf(e[u][l+s+t])*h*(1-v)+Ad.fromHalf(e[u][l+p+t])*h*v+Ad.fromHalf(e[u][c+s+t])*m*(1-v)+Ad.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Pd.UNSIGNED_SHORT&&e!==Pd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent()),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(wd.LINEAR),e.setMagnificationFilter(wd.LINEAR)):(e.setMinificationFilter(wd.NEAREST),e.setMagnificationFilter(wd.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Id.REPEAT),e.setWrapS(Id.REPEAT),e.setWrapT(Id.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Md("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Od(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Md("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Od("Unsupported internal texture type!"),Od(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Pd.SHORT:case t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Pd.FLOAT:case Pd.VOID:default:return t.context.FLOAT}switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Pd.FLOAT:case Pd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case wd.NEAREST:return t.context.NEAREST;case wd.LINEAR:return t.context.LINEAR;case wd.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case wd.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case wd.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case wd.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Id.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Id.REPEAT:return t.context.REPEAT;case Id.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Pd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Rd("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Pd.UNSIGNED_CHAR),e.getInternalFormat(Pd.UNSIGNED_CHAR,4),e.getFormat(Pd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Pd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Pd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Es.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Es.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Pd.FLOAT||t.useHalfFloat&&(u===Pd.SHORT||u===Pd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Pd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Pd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Pd.UNSIGNED_CHAR;if(u===Pd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Pd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Pd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Pd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Pd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Vd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Id.CLAMP_TO_EDGE,wrapT:Id.CLAMP_TO_EDGE,wrapR:Id.CLAMP_TO_EDGE,minificationFilter:wd.NEAREST,magnificationFilter:wd.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function Ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),Ed(e,t)}const Bd=Et(Ld,"vtkOpenGLTexture");var Nd={newInstance:Bd,extend:Ld,...xd};rn("vtkTexture",Bd);var _d="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Fd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",kd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=cd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},Gd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=cd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Ud}=ol,{primTypes:zd}=yd,{Representation:Wd,Shading:Hd}=ds,{ScalarMode:jd}=nc,{Filter:Kd,Wrap:$d}=Nd,{vtkErrorMacro:qd}=Kt,Xd={type:"StartEvent"},Yd={type:"EndEvent"},{CoordinateSystem:Zd}=Zi;function Qd(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=zd.Start;e<zd.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=cd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?t.renderable.getInterpolateScalarsBeforeMapping()&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=cd.substitute(i,"//VTK::Color::Impl",c).result):i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=cd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=cd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=cd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:qd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=cd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=cd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=cd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=cd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=cd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=cd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=cd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()||o.getEdgeVisibility()&&o.getRepresentation()===Wd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===zd.Points||o.getRepresentation()===Wd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===zd.Lines||o.getRepresentation()===Wd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==zd.Tris&&e!==zd.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==zd.TrisEdges&&e!==zd.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=cd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==zl.ID_LOW24&&t.lastSelectionState!==zl.ID_HIGH24||(a=cd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=cd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=cd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=cd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case zl.ID_LOW24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case zl.ID_HIGH24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=cd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Hd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||qd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||qd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||qd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===Zd.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():zl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===Wd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===zl.ID_LOW24||t.lastSelectionState===zl.ID_HIGH24);for(let i=zd.Start;i<zd.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===zd.TrisEdges||i===zd.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(Xd),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(Yd),!t.currentInput)return void qd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==jd.USE_CELL_DATA&&i!==jd.USE_CELL_FIELD_DATA&&i!==jd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===jd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Hd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();if(t.openGLActor.getActiveTextures()||(c=null),t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),t.internalColorTexture||(t.internalColorTexture=Nd.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Kd.NEAREST),e.setMagnificationFilter(Kd.NEAREST),e.setWrapS($d.CLAMP_TO_EDGE),e.setWrapT($d.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const u=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==u){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],d=r.getProperty().getEdgeVisibility()&&l===Wd.SURFACE;for(let e=zd.Start;e<zd.End;e++)e!==zd.TrisEdges&&e!==zd.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):d?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,Wd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Jd={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:zl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ep(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jd,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=zd,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=zd.Start;e<zd.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),Qd(e,t)}const tp=Et(ep,"vtkOpenGLPolyDataMapper");var np={newInstance:tp,extend:ep};rn("vtkMapper",tp);const{ColorMode:rp,ScalarMode:op,GetArray:ap}=hl;function ip(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString(rp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(op,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===op.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===op.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===op.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===op.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_FIELD_DATA){const t=e.getFieldData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const sp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function lp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),ip(e,t)}var cp={newInstance:jt.newInstance(lp,"vtkMapper2D"),extend:lp};const up=1,{primTypes:dp}=yd,{ScalarMode:pp}=cp,{vtkErrorMacro:fp}=Kt,gp={type:"StartEvent"},mp={type:"EndEvent"};function hp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=dp.Start;e<dp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(gp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(mp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==pp.USE_CELL_DATA&&a!==pp.USE_CELL_FIELD_DATA&&a!==pp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===pp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=mc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[dp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[dp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[dp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[dp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=dp.Start;a<dp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=cd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=cd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=cd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=cd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=cd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=cd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=cd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=cd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=cd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||fp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||fp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||fp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===up?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const vp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const yp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vp,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=dp,t.tmpMat4=g(new Float64Array(16));for(let e=dp.Start;e<dp.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),hp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",yp);const{VectorMode:Tp}=Pl;function bp(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function xp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Cp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Sp(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;bp(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,bp(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{bp(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Tp.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Es.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Ap=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=nc.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=gu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Tu.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Sp(e,t)}),"vtkScalarBarActorHelper");function Ip(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(xp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Cp())}}function wp(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Pp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wp(n)),t.autoLayout||(t.autoLayout=xp(0,t)),t.generateTicks||(t.generateTicks=Cp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Ip(e,t)}var Op={newInstance:jt.newInstance(Pp,"vtkScalarBarActor"),extend:Pp,newScalarBarActorHelper:Ap};function Rp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Mp={};const Dp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Mp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Op.newScalarBarActorHelper(),Rp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Dp);const{vtkErrorMacro:Ep}=Kt;function Vp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Es.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ep("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||Ep("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const Lp={context:null};const Bp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lp,n),Zt.extend(e,t,n),t.openGLTexture=Nd.newInstance(),t.tris=yd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Vp(e,t)}));rn("vtkSkybox",Bp);const{FieldAssociations:Np}=ol;function _p(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Fp={fieldAssociation:Np.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function kp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),_p(e,t)}var Gp={newInstance:jt.newInstance(kp,"vtkHardwareSelector"),extend:kp};function Up(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>{const e=[0,0];return null!==t.glFramebuffer&&(e[0]=t.glFramebuffer.width,e[1]=t.glFramebuffer.height),e},e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Nd.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(bd.LINEAR),r.setMagnificationFilter(bd.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const zp={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function Wp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zp,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Up(e,t)}var Hp={newInstance:Et(Wp,"vtkFramebuffer"),extend:Wp};function jp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Kp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function $p(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),jp(e,t)}var qp={newInstance:jt.newInstance($p,"vtkSelectionNode"),extend:$p,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:Xp}=Wl,{SelectionContent:Yp,SelectionField:Zp}=qp,{FieldAssociations:Qp}=ol,{vtkErrorMacro:Jp}=jt;function ef(e){return`${e.propID} ${e.compositeID}`}function tf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function nf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function rf(e,t){let n=t;return n<<=24,n|=e,n}function of(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=nf(n[0],n[1],e.pixBuffer[Xp.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=nf(n[0],n[1],e.pixBuffer[Xp.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[Xp.ID_LOW24]&&0===tf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area))return a;const s=nf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area),l=nf(n[0],n[1],e.pixBuffer[Xp.ID_HIGH24],e.area);return a.attributeID=rf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=of(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=of(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=of(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function af(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=qp.newInstance();switch(l.setContentType(Yp.INDICES),e){case Qp.FIELD_ASSOCIATION_CELLS:l.setFieldType(Zp.CELL);break;case Qp.FIELD_ASSOCIATION_POINTS:l.setFieldType(Zp.POINT);break;default:Jp("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function sf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=of(e,[n,t],0,u);if(r&&r.valid){const t=ef(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return af(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function lf(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e[0]!==n[0]||e[1]!==n[1]?(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer()):t.framebuffer.bind()}else t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return sf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return Jp("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=Xp.MIN_KNOWN_PASS;t.currentPass<=Xp.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===Xp.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===Xp.ID_HIGH24){if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===Xp.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=nf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===Xp.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===Xp.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(Xp,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=nf(e[0],e[1],t.pixBuffer[Xp.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=nf(e[0],e[1],t.pixBuffer[Xp.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[Xp.ID_LOW24]&&0===tf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area))return a;const s=nf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area),l=nf(e[0],e[1],t.pixBuffer[Xp.ID_HIGH24],t.area);return a.attributeID=rf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=ef(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return af(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const cf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cf,n),Gp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),lf(e,t)}var df={newInstance:jt.newInstance(uf,"vtkOpenGLHardwareSelector"),extend:uf,...Wl};const{vtkErrorMacro:pf}=Kt,{Representation:ff}=ds,{ObjectType:gf}=qu,{PassTypes:mf}=df,hf={type:"StartEvent"},vf={type:"EndEvent"};function yf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(hf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(vf),!t.currentInput)return void pf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=cd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=cd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=cd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=cd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=cd.substitute(o,"//VTK::Color::Impl",s).result,o=cd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=cd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=cd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=cd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=cd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===ff.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===mf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||pf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||pf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=qu.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=qu.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=qu.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=qu.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,gf.ARRAY_BUFFER),t.normalBuffer.upload(n,gf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),gf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,gf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Tf={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const bf=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tf,n),np.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),yf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",bf);const{vtkErrorMacro:xf}=jt;class Cf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Sf(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void xf("Invalid or missing input");const r=gu.newInstance();r.shallowCopy(n);const o=new Cf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(cc.newInstance({values:c,name:"faces"})),t[0]=r}}const Af={};function If(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),jt.obj(e,t),jt.algo(e,t,1,1),Sf(e,t)}var wf={newInstance:jt.newInstance(If,"vtkClosedPolyLineToSurfaceFilter"),extend:If};const{vtkErrorMacro:Pf}=Kt;function Of(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Pf("Invalid or missing input");if(!t.cutFunction)return void Pf("Missing cut function");const o=gu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const 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o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Rf={cutFunction:null,cutScalars:null,cutValue:0};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Of(e,t)}var Df={newInstance:Et(Mf,"vtkCutter"),extend:Mf};const Ef=e=>e,Vf=1e-6;class Lf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:Ef}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=Dn(n,r);return a>=1||(En(this.tmp,n,r),Sn(this.tmp)<Vf&&(En(this.tmp,[1,0,0],e),Sn(this.tmp)<Vf&&En(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Bf=function(){return new Lf(!0)},Nf=function(){return new Lf(!1)};const _f=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Ff=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function kf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=gu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Es.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Es.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Bf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Nf().translate(...t.center).apply(o),t.matrix){Nf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Nf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const Gf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=cc.newInstance({values:Uint16Array.from(Ff)}),t._lineCells=cc.newInstance({values:Uint16Array.from(_f)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),kf(e,t)}var zf={newInstance:jt.newInstance(Uf,"vtkCubeSource"),extend:Uf};const{vtkErrorMacro:Wf}=jt;function Hf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void Wf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):Wf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const jf={};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=zf.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkImageDataOutlineFilter"),extend:Kf};let qf;function Xf(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>qf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const Yf={preMultiplyFlag:!1,matrix:[...vo]};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),Xf(e,t)}qf=jt.newInstance(Zf,"vtkTransform");var Qf={newInstance:qf,extend:Zf};const Jf={NEAREST:0,LINEAR:1};var eg={InterpolationType:Jf};const{vtkErrorMacro:tg}=Kt;function ng(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function rg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function og(e,t){t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const n=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):tg("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Jf.NEAREST?(t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.openGLTexture.setMinificationFilter(bd.LINEAR),t.openGLTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime(),e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(!r)return;const o=r.getPointData()?.getScalars();if(!o)return;t._scalars!==o&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=o);const a=o.getNumberOfComponents();let i=`${r.getMTime()}A${o.getMTime()}`;const s=t._openGLRenderWindow.getGraphicsResourceForObject(o);if(s?.vtkObj&&s?.hash===i&&t.openGLTextureString===i)t.openGLTexture=s.vtkObj,t.openGLTextureString=s.hash;else{t.openGLTexture||(t.openGLTexture=Nd.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const e=r.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],o),t.openGLTextureString=i,o&&t._openGLRenderWindow.setGraphicsResourceForObject(o,t.openGLTexture,t.openGLTextureString)}const l=n.getProperty(),c=l.getIndependentComponents(),u=c?a:1,d=c?2*u:1,p=l.getRGBTransferFunction();i=ng(l,p,u);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===i&&t.colorTextureString===i)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(e*d*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),p){const r=new Float32Array(3*e);for(let t=0;t<u;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),c)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=i,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}const g=l.getPiecewiseFunction();i=ng(l,g,u);const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);if(m?.vtkObj&&m?.hash===i&&t.pwfTextureString===i)t.pwfTexture=m.vtkObj,t.pwfTextureString=m.hash;else{const e=1024,n=e*d,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),g){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<u;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=i,g&&t._openGLRenderWindow.setGraphicsResourceForObject(g,t.pwfTexture,t.pwfTextureString)}const h=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==h){const e=Es.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Es.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:tg("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=h,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",rg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",rg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=cd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:tg("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=cd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:tg("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=cd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=cd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=cd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`));const t=ue(n?.getDirection());ge(t,t);const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getDimensions(),l=[0,i[0]-1,0,i[1]-1,0,i[2]-1];let c=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[c+a]=r[a],e[c+o[0]]=l[2*o[0]+n],e[c+o[1]]=l[2*o[1]+t],c+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const u=new Uint16Array(8);u[0]=3,u[1]=0,u[2]=1,u[3]=3,u[4]=3,u[5]=0,u[6]=3,u[7]=2;const d=s.getNormal();za.normalize(d);const p=new Float32Array(12);for(let e=0;e<4;++e)p[3*e]=d[0],p[3*e+1]=d[1],p[3*e+2]=d[2];t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(u,1);const f=Es.newInstance({numberOfComponents:3,values:p,name:"Normals"});t.resliceGeom.getPointData().setNormals(f)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=gu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Es.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else tg("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)}}const ag={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,openGLTextureString:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1,_scalars:null};const ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ag,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=$f.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=gu.newInstance(),t.cutter=Df.newInstance(),t.lineToSurfaceFilter=wf.newInstance(),t.transform=Qf.newInstance(),bt(e,t,["openGLTexture"]),og(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",ig);var sg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:lg}=Kt,{SlicingMode:cg}=sg;function ug(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function dg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function pg(e,t){t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(n.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=cd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=cd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:lg("Unsupported number of independent coordinates.")}}if(i=cd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=cd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=cd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=cd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:lg("Unsupported number of independent coordinates.")}i=cd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=cd.substitute(i,"//VTK::TCoord::Impl",[...dg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); \n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (actualThickness == 0) {\n                    gl_FragData[0] = vec4(0.0, 0.0, 1.0, 1.0);\n                    return;\n                  }\n                  if (opacityToUse > 0.01) {\n                    for (int i = -actualThickness; i <= actualThickness; i++) {\n                      for (int j = -actualThickness; j <= actualThickness; j++) {\n                        if (i == 0 || j == 0) {\n                          continue;\n                        }\n                        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                          gl_FragCoord.y + float(j),\n                          gl_FragCoord.z, gl_FragCoord.w);\n                        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                        float value = texture2D(texture1, neighborPosIS.xy).r;\n                        if (value != centerValue) {\n                          pixelOnBorder = true;\n                          break;\n                        }\n                      }\n                      if (pixelOnBorder == true) {\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      gl_FragData[0] = vec4(tColor, outlineOpacity);\n                    }\n                    else {\n                      gl_FragData[0] = vec4(tColor, opacityToUse);\n                    }\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||lg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||lg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):lg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=ug(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.vtkObj&&m?.hash===g&&t.colorTextureString===g)t.colorTexture=m.vtkObj,t.colorTextureString=m.hash;else{const e=1024,n=new Uint8ClampedArray(e*p*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST)):(t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR)),f){const r=new Float32Array(3*e);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),u)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,p,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=g,f&&t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,t.colorTextureString)}const h=l.getPiecewiseFunction(),v=ug(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.vtkObj&&y?.hash===v&&t.pwfTextureString===v)t.pwfTexture=y.vtkObj,t.pwfTextureString=y.hash;else{const e=1024,n=e*p,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),h){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<d;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),u)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,p,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=v,h&&t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,t.pwfTextureString)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===cg.I&&(S=x-C[0]),T===cg.J&&(S=x-C[2]),T!==cg.K&&T!==cg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Nd.newInstance({resizable:!0}),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===Jf.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.NEAREST)):t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(bd.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(bd.LINEAR)),t.openGLTexture.setWrapS(Td.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Td.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[cg.X,cg.Y,cg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===cg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===cg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===cg.K||T===cg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):lg("Reformat slicing not yet supported.");const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);m?.vtkObj?(t.openGLTexture=m.vtkObj,t.VBOBuildString=m.hash):(t._scalars!==g&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=g),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t._openGLRenderWindow.setGraphicsResourceForObject(g,t.openGLTexture,t.VBOBuildString)),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const h=Es.newInstance({numberOfComponents:3,values:r});h.setName("points");const v=Es.newInstance({numberOfComponents:2,values:l});v.setName("tcoords");const y=new Uint16Array(8);y[0]=3,y[1]=0,y[2]=1,y[3]=3,y[4]=3,y[5]=0,y[6]=3,y[7]=2;const C=Es.newInstance({numberOfComponents:1,values:y});t.tris.getCABO().createVBO(C,"polys",rs.SURFACE,{points:h,tcoords:v,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=e=>{t.labelOutlineThicknessTexture||(t.labelOutlineThicknessTexture=Nd.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]}}const fg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,_scalars:null};const gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fg,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),pg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",gg);const{vtkErrorMacro:mg}=jt;function hg(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function vg(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===Jf.NEAREST?(t.volumeTexture.setMinificationFilter(bd.NEAREST),t.volumeTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.volumeTexture.setMinificationFilter(bd.LINEAR),t.volumeTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=hg(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=hg(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Es.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Es.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Es.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Es.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getCenterlineTangentDirections(),r=new Float32Array(3*i),o=new Float32Array(3*i);for(let t=0,a=0;t<n;++t){const n=3*t;for(let t=0;t<4;++t)r[a+0]=e[n+0],r[a+1]=e[n+1],r[a+2]=e[n+2],o[a+0]=e[n+3],o[a+1]=e[n+4],o[a+2]=e[n+5],a+=3}const a=Es.newInstance({numberOfComponents:3,values:r,name:"centerlineTopDirection"}),s=Es.newInstance({numberOfComponents:3,values:o,name:"centerlineBotDirection"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const s=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],l=t.renderable.getUseUniformOrientation();l?s.push("out vec3 centerlineDirVSOutput;","uniform vec3 centerlineDirection;"):s.push("out vec3 centerlineTopDirVSOutput;","out vec3 centerlineBotDirVSOutput;","out float centerlineAngleVSOutput;","attribute vec3 centerlineTopDirection;","attribute vec3 centerlineBotDirection;"),a=cd.substitute(a,"//VTK::Color::Dec",s).result;const c=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];l?c.push("centerlineDirVSOutput = centerlineDirection;"):c.push("vec3 sumVec = centerlineTopDirection + centerlineBotDirection;","float sumLen2 = dot(sumVec, sumVec);","float diffLen2 = 4.0 - sumLen2;","if (diffLen2 < 0.001) {","  // vectors are too close to each other, use lerp","  centerlineAngleVSOutput = -1.0; // use negative angle as a flag for lerp","  centerlineTopDirVSOutput = centerlineTopDirection;","  centerlineBotDirVSOutput = centerlineBotDirection;","} else if (sumLen2 == 0.0) {","  // vector are opposite to each other, don't make a choice for the user","  // use slerp without direction, it will display the centerline color on each row of pixel","  centerlineAngleVSOutput = 0.0;","  centerlineTopDirVSOutput = vec3(0.0);","  centerlineBotDirVSOutput = vec3(0.0);","} else {","  // use slerp","  centerlineAngleVSOutput = 2.0 * atan(sqrt(diffLen2/sumLen2));","  float sinAngle = sin(centerlineAngleVSOutput);","  centerlineTopDirVSOutput = centerlineTopDirection / sinAngle;","  centerlineBotDirVSOutput = centerlineBotDirection / sinAngle;","}"),a=cd.substitute(a,"//VTK::Color::Impl",c).result;const u=t.volumeTexture.getComponents(),d=o.getProperty().getIndependentComponents();let p=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];l?p.push("in vec3 centerlineDirVSOutput;"):p.push("in vec3 centerlineTopDirVSOutput;","in vec3 centerlineBotDirVSOutput;","in float centerlineAngleVSOutput;");const f=t.renderable.getCenterPoint();if(f&&p.push("uniform vec3 globalCenterPoint;"),d){for(let e=1;e<u;e++)p=p.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(u){case 1:p=p.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:p=p.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:mg("Unsupported number of independent coordinates.")}}i=cd.substitute(i,"//VTK::TCoord::Dec",p).result;let g=[];if(l?g.push("vec3 interpolatedCenterlineDir = centerlineDirVSOutput;"):g.push("vec3 interpolatedCenterlineDir;","if (centerlineAngleVSOutput < 0.0) {","  // Lerp","  interpolatedCenterlineDir = quadOffsetVSOutput.y * centerlineTopDirVSOutput + (1.0 - quadOffsetVSOutput.y) * centerlineBotDirVSOutput;","} else {","  // Slerp","  float topInterpolationAngle = quadOffsetVSOutput.y * centerlineAngleVSOutput;","  float botInterpolationAngle = centerlineAngleVSOutput - topInterpolationAngle;","  interpolatedCenterlineDir = sin(topInterpolationAngle) * centerlineTopDirVSOutput + sin(botInterpolationAngle) * centerlineBotDirVSOutput;","}","// Slerp should give a normalized vector but when sin(angle) is small, rounding error occurs","// Normalize for both lerp and slerp","interpolatedCenterlineDir = normalize(interpolatedCenterlineDir);"),f?g.push("float baseOffset = dot(interpolatedCenterlineDir, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):g.push("float horizontalOffset = quadOffsetVSOutput.x;"),g.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * interpolatedCenterlineDir;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(volumeTexture, volumePosTC);"),d){const e=["r","g","b","a"];for(let t=0;t<u;++t)g=g.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(u){case 1:g=g.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:g=g.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:mg("Unsupported number of independent coordinates.")}}else switch(u){case 1:g=g.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:g=g.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",g).result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||mg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&n.getProgram().isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),e.name,e.offset,n.getCABO().getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&mg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const a=t.volumeTexture.getTextureUnit();if(n.getProgram().setUniformi("volumeTexture",a),n.getProgram().setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4f("backgroundColor",...t.renderable.getBackgroundColor()),n.getProgram().isUniformUsed("centerlineDirection")){const e=t.renderable.getUniformDirection();n.getProgram().setUniform3fArray("centerlineDirection",e)}if(n.getProgram().isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();n.getProgram().setUniform3fArray("globalCenterPoint",e)}const i=t.currentImageDataInput,s=i.getWorldToIndex(),l=P(new Float32Array(16),Rn([],i.getDimensions())),c=oe(l,l,s);if(n.getProgram().setUniformMatrix("MCTCMatrix",c),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const yg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Tg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),kd(e,t,n),jt.algo(e,t,2,0),t.tris=yd.newInstance(),t.volumeTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.pwfTexture=Nd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),vg(e,t)}),"vtkOpenGLImageCPRMapper");function bg(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Tg);const xg={context:null,keyMatrixTime:null,keyMatrices:null};const Cg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),bg(e,t)}),"vtkOpenGLImageSlice");function Sg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Cg);const Ag={};const Ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ag,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Sg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Ig);const wg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Pg={FRACTIONAL:0,PROPORTIONAL:1};var Og={InterpolationType:wg,OpacityMode:Pg};const Rg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5};var Mg={BlendMode:Rg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Dg,vtkErrorMacro:Eg}=Kt;function Vg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function Lg(e,t){t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(n,r)=>{if(n){t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const n=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera()),e.renderPiece(r,n)}},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define vtkIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n#ifdef vtkImageLabelOutlineOn\n\nuniform float outlineOpacity;\nuniform float vpWidth;\nuniform float vpHeight;\nuniform float vpOffsetX;\nuniform float vpOffsetY;\nuniform mat4 PCWCMatrix;\nuniform mat4 vWCtoIDX;\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents >= 3\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#endif\n#if vtkNumComponents >= 4\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef vtkIndependentComponentsOn\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.a = tmp.g;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n#if defined(vtkGradientOpacityOn)\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n#else\n  return 1.0;\n#endif\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n#ifdef vtkClippingPlanesOn\n  void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n  {\n    vec3 g1VC[3];\n    for (int i = 0; i < 3; ++i)\n    {\n      g1VC[i] = IStoVC(texPos[i]);\n    }\n    vec3 posVC = IStoVC(pos);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      for (int j = 0; j < 3; ++j)\n      {\n        if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n        {\n          g1[j] = 0.0;\n        }\n      }\n    }\n  }\n#endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    #ifdef vtkGradientOpacityOn\n      vec4 computeDensityNormal(float gradientMag, vec3 scalarInterp[2])\n      {\n    #else\n      //if gradient opacity not on but using density gradient\n      vec4 computeDensityNormal(vec3 scalarInterp[2])\n      {\n    #endif\n        vec3 opacityG1, opacityG2;\n        opacityG1.x = texture2D(otexture, vec2(scalarInterp[0].x * oscale0 + oshift0, 0.5)).r;\n        opacityG1.y = texture2D(otexture, vec2(scalarInterp[0].y * oscale0 + oshift0, 0.5)).r;\n        opacityG1.z = texture2D(otexture, vec2(scalarInterp[0].z * oscale0 + oshift0, 0.5)).r;\n        opacityG2.x = texture2D(otexture, vec2(scalarInterp[1].x * oscale0 + oshift0, 0.5)).r;\n        opacityG2.y = texture2D(otexture, vec2(scalarInterp[1].y * oscale0 + oshift0, 0.5)).r;\n        opacityG2.z = texture2D(otexture, vec2(scalarInterp[1].z * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n        float gradOpacityFactor = 1.0f;\n        if (gradientMag >= 0.0){\n          gradOpacityFactor = computeGradientOpacityFactor(gradientMag, goscale0, goshift0, gomin0, gomax0);\n        }\n        opacityG1.xyz *= gradOpacityFactor;\n        opacityG2.xyz *= gradOpacityFactor;\n    #endif\n\n        vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n        // divide by spacing\n        opacityG.xyz /= vSpacing;\n        opacityG.w = length(opacityG.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(opacityG.xyz);\n        if (length(opacityG.xyz) > 0.0) {\n          return vec4(normalize(opacityG.xyz),opacityG.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n\n      vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2])\n      {\n        vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n        vec3 yvec = vec3(0.0, tstep.y, 0.0);\n        vec3 zvec = vec3(0.0, 0.0, tstep.z);\n        vec3 texPosPVec[3];\n        texPosPVec[0] = pos + xvec;\n        texPosPVec[1] = pos + yvec;\n        texPosPVec[2] = pos + zvec;\n        vec3 texPosNVec[3];\n        texPosNVec[0] = pos - xvec;\n        texPosNVec[1] = pos - yvec;\n        texPosNVec[2] = pos - zvec;\n        vec3 g1, g2;\n\n        scalarInterp[0].x = getTextureValue(texPosPVec[0]).a;\n        scalarInterp[0].y = getTextureValue(texPosPVec[1]).a;\n        scalarInterp[0].z = getTextureValue(texPosPVec[2]).a;\n        scalarInterp[1].x = getTextureValue(texPosNVec[0]).a;\n        scalarInterp[1].y = getTextureValue(texPosNVec[1]).a;\n        scalarInterp[1].z = getTextureValue(texPosNVec[2]).a;\n\n        #ifdef vtkClippingPlanesOn\n          adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n          adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n        #endif\n        vec4 result;\n        result.x = scalarInterp[0].x - scalarInterp[1].x;\n        result.y = scalarInterp[0].y - scalarInterp[1].y;\n        result.z = scalarInterp[0].z - scalarInterp[1].z;\n        // divide by spacing\n        result.xyz /= vSpacing;\n        result.w = length(result.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(result.xyz);\n        if (length(result.xyz) > 0.0) {\n          return vec4(normalize(result.xyz),result.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n  #endif\n\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n#ifdef vtkImageLabelOutlineOn\nvec3 fragCoordToIndexSpace(vec4 fragCoord) {\n  vec4 pcPos = vec4(\n    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n    (fragCoord.z - 0.5) * 2.0,\n    1.0);\n\n  vec4 worldCoord = PCWCMatrix * pcPos;\n  vec4 vertex = (worldCoord/worldCoord.w);\n\n  vec3 index = (vWCtoIDX * vertex).xyz;\n\n  // half voxel fix for labelmapOutline\n  return (index + vec3(0.5)) / vec3(volumeDimensions);\n}\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  // support gradient opacity\n  #ifdef vtkGradientOpacityOn\n    vec4 normal;\n  #endif\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n      normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n//=======================================================================\n// Given a texture value compute the color and opacity\n//\nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n#ifdef vtkImageLabelOutlineOn\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n  if (actualThickness == 0) {\n    return vec4(0, 0, 1, 1);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  if (float(tColor.a) > 0.01) {\n    for (int i = -actualThickness; i <= actualThickness; i++) {\n      for (int j = -actualThickness; j <= actualThickness; j++) {\n        if (i == 0 || j == 0) {\n          continue;\n        }\n\n        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n          gl_FragCoord.y + float(j),\n          gl_FragCoord.z, gl_FragCoord.w);\n\n        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n        vec4 value = getTextureValue(neighborPosIS);\n\n        // If any of my neighbours are not the same value as I\n        // am, this means I am on the border of the segment.\n        // We can break the loops\n        if (any(notEqual(value, centerValue))) {\n          pixelOnBorder = true;\n          break;\n        }\n      }\n\n      if (pixelOnBorder == true) {\n        break;\n      }\n    }\n\n    // If I am on the border, I am displayed at full opacity\n    if (pixelOnBorder == true) {\n      tColor.a = outlineOpacity;\n    }\n  }\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n  //\n  vec4 goFactor = vec4(1.0,1.0,1.0,1.0);\n\n  // compute the normal vectors as needed\n  #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #if !defined(vtkComponent0Proportional)\n        vec4 normal0 = normalMat[0];\n      #endif\n      #if !defined(vtkComponent1Proportional)\n        vec4 normal1 = normalMat[1];\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          vec4 normal2 = normalMat[2];\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            vec4 normal3 = normalMat[3];\n          #endif\n        #endif\n      #endif\n    #else\n      vec4 normalLight;\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp);\n        if (length(normal0)>0.0){\n          #ifdef vtkGradientOpacityOn\n            normalLight = computeDensityNormal(normal0.w, scalarInterp);\n          #else\n            normalLight = computeDensityNormal(scalarInterp);\n          #endif\n          if (length(normalLight) == 0.0){\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n        normalLight = normal0;\n      #endif\n    #endif\n  #endif\n\n  // compute gradient opacity factors as needed\n  #if defined(vtkGradientOpacityOn)\n    #if !defined(vtkComponent0Proportional)\n      goFactor.x =\n        computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      #if !defined(vtkComponent1Proportional)\n        goFactor.y =\n          computeGradientOpacityFactor(normal1.a, goscale1, goshift1, gomin1, gomax1);\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          goFactor.z =\n            computeGradientOpacityFactor(normal2.a, goscale2, goshift2, gomin2, gomax2);\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            goFactor.w =\n              computeGradientOpacityFactor(normal3.a, goscale3, goshift3, gomin3, gomax3);\n          #endif\n        #endif\n      #endif\n    #endif\n  #endif\n\n  // single component is always independent\n  #if vtkNumComponents == 1\n    vec4 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5));\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n    if (tColor.a < EPSILON){\n      return vec4(0.0);\n    }\n  #endif\n\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n    vec4 tColor = mix0*texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0));\n    #if !defined(vtkComponent0Proportional)\n      tColor.a = goFactor.x*mix0*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n      tColor *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix0));\n    #endif\n\n    vec3 tColor1 = mix1*texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n    #if !defined(vtkComponent1Proportional)\n      tColor.a += goFactor.y*mix1*texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n      tColor1 *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix1));\n    #endif\n\n    #if vtkNumComponents >= 3\n      vec3 tColor2 = mix2*texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n      #if !defined(vtkComponent2Proportional)\n        tColor.a += goFactor.z*mix2*texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n      #else\n        float pwfValue = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n        tColor2 *= pwfValue;\n        tColor.a *= mix(pwfValue, 1.0, (1.0 - mix2));\n      #endif\n\n      #if vtkNumComponents >= 4\n        vec3 tColor3 = mix3*texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n        #if !defined(vtkComponent3Proportional)\n          tColor.a += goFactor.w*mix3*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #else\n          float pwfValue = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n          tColor3 *= pwfValue;\n          tColor.a *= mix(pwfValue, 1.0, (1.0 - mix3));\n        #endif\n      #endif\n    #endif\n  #else // then not independent\n\n  #if vtkNumComponents == 2\n    float lum = tValue.r * cscale0 + cshift0;\n    float alpha = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    vec4 tColor = vec4(lum, lum, lum, alpha);\n  #endif\n  #if vtkNumComponents == 3\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n  #endif\n  #if vtkNumComponents == 4\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n  #endif\n  #endif // dependent\n\n  // apply lighting if requested as appropriate\n  #if vtkLightComplexity > 0\n    #if !defined(vtkComponent0Proportional)\n      #if vtkNumComponents == 1\n        #ifdef SurfaceShadowOn\n            #if vtkLightComplexity < 3\n                vec3 tColorS = applyLightingDirectional(posIS, tColor, normalLight);\n            #else\n                vec3 tColorS = applyLightingPositional(posIS, tColor, normalLight, IStoVC(posIS));\n            #endif\n        #endif\n\n        #ifdef VolumeShadowOn\n          vec3 tColorVS = applyShadowRay(tColor.rgb, posIS, rayDirVC);\n          #ifdef SurfaceShadowOn\n            float vol_coef = volumetricScatteringBlending * (1.0 - tColor.a / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n            tColor.rgb = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n          #else\n            tColor.rgb = tColorVS;\n          #endif\n        #else\n            tColor.rgb = tColorS;\n        #endif\n\n      #else\n        applyLighting(tColor.rgb, normal0);\n      #endif\n    #endif\n\n    #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n      #if !defined(vtkComponent1Proportional)\n        applyLighting(tColor1, normal1);\n      #endif\n    #if vtkNumComponents >= 3\n      #if !defined(vtkComponent2Proportional)\n        applyLighting(tColor2, normal2);\n      #endif\n    #if vtkNumComponents >= 4\n      #if !defined(vtkComponent3Proportional)\n        applyLighting(tColor3, normal3);\n      #endif\n    #endif\n    #endif\n    #endif\n  #endif\n\n// perform final independent blend as needed\n#if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n  tColor.rgb += tColor1;\n#if vtkNumComponents >= 3\n  tColor.rgb += tColor2;\n#if vtkNumComponents >= 4\n  tColor.rgb += tColor3;\n#endif\n#endif\n#endif\n\n#endif\nreturn tColor;\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents == 2\n     if (val.r >= min.r && val.r <= max.r &&\n        val.g >= min.g && val.g <= max.g) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 3\n    if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n        all(lessThanEqual(val, ipScalarRangeMax))) {\n      withinRange = true;\n    }\n  #endif\n  return withinRange;\n}\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Fragment;o.getProperty().getInterpolationType()===wg.LINEAR&&(a=cd.substitute(a,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===o.getProperty().getUseLabelOutline()&&(a=cd.substitute(a,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result);const i=t.scalarTexture.getComponents();a=cd.substitute(a,"//VTK::NumComponents",`#define vtkNumComponents ${i}`).result;const s=o.getProperty().getIndependentComponents();if(s){a=cd.substitute(a,"//VTK::IndependentComponentsOn","#define vtkIndependentComponentsOn").result;const e=[];for(let t=0;t<i;t++)o.getProperty().getOpacityMode(t)===Pg.PROPORTIONAL&&e.push(`#define vtkComponent${t}Proportional`);e.length>0&&(a=cd.substitute(a,"//VTK::vtkProportionalComponents",e.join("\n")).result)}const l=t.currentInput.getSpatialExtent(),c=t.currentInput.getSpacing(),u=new Float64Array(3);In(u,(l[1]-l[0])*c[0],(l[3]-l[2])*c[1],(l[5]-l[4])*c[2]);const d=Sn(u)/e.getCurrentSampleDistance(r);a=cd.substitute(a,"//VTK::MaximumSamplesValue",`${Math.ceil(d)}`).result,a=cd.substitute(a,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lastLightComplexity}`).result,t.lastLightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(a=cd.substitute(a,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(a=cd.substitute(a,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0&&(a=cd.substitute(a,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result)),t.gopacity=o.getProperty().getUseGradientOpacity(0);for(let e=1;s&&!t.gopacity&&e<i;++e)o.getProperty().getUseGradientOpacity(e)&&(t.gopacity=!0);t.gopacity&&(a=cd.substitute(a,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(a=cd.substitute(a,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(a=cd.substitute(a,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,a=cd.substitute(a,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),a=cd.substitute(a,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=a,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lastLightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=cd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lastLightComplexity&&(o=cd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=cd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=cd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=cd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=cd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{let a=0;o.getProperty().getShade()&&t.renderable.getBlendMode()===Rg.COMPOSITE_BLEND&&(a=0,t.numberOfLights=0,r.getLights().forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===a&&(a=1)),1===a&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(a=2),a<3&&e.getPositional()&&(a=3)})));let i=!1;t.lastLightComplexity!==a&&(t.lastLightComplexity=a,i=!0);const s=t.scalarTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=!1;const u=[];if(l){for(let e=0;e<s;e++)u.push(o.getProperty().getOpacityMode(e));u.length>0&&(c=!0)}const d=t.currentInput.getSpatialExtent(),p=t.currentInput.getSpacing(),f=new Float64Array(3);In(f,(d[1]-d[0])*p[0],(d[3]-d[2])*p[1],(d[5]-d[4])*p[2]);const g=Sn(f)/e.getCurrentSampleDistance(r),m={interpolationType:o.getProperty().getInterpolationType(),useLabelOutline:o.getProperty().getUseLabelOutline(),numComp:s,usesProportionalComponents:c,iComps:l,maxSamples:g,useGradientOpacity:o.getProperty().getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),proportionalComponents:u};return t.previousState&&t.previousState.interpolationType===m.interpolationType&&t.previousState.useLabelOutline===m.useLabelOutline&&t.previousState.numComp===m.numComp&&t.previousState.usesProportionalComponents===m.usesProportionalComponents&&t.previousState.iComps===m.iComps&&t.previousState.maxSamples===m.maxSamples&&t.previousState.useGradientOpacity===m.useGradientOpacity&&t.previousState.blendMode===m.blendMode&&function(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;++n)if(e[n]!==t[n])return!1;return!0}(t.previousState.proportionalComponents,m.proportionalComponents)?!!(0===n.getProgram()?.getHandle()||i||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||!!t.lastZBufferTexture!=!!t.zBufferTexture||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime())&&(t.lastZBufferTexture=t.zBufferTexture,!0):(t.previousState={...m},!0)},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Dg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*Dn(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(o.getProperty().getUseLabelOutline()){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lastLightComplexity)return;let O=0;const R=[],M=[],D=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],D[0+3*O]=-.5*w[0],D[1+3*O]=-.5*w[1],D[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",D),3===t.lastLightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();o.setUniformi("ctexture",t.colorTexture.getTextureUnit()),o.setUniformi("otexture",t.opacityTexture.getTextureUnit()),o.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),o.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const a=t.scalarTexture.getVolumeInfo(),i=r.getProperty(),s=t.scalarTexture.getComponents(),l=r.getProperty().getIndependentComponents();if(l&&s>=2)for(let e=0;e<s;e++)o.setUniformf(`mix${e}`,r.getProperty().getComponentWeight(e));for(let e=0;e<s;e++){const t=l?e:0,n=a.scale[e],r=i.getScalarOpacity(t).getRange(),s=n/(r[1]-r[0]),c=(a.offset[e]-r[0])/(r[1]-r[0]);o.setUniformf(`oshift${e}`,c),o.setUniformf(`oscale${e}`,s);const u=i.getRGBTransferFunction(t).getRange(),d=(a.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);o.setUniformf(`cshift${e}`,d),o.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(l)for(let e=0;e<s;++e){const t=a.scale[e];if(i.getUseGradientOpacity(e)){const n=i.getGradientOpacityMinimumOpacity(e),r=i.getGradientOpacityMaximumOpacity(e);o.setUniformf(`gomin${e}`,n),o.setUniformf(`gomax${e}`,r);const a=[i.getGradientOpacityMinimumValue(e),i.getGradientOpacityMaximumValue(e)];o.setUniformf(`goscale${e}`,t*(r-n)/(a[1]-a[0])),o.setUniformf(`goshift${e}`,-a[0]*(r-n)/(a[1]-a[0])+n)}else o.setUniformf(`gomin${e}`,1),o.setUniformf(`gomax${e}`,1),o.setUniformf(`goscale${e}`,0),o.setUniformf(`goshift${e}`,1)}else{const e=a.scale[s-1],t=i.getGradientOpacityMinimumOpacity(0),n=i.getGradientOpacityMaximumOpacity(0);o.setUniformf("gomin0",t),o.setUniformf("gomax0",n);const r=[i.getGradientOpacityMinimumValue(0),i.getGradientOpacityMaximumValue(0)];o.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),o.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===r.getProperty().getUseLabelOutline()){const e=r.getProperty().getLabelOutlineOpacity();o.setUniformf("outlineOpacity",e)}t.lastLightComplexity>0&&(o.setUniformf("vAmbient",i.getAmbient()),o.setUniformf("vDiffuse",i.getDiffuse()),o.setUniformf("vSpecular",i.getSpecular()),o.setUniformf("vSpecularPower",i.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*Dn(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===wg.NEAREST?(t.scalarTexture.setMinificationFilter(bd.NEAREST),t.scalarTexture.setMagnificationFilter(bd.NEAREST)):(t.scalarTexture.setMinificationFilter(bd.LINEAR),t.scalarTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null,null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=fd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Eg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;t._scalars!==a&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=a);const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(bd.LINEAR),t.jitterTexture.setMagnificationFilter(bd.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=i.getIndependentComponents()?s:1,c=i.getScalarOpacity(),u=t._openGLRenderWindow.getGraphicsResourceForObject(c);let d=Vg(i,c,l);if(u.vtkObj&&u.hash===d&&t.opacityTextureString===d)t.opacityTexture=u.vtkObj,t.opacityTextureString=u.hash;else{const r=1024,o=2*r*l,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<l;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.releaseGraphicsResources(t._openGLRenderWindow),t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(bd.LINEAR),t.opacityTexture.setMagnificationFilter(bd.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*l,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*l,1,ys.UNSIGNED_CHAR,e)}t.opacityTextureString=d,c&&t._openGLRenderWindow.setGraphicsResourceForObject(c,t.opacityTexture,t.opacityTextureString)}const p=i.getRGBTransferFunction();d=Vg(i,p,l);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===d&&t.colorTextureString===d)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(2*e*l*3),r=new Float32Array(3*e);for(let t=0;t<l;++t){const o=i.getRGBTransferFunction(t),a=o.getRange();o.getTable(a[0],a[1],e,r,1);for(let o=0;o<3*e;++o)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.create2DFromRaw(e,2*l,3,ys.UNSIGNED_CHAR,n),t.colorTextureString=d,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}e.updateLabelOutlineThicknessTexture(r);const g=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(d=`${o.getMTime()}A${a.getMTime()}`,g?.vtkObj&&g?.hash===d&&t.scalarTextureString===d)t.scalarTexture=g.vtkObj,t.scalarTextureString=g.hash;else{const e=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.releaseGraphicsResources(t._openGLRenderWindow),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],a,t.renderable.getPreferSizeOverAccuracy()),t.scalarTextureString=d,a&&t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,t.scalarTextureString)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Es.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Es.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=e=>{const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}}}const Bg={context:null,VBOBuildTime:null,scalarTexture:null,scalarTextureString:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lastLightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0,_scalars:null};const Ng=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bg,n),Zt.extend(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=yd.newInstance(),t.scalarTexture=Nd.newInstance(),t.opacityTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.jitterTexture=Nd.newInstance(),t.jitterTexture.setWrapS(Td.REPEAT),t.jitterTexture.setWrapT(Td.REPEAT),t.labelOutlineThicknessTexture=Nd.newInstance(),t.framebuffer=Hp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Lg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",Ng);const{vtkDebugMacro:_g}=Kt;function Fg(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent();const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():_g("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const kg={};const Gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kg,n),Zt.extend(e,t,n),Fg(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Gg);var Ug="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:zg}=ds;function Wg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Nd.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Hg(e,t){return Wg(e,t,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE)}function jg(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Hg(t._openGLRenderWindow,t.size),t.seedTexture0=Hg(t._openGLRenderWindow,t.size),t.licTexture1=Hg(t._openGLRenderWindow,t.size),t.seedTexture1=Hg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Wg(t._openGLRenderWindow,t.size,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Wg(e,t,Nd.Filter.LINEAR,Nd.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const Kg={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function $g(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kg,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),jg(e,t)}var qg={newInstance:jt.newInstance($g,"vtkLICPingPongBufferManager"),extend:$g};const Xg=0,Yg=1,Zg=2,Qg=3,Jg=1;function em(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Hp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;f&&o[0]===f.getSize()[0]&&o[1]===f.getSize()[1]||(f=Hp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=qg.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const g=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],m=1/o[0],h=1/o[1],v=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;v.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:y}=t;v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",0),y.setUniformf("uMaskThreshold",t.maskThreshold),y.setUniform2f("uNoiseBoundsPt1",...g),y.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),y.setUniformi("texLIC",t.bufs.getLICTextureUnit()),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,y);const{LICIShaderProgram:T}=t;v.readyShaderProgram(T),T.setUniformi("uPassNo",0),T.setUniformf("uStepSize",-d),T.setUniform2f("uNoiseBoundsPt1",...g),T.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:b}=t;if(v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,b),t.enhancedLIC){t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",m),a.setUniformf("uDy",h),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",1),n(y,t.bufs),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformi("uPassNo",1),T.setUniformf("uStepSize",-d),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),b.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,b)}if(t.antiAlias){const e=t.AAHProgram;v.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",m);const a=t.AAVProgram;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",h);for(let i=0;i<t.antiAlias;++i)v.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),v.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const tm={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLineIntegralConvolution2D"),extend:nm};function om(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const am={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:Xg,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:Jg,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function im(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,am,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),om(0,t)}var sm={newInstance:jt.newInstance(im,"vtkSurfaceLICInterface"),extend:im};const{Representation:lm}=ds;function cm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===Jg?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Nd.newInstance({wrapS:Nd.Wrap.REPEAT,wrapT:Nd.Wrap.REPEAT,minificationFilter:Nd.Filter.NEAREST,magnificationFilter:Nd.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.allocateTextures=()=>{const n=Nd.Filter.NEAREST,r=Nd.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Nd.newInstance({wrapS:Nd.Wrap.CLAMP_TO_EDGE,wrapT:Nd.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Nd.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Hp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",lm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=rm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===Zg||u===Qg){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const um={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),sm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkSurfaceLICInterface"),extend:dm};const{vtkErrorMacro:fm}=Kt;function gm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=cd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=cd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=cd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=cd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=cd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(fm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(fm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(fm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=sm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=pm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(fm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const mm={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const hm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mm,n),np.extend(e,t,n),gm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",hm);const{vtkErrorMacro:vm}=Kt;function ym(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||vm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,zu.ARRAY_BUFFER),o&&a.getColorBO().upload(f,zu.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Tm={};const bm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),np.extend(e,t,n),ym(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",bm);const{vtkErrorMacro:xm}=Kt;function Cm(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=cd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||xm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||xm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||xm("Error setting 'radiusMC' in shader 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o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():xm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let 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e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const Vh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Lh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Eh(e,t)}var Bh={newInstance:jt.newInstance(Lh,"vtkCamera"),extend:Lh};function Nh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const _h={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function Fh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_h,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),Nh(e,t)}var kh={newInstance:jt.newInstance(Fh,"vtkLight"),extend:Fh,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Gh}=jt;function Uh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Gh("call displayToView on your view instead"),e.viewToDisplay=()=>Gh("callviewtodisplay on your view instead"),e.getSize=()=>Gh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Gh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkViewport"),extend:Wh};const{vtkDebugMacro:jh,vtkErrorMacro:Kh,vtkWarningMacro:$h}=Kt;function qh(e){return()=>Kh(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function Xh(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(jh("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):Kh("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=qh("allocateTime"),e.updateGeometry=qh("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{jh(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Bh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=kh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return Kh("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return Kh("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),jh("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return jh("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return Kh("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&($h("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return jh("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return Kh("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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Yh={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function Zh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,Yh,n),Hh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),Xh(e,t)}var Qh={newInstance:Et(Zh,"vtkRenderer"),extend:Zh};const Jh=Object.create(null);function ev(e,t){Jh[e]=t}function tv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Jh[e]&&Jh[e](t)}function nv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return tv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))}}const rv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1};function ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),nv(e,t)}var av={newInstance:jt.newInstance(ov,"vtkRenderWindow"),extend:ov,registerViewConstructor:ev,listViewAPIs:function(){return Object.keys(Jh)},newAPISpecificView:tv};const iv={Unknown:0,LeftController:1,RightController:2},sv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var lv={Device:iv,Input:sv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:cv,Input:uv}=lv,{vtkWarningMacro:dv,vtkErrorMacro:pv,normalizeWheel:fv,vtkOnceErrorMacro:gv}=jt,mv={ctrlKey:!1,altKey:!1,shiftKey:!1},hv={"xr-standard":[uv.Trigger,uv.Grip,uv.TrackPad,uv.Thumbstick,uv.A,uv.B]},vv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function yv(e){e.cancelable&&e.preventDefault()}function Tv(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function bv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{pv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>dv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",yv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",yv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&yv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&dv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&yv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:pv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?dv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):pv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;dv(`${e} did not request an 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t.lastGamepadValues[i.index][s].buttons||(t.lastGamepadValues[i.index][s].buttons[r]=!1),t.lastGamepadValues[i.index][s].buttons[r]!==i.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,pressed:i.buttons[r].pressed,device:"left"===n.handedness?cv.LeftController:cv.RightController,input:hv[i.mapping]&&hv[i.mapping][r]?hv[i.mapping][r]:uv.Trigger}),t.lastGamepadValues[i.index][s].buttons[r]=i.buttons[r].pressed),t.lastGamepadValues[i.index][s].buttons[r]&&null!=a&&e.move3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,device:"left"===n.handedness?cv.LeftController:cv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{yv(n);const r={...fv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:pv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);if(2===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Tv(o);e.recognizeGesture("TouchEnd",t);const a={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},vv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void gv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const xv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Cv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xv,n),jt.obj(e,t),t._animationExtendedEnd=0,jt.event(e,t,"RenderEvent"),vv.forEach((n=>jt.event(e,t,n))),jt.get(e,t,["initialized","interactorStyle","lastFrameTime","recentAnimationFrameRate","_view"]),jt.setGet(e,t,["container","lightFollowCamera","enabled","enableRender","recognizeGestures","desiredUpdateRate","stillUpdateRate","picker","preventDefaultOnPointerDown","preventDefaultOnPointerUp","mouseScrollDebounceByPass"]),jt.moveToProtected(e,t,["view"]),bv(e,t)}var Sv={newInstance:jt.newInstance(Cv,"vtkRenderWindowInteractor"),extend:Cv,handledEvents:vv,...lv};const{vtkErrorMacro:Av,VOID:Iv}=jt;function wv(e,t){t.classHierarchy.push("vtkInteractorObserver");const n={...e};function r(){for(;t.subscribedEvents.length;)t.subscribedEvents.pop().unsubscribe()}function o(){Sv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Iv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Av("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Pv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function Ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),wv(e,t)}var Rv={newInstance:jt.newInstance(Ov,"vtkInteractorObserver"),extend:Ov,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},Mv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Dv}=Mv,Ev={Rotate:Dv.IS_ROTATE,Pan:Dv.IS_PAN,Spin:Dv.IS_SPIN,Dolly:Dv.IS_DOLLY,CameraPose:Dv.IS_CAMERA_POSE,WindowLevel:Dv.IS_WINDOW_LEVEL,Slice:Dv.IS_SLICE};function Vv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Ev).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Dv.IS_NONE&&(t.state=Ev[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Ev[n]&&(t.state=Dv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":e.pokedRenderer.resetCamera(),n.render();break;case"w":case"W":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Lv={state:Dv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Bv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lv,n),Rv.extend(e,t,n),Vv(e,t)}var Nv={newInstance:jt.newInstance(Bv,"vtkInteractorStyle"),extend:Bv,...Mv};const{States:_v}=Mv;function Fv(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=n.pokedRenderer;switch(t.state){case _v.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad?!n||n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad||t.state!==_v.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===_v.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const t=e.pokedRenderer.getActiveCamera(),n=t.getPhysicalTranslation(),r=.025*t.getPhysicalScale(),o=t.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);t.setPhysicalTranslation(n[0]+o[0]*r,n[1]+o[1]*r,n[2]+o[2]*r)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case _v.IS_DOLLY:e.endDolly();break;case _v.IS_PAN:e.endPan();break;case _v.IS_SPIN:e.endSpin();break;case _v.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(n.pokedRenderer,n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=n.pokedRenderer.getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(n.pokedRenderer,o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(n.pokedRenderer,o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(n.pokedRenderer,o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&n.pokedRenderer.updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=e.pokedRenderer.getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(n.pokedRenderer,r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const kv={motionFactor:10,zoomFactor:10};function Gv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kv,n),Nv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),Fv(e,t)}var Uv={newInstance:jt.newInstance(Gv,"vtkInteractorStyleTrackballCamera"),extend:Gv};function zv(e){return e}function Wv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Wv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Hv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Wv:zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function jv(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const Kv={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function $v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kv,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),jv(e,t)}var qv={newInstance:jt.newInstance($v,"vtkRenderPass"),extend:$v};const{Representation:Xv}=ds,{vtkErrorMacro:Yv}=jt;function Zv(e){const t=cd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function Qv(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Es.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Es.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",Xv.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Nd.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Nd.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Nd.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||Yv("Error setting vertexDC in copy shader 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CT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ST(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group 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0!==arguments[2]?arguments[2]:{};Object.assign(t,AT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ST(e,t)}var wT={newInstance:Et(IT,"vtkWebGPURenderEncoder"),extend:IT},PT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const OT=["getMappedRange","mapAsync","unmap"];function RT(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<OT.length;n++)e[OT[n]]=function(){return t.handle[OT[n]](...arguments)}}const MT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function DT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,MT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),RT(e,t)}var ET={newInstance:jt.newInstance(DT),extend:DT,...PT};const{Representation:VT}=ds,{PrimitiveTypes:LT}=PT;class BT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function NT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function _T(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=NT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=NT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=NT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=NT(n,o,t),n.ibo[n.iboId++]=a}function FT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let kT;const GT=new Uint32Array(1),UT=new Uint32Array(2),zT=new Uint32Array(3),WT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)GT[0]=t[n+a],kT(GT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)UT[0]=t[n+a],UT[1]=t[n+(a+1)%e],kT(UT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o);for(let a=0;a<e-2;a++)UT[0]=t[n+a],UT[1]=t[n+a+2],kT(UT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n],zT[1]=t[n+a+1],zT[2]=t[n+a+2],kT(zT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n+a],zT[1]=t[n+a+1+a%2],zT[2]=t[n+a+1+(a+1)%2],kT(zT,r,o)}};function HT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case LT.Points:return"points";case LT.Lines:return"lines";case LT.Triangles:case LT.TriangleEdges:return"polys";case LT.TriangleStripEdges:case LT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new BT};let d=null;d=o===VT.POINTS||r===LT.Points?WT.anythingToPoints:o===VT.WIREFRAME||r===LT.Lines?WT[`${l}ToWireframe`]:WT[`${l}ToSurface`],kT=FT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),kT=_T,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const jT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function KT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jT,n),ET.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),HT(e,t)}var $T={newInstance:jt.newInstance(KT),extend:KT,...PT};const{BufferUsage:qT}=PT,{vtkErrorMacro:XT}=Kt,{VtkDataTypes:YT}=Es;function ZT(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function QT(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function JT(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===qT.Index&&(n=$T.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=ET.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===qT.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===qT.PointArray){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=ZT(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===qT.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=QT(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ZT(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===qT.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case YT.UNSIGNED_CHAR:t="uint8";break;case YT.FLOAT:t="float32";break;case YT.UNSIGNED_INT:t="uint32";break;case YT.INT:t="sint32";break;case YT.DOUBLE:t="float32";break;case YT.UNSIGNED_SHORT:t="uint16";break;case YT.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||XT(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:qT.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=ET.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const eb={device:null,fullScreenQuadBuffer:null};function tb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eb,n),yt(e,t),At(e,t,["device"]),JT(e,t)}var nb={newInstance:Et(tb),extend:tb,...PT};function rb(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const ob={device:null,handle:null,label:null,options:null};function ab(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ob,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),rb(e,t)}var ib={newInstance:jt.newInstance(ab),extend:ab};function sb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(t.texture.getFormat());t.bindGroupLayoutEntry.texture.sampleType=r.sampleType},e.createFromTextureHandle=(e,n)=>{t.texture=null,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e,t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(n.format);t.bindGroupLayoutEntry.texture.sampleType=r.sampleType,t.bindGroupTime.modified()},e.getBindGroupEntry=()=>({resource:e.getHandle()}),e.getShaderCode=(e,n)=>{let r="f32";"sint"===t.bindGroupLayoutEntry.texture.sampleType?r="i32":"uint"===t.bindGroupLayoutEntry.texture.sampleType&&(r="u32");let o=`@binding(${e}) @group(${n}) var ${t.label}: texture_${t.options.dimension}<${r}>;`;return"depth"===t.bindGroupLayoutEntry.texture.sampleType&&(o=`@binding(${e}) @group(${n}) var ${t.label}: texture_depth_${t.options.dimension};`),o},e.addSampler=(n,r)=>{const o=ib.newInstance({label:`${t.label}Sampler`});o.create(n,r),e.setSampler(o)},e.getBindGroupTime=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.bindGroupTime),e.getHandle=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.handle)}const lb={texture:null,handle:null,sampler:null,label:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),jt.obj(e,t),t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,texture:{sampleType:"float",viewDimension:"2d"}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","texture"]),jt.setGet(e,t,["bindGroupLayoutEntry","label","sampler"]),sb(e,t)}var ub={newInstance:jt.newInstance(cb),extend:cb};const{BufferUsage:db}=nb;function pb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return t.device.getHandle().queue.copyExternalImageToTexture({source:n.canvas,flipY:n.flip},{texture:t.handle,premultipliedAlpha:!0},[t.width,t.height,t.depth]),void(t.ready=!0);n.jsImageData&&!n.nativeArray&&(n.width=n.jsImageData.width,n.height=n.jsImageData.height,n.depth=1,n.format="rgba8unorm",n.flip=!0,n.nativeArray=n.jsImageData.data);const o=tT(t.format);let a=t.width*o.stride;const i=(e,t,n)=>{const r=e.length/(t*n)*e.BYTES_PER_ELEMENT,a=2===o.elementSize&&"float"===o.sampleType;if(a||r%256){const i=e,s=r/i.BYTES_PER_ELEMENT,l=o.elementSize,c=256*Math.floor((s*l+255)/256),u=c/l,d=jt.newTypedArray(a?"Uint16Array":i.constructor.name,u*t*n);for(let e=0;e<t*n;e++)if(a)for(let t=0;t<s;t++)d[e*u+t]=Ad.toHalf(i[e*s+t]);else d.set(i.subarray(e*s,(e+1)*s),e*u);return[d,c]}return[e,r]};if(n.nativeArray&&(r=n.nativeArray),n.image){const e=document.createElement("canvas");e.width=n.image.width,e.height=n.image.height;const t=e.getContext("2d");t.translate(0,e.height),t.scale(1,-1),t.drawImage(n.image,0,0,n.image.width,n.image.height,0,0,e.width,e.height),r=t.getImageData(0,0,n.image.width,n.image.height).data}const s=t.device.createCommandEncoder();if(3!==e.getDimensionality()){const e=Tu.generateMipmaps(r,t.width,t.height,t.mipLevel);let o=t.width,l=t.height;for(let r=0;r<=t.mipLevel;r++){const c=i(e[r],l,1);a=c[1];const u={dataArray:n.dataArray?n.dataArray:null,nativeArray:c[0],usage:db.Texture},d=t.device.getBufferManager().getBuffer(u);s.copyBufferToTexture({buffer:d.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:l},{texture:t.handle,mipLevel:r},[o,l,1]),o/=2,l/=2}t.device.submitCommandEncoder(s),t.ready=!0}else{const e=i(r,t.height,t.depth);a=e[1];const o={dataArray:n.dataArray?n.dataArray:null,usage:db.Texture};o.nativeArray=e[0];const l=t.device.getBufferManager().getBuffer(o);s.copyBufferToTexture({buffer:l.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:t.height},{texture:t.handle},[t.width,t.height,t.depth]),t.device.submitCommandEncoder(s),t.ready=!0}},e.getScale=()=>{const e=tT(t.format);return 2===e.elementSize&&"float"===e.sampleType?1:255},e.getNumberOfComponents=()=>tT(t.format).numComponents,e.getDimensionality=()=>{let e=0;return t.width>1&&e++,t.height>1&&e++,t.depth>1&&e++,e},e.resizeToMatch=e=>{e.getWidth()===t.width&&e.getHeight()===t.height&&e.getDepth()===t.depth||(t.width=e.getWidth(),t.height=e.getHeight(),t.depth=e.getDepth(),t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.resize=function(e,n){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;e===t.width&&n===t.height&&r===t.depth||(t.width=e,t.height=n,t.depth=r,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.createView=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};r.dimension||(r.dimension=1===t.depth?"2d":"3d");const o=ub.newInstance({label:n});return o.create(e,r),o}}const fb={device:null,handle:null,buffer:null,ready:!1,label:null};function gb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fb,n),jt.obj(e,t),jt.get(e,t,["handle","ready","width","height","depth","format","usage"]),jt.setGet(e,t,["device","label"]),pb(e,t)}var mb={newInstance:jt.newInstance(gb),extend:gb};function hb(e,t){t.classHierarchy.push("vtkWebGPUOpaquePass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.renderEncoder)t.colorTexture.resize(r.getCanvas().width,r.getCanvas().height),t.depthTexture.resize(r.getCanvas().width,r.getCanvas().height);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"opaquePassColor"}),t.colorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const n=t.colorTexture.createView("opaquePassColorTexture");t.renderEncoder.setColorTextureView(0,n),t.depthFormat="depth32float",t.depthTexture=mb.newInstance({label:"opaquePassDepth"}),t.depthTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:t.depthFormat,usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("opaquePassDepthTexture");t.renderEncoder.setDepthTextureView(a)}t.renderEncoder.attachTextureViews(),e.setCurrentOperation("opaquePass"),n.setRenderEncoder(t.renderEncoder),n.traverse(e)},e.getColorTextureView=()=>t.renderEncoder.getColorTextureViews()[0],e.getDepthTextureView=()=>t.renderEncoder.getDepthTextureView(),e.createRenderEncoder=()=>{t.renderEncoder=wT.newInstance({label:"OpaquePass"}),t.renderEncoder.setPipelineHash("op")}}const vb={renderEncoder:null,colorTexture:null,depthTexture:null};function yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vb,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),hb(e,t)}var Tb={newInstance:jt.newInstance(yb,"vtkWebGPUOpaquePass"),extend:yb};function bb(e,t){t.classHierarchy.push("vtkWebGPUOrderIndependentTranslucentPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.translucentRenderEncoder)t.translucentColorTexture.resizeToMatch(t.colorTextureView.getTexture()),t.translucentAccumulateTexture.resizeToMatch(t.colorTextureView.getTexture());else{e.createRenderEncoder(),e.createFinalEncoder(),t.translucentColorTexture=mb.newInstance({label:"translucentPassColor"}),t.translucentColorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const n=t.translucentColorTexture.createView("oitpColorTexture");t.translucentRenderEncoder.setColorTextureView(0,n),t.translucentAccumulateTexture=mb.newInstance({label:"translucentPassAccumulate"}),t.translucentAccumulateTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=xT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n")}t.translucentRenderEncoder.setDepthTextureView(t.depthTextureView),t.translucentRenderEncoder.attachTextureViews(),e.setCurrentOperation("translucentPass"),n.setRenderEncoder(t.translucentRenderEncoder),n.traverse(e),e.finalPass(r,n)},e.finalPass=(e,n)=>{t.translucentFinalEncoder.setColorTextureView(0,t.colorTextureView),t.translucentFinalEncoder.attachTextureViews(),t.translucentFinalEncoder.begin(e.getCommandEncoder()),n.scissorAndViewport(t.translucentFinalEncoder),t.fullScreenQuad.prepareAndDraw(t.translucentFinalEncoder),t.translucentFinalEncoder.end()},e.getTextures=()=>[t.translucentColorTexture,t.translucentAccumulateTexture],e.createRenderEncoder=()=>{t.translucentRenderEncoder=wT.newInstance({label:"translucentRender"});const e=t.translucentRenderEncoder.getDescription();e.colorAttachments=[{view:void 0,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:void 0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = computedColor.a;"]).result,t.setCode(n)})),t.translucentRenderEncoder.setPipelineHash("oitpr"),t.translucentRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!1,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one"},alpha:{srcfactor:"one",dstFactor:"one"}}},{format:"r16float",blend:{color:{srcFactor:"zero",dstFactor:"one-minus-src"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createFinalEncoder=()=>{t.translucentFinalEncoder=wT.newInstance({label:"translucentFinal"}),t.translucentFinalEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t.translucentFinalEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t.translucentFinalEncoder.setPipelineHash("oitpf"),t.translucentFinalEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})}}const xb={colorTextureView:null,depthTextureView:null};function Cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xb,n),qv.extend(e,t,n),jt.setGet(e,t,["colorTextureView","depthTextureView"]),bb(e,t)}var Sb={newInstance:jt.newInstance(Cb,"vtkWebGPUOrderIndependentTranslucentPass"),extend:Cb};const{BufferUsage:Ab}=nb,{vtkErrorMacro:Ib}=jt;function wb(e,t){t.classHierarchy.push("vtkWebGPUUniformBuffer"),e.addEntry=(e,n)=>{t._bufferEntryNames.has(e)?Ib(`entry named ${e} already 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o=t.bufferEntries[r];o.packed||(o.packed=!0,o.offset=e,n.push(o),e+=o.sizeInBytes)}t.bufferEntries=n,t._bufferEntryNames.clear();for(let e=0;e<t.bufferEntries.length;e++)t._bufferEntryNames.set(t.bufferEntries[e].name,e);t.sizeInBytes=e,t.sizeInBytes=r*Math.ceil(t.sizeInBytes/r),t.sortDirty=!1},e.sendIfNeeded=e=>{if(!t.UBO){const n={nativeArray:t.Float32Array,usage:Ab.UniformArray,label:t.label};t.UBO=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),t.sendDirty=!1}t.sendDirty&&(e.getHandle().queue.writeBuffer(t.UBO.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes),t.sendDirty=!1),t.sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];a.lastValue!==r&&(i[a.offset/i.BYTES_PER_ELEMENT]=r,t.sendDirty=!0),a.lastValue=r},e.setArray=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];let s=!1;for(let e=0;e<r.length;e++)a.lastValue&&a.lastValue[e]===r[e]||(i[a.offset/i.BYTES_PER_ELEMENT+e]=r[e],s=!0);s&&(t.sendDirty=!0,a.lastValue=[...r])},e.getBindGroupEntry=()=>({resource:{buffer:t.UBO.getHandle()}}),e.getSendTime=()=>t.sendTime.getMTime(),e.getShaderCode=(n,r)=>{e.sortBufferEntries();const o=[`struct ${t.label}Struct\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];o.push(`  ${n.name}: ${n.type},`)}return o.push(`};\n@binding(${n}) @group(${r}) var<uniform> ${t.label}: ${t.label}Struct;`),o.join("\n")}}const Pb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Ob(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),wb(e,t)}var Rb={newInstance:jt.newInstance(Ob,"vtkWebGPUUniformBuffer"),extend:Ob};const{BufferUsage:Mb}=nb,{vtkErrorMacro:Db}=jt;function Eb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void Db(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void Db(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Vb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function Lb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Eb(e,t)}var Bb={newInstance:jt.newInstance(Lb,"vtkWebGPUStorageBuffer"),extend:Lb};const Nb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",_b=new Float64Array(16),Fb=new Float64Array(16);function kb(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Uy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Rg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Uy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Uy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Uy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Rg.COMPOSITE_BLEND?l=!0:n===Rg.MAXIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.MINIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.AVERAGE_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.ADDITIVE_INTENSITY_BLEND&&(a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Uy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(_b),b(_b,_b,o);const p=n.getMatrix();m(Fb,p),h(Fb,Fb),T(_b,Fb,_b);const f=d.getWorldToIndex();T(_b,f,_b);const v=d.getDimensions();g(Fb),x(Fb,Fb,[1/v[0],1/v[1],1/v[2]]),T(_b,Fb,_b);for(let t=0;t<16;t++)a[16*e+t]=_b[t];h(_b,_b);for(let t=0;t<4;t++)i[16*e+4*t]=_b[4*t],i[16*e+4*t+1]=_b[4*t+1],i[16*e+4*t+2]=_b[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=tT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=ib.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const Gb={volumes:null,rowLength:1024,lastVolumeLength:0};function Ub(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gb,n),xT.extend(e,t,n),t.fragmentShaderTemplate=Nb,t.UBO=Rb.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Bb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Bb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),kb(e,t)}var zb={newInstance:jt.newInstance(Ub,"vtkWebGPUVolumePassFSQ"),extend:Ub};const{Representation:Wb}=ds,{BufferUsage:Hb,PrimitiveTypes:jb}=nb,Kb=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],$b="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function qb(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=zb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=xT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=Rb.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Hb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:jb.Triangles,representation:Wb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Hb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+Kb[e][0],l[n++]=r+Kb[e][1],l[n++]=r+Kb[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=wT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=mb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=mb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=mb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=wT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=wT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=wT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const Xb={colorTextureView:null,depthTextureView:null,volumes:null};function Yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Xb,n),qv.extend(e,t,n),t._mapper=vT.newInstance(),t._mapper.setFragmentShaderTemplate($b),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=gu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),qb(e,t)}var Zb={newInstance:jt.newInstance(Yb,"vtkWebGPUVolumePass"),extend:Yb};function Qb(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Tb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Sb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=Zb.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=wT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=ib.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=xT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=ub.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const Jb={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function ex(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jb,n),qv.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),Qb(e,t)}var tx={newInstance:jt.newInstance(ex,"vtkForwardPass"),extend:ex};const{VtkDataTypes:nx}=Es;function rx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case nx.UNSIGNED_CHAR:e.format+="8unorm";break;case nx.FLOAT:case nx.UNSIGNED_INT:case nx.INT:case nx.DOUBLE:case nx.UNSIGNED_SHORT:case nx.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=mb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const ox={handle:null,device:null};function ax(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ox,n),jt.obj(e,t),jt.setGet(e,t,["device"]),rx(e,t)}var ix={newInstance:jt.newInstance(ax),extend:ax};class sx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function lx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const cx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function ux(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new sx,t.shaderCache=Uy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=nb.newInstance(),t.bufferManager.setDevice(e),t.textureManager=ix.newInstance(),t.textureManager.setDevice(e),t.pipelines={},lx(e,t)}var dx={newInstance:Et(ux,"vtkWebGPUDevice"),extend:ux};function px(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=mb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=wT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const fx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function gx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fx,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),px(e,t)}var mx={newInstance:jt.newInstance(gx,"vtkWebGPUHardwareSelectionPass"),extend:gx};const{SelectionContent:hx,SelectionField:vx}=qp,{FieldAssociations:yx}=ol,{vtkErrorMacro:Tx}=jt;function bx(e){return`${e.propID} ${e.compositeID}`}function xx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Cx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=xx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=xx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Cx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Cx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Cx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Cx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Cx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Sx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Tx("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=ET.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=ET.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Cx(e,[n,t],0,u);if(r){const t=bx(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=qp.newInstance();switch(i.setContentType(hx.INDICES),e){case yx.FIELD_ASSOCIATION_CELLS:i.setFieldType(vx.CELL);break;case yx.FIELD_ASSOCIATION_POINTS:i.setFieldType(vx.POINT);break;default:Tx("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Ax={};function Ix(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ax,n),Gp.extend(e,t,n),t._selectionPass=mx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Sx(e,t)}var wx={newInstance:jt.newInstance(Ix,"vtkWebGPUHardwareSelector"),extend:Ix};const Px=Object.create(null);function Ox(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const Rx={};function Mx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rx,n),t.overrides=Px,tn.extend(e,t,n),Ox(0,t)}var Dx={newInstance:jt.newInstance(Mx,"vtkWebGPUViewNodeFactory"),extend:Mx};const{vtkErrorMacro:Ex}=jt,Vx={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function Lx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Ex("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Ex("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=dx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=qv.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Vx,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=ET.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Ad.fromHalf(r.colorValues[o]),s[n+1]=255*Ad.fromHalf(r.colorValues[o+1]),s[n+2]=255*Ad.fromHalf(r.colorValues[o+2]),s[n+3]=255*Ad.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=wx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Bx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Nx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",xy.extend(e,t,n),t.myFactory=Dx.newInstance(),t.myFactory.registerOverride("vtkRenderWindow",Nx),t.renderPasses[0]=tx.newInstance(),t.selector||(t.selector=wx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),Lx(e,t)}),"vtkWebGPURenderWindow");ev("WebGPU",Nx);const _x=Hv(),Fx={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},kx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Gx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Ux(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Gx(t.container,t.containerStyle||Fx),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=av.newInstance(),t.renderer=Qh.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(_x.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Sv.newInstance(),t.interactor.setInteractorStyle(Uv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Gx(t.controlContainer,t.controlPanelStyle||kx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",(t=>{"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}))},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Wx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Ux(e,t)}var Hx={newInstance:jt.newInstance(Wx),extend:Wx},jx={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:Kx,Scale:$x}=jx,{ScalarMappingTarget:qx}=Pl,{vtkDebugMacro:Xx,vtkErrorMacro:Yx,vtkWarningMacro:Zx}=jt;function Qx(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function Jx(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function eC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];Qx(o,i),Qx(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=Jx(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=Jx(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function tC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return Yx("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return Yx("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===$x.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Do(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===Kx.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===Kx.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===Kx.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===Kx.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return Yx("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(Yx("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return Yx("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>t.indexedLookup&&e.getSize()?e.getSize():t.tableSize?t.tableSize:16777216,e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void Yx("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):Xx("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void Zx("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===qx.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===qx.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===qx.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===qx.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=Kx[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(Yx(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=Kx.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const nC={clamping:!0,colorSpace:Kx.RGB,hSVWrap:!0,scale:$x.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function rC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","colorSpace","discretize","numberOfValues"]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),tC(e,t)}var oC={newInstance:jt.newInstance(rC,"vtkColorTransferFunction"),extend:rC,...jx},aC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:iC,ScaleModes:sC}=aC,{vtkErrorMacro:lC}=jt;function cC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(iC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(iC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(iC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(iC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(sC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(sC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(sC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(sC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===sC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(lC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case iC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case iC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case iC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case sC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case sC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case sC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const uC={orient:!0,orientationMode:iC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:sC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function dC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,uC,n),nc.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),cC(e,t)}var pC={newInstance:jt.newInstance(dC,"vtkGlyph3DMapper"),extend:dC,...aC};const{vtkErrorMacro:fC}=jt;function gC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(fC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return fC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return fC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return fC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const i={x:n,y:r,midpoint:o,sharpness:a};let s;for(t.nodes.push(i),e.sortAndUpdateRange(),s=0;s<t.nodes.length&&t.nodes[s].x!==n;s++);return s<t.nodes.length?s:-1},e.setNodes=n=>{t.nodes!==n&&(t.nodes=n,e.sortAndUpdateRange())},e.sortAndUpdateRange=()=>{t.nodes.sort(((e,t)=>e.x-t.x)),e.updateRange()||e.modified()},e.updateRange=()=>{const n=t.range.slice(),r=t.nodes.length;return r?(t.range[0]=t.nodes[0].x,t.range[1]=t.nodes[r-1].x):(t.range[0]=0,t.range[1]=0),(n[0]!==t.range[0]||n[1]!==t.range[1])&&(e.modified(),!0)},e.removePoint=n=>{let r;for(r=0;r<t.nodes.length&&t.nodes[r].x!==n;r++);if(r>=t.nodes.length)return-1;const o=r;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addSegment=(n,r,o,a)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=o?t.nodes.splice(e,1):e++;e.addPoint(n,r,.5,0),e.addPoint(o,a,.5,0)},e.getValue=t=>{const n=[];return e.getTable(t,t,1,n),n[0]},e.adjustRange=n=>{if(n.length<2)return 0;const r=e.getRange();r[0]<n[0]?e.addPoint(n[0],e.getValue(n[0])):e.addPoint(n[0],e.getValue(r[0])),r[1]>n[1]?e.addPoint(n[1],e.getValue(n[1])):e.addPoint(n[1],e.getValue(r[1])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return e.sortAndUpdateRange(),1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{const e=t.nodes.length;if(e<2)return-1;let n=t.nodes[1].x-t.nodes[0].x;for(let r=0;r<e-1;r++){const e=t.nodes[r+1].x-t.nodes[r].x;e<n&&(n=e)}return n},e.getTable=function(e,n,r,o){let a,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=0;const l=t.nodes.length;let c=0;0!==l&&(c=t.nodes[l-1].y);let u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const mC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function hC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),gC(e,t)}var vC={newInstance:jt.newInstance(hC,"vtkPiecewiseFunction"),extend:hC};const{InterpolationType:yC,OpacityMode:TC}=Og,{vtkErrorMacro:bC}=jt;function xC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(bC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=vC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=oC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=vC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return bC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(bC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(yC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(yC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(yC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(yC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const CC={independentComponents:!0,interpolationType:yC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function SC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,CC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:TC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),xC(e,t)}var AC={newInstance:jt.newInstance(SC,"vtkVolumeProperty"),extend:SC,...Og};const{vtkDebugMacro:IC}=jt;function wC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=AC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){IC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const PC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function OC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),wC(e,t)}var RC={newInstance:jt.newInstance(OC,"vtkVolume"),extend:OC};const{BlendMode:MC,FilterMode:DC}=Mg,EC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=vC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function VC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(MC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(MC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(MC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(MC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(MC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(MC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(MC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(DC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(DC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(DC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(DC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Do(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const LC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:MC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:DC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function BC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LC,n),Bs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),VC(e,t)}var NC={newInstance:jt.newInstance(BC,"vtkVolumeMapper"),extend:BC,...EC};const{InterpolationType:_C}=eg,{vtkErrorMacro:FC}=jt;function kC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return FC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(FC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(_C.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(_C.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(_C,t.interpolationType)}const GC={independentComponents:!1,interpolationType:_C.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function UC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,GC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),kC(e,t)}var zC={newInstance:jt.newInstance(UC,"vtkImageProperty"),extend:UC};const{vtkDebugMacro:WC}=jt;function HC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=zC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){WC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const jC={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function KC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),HC(e,t)}var $C={newInstance:jt.newInstance(KC,"vtkImageSlice"),extend:KC};function qC(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const XC={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var YC=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,XC,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),qC(e,t)};function ZC(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:QC,otherStaticMethods:JC}=Ul,{SlicingMode:eS}=sg;function tS(e,t){function n(){let n;switch(t.slicingMode){case eS.X:n=0;break;case eS.Y:n=1;break;case eS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case eS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case eS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case eS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case eS.I:l=Ma(a[0],i[0],i[1]);break;case eS.J:l=Ma(a[1],i[2],i[3]);break;case eS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case eS.I:case eS.J:case eS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case eS.X:e.setSlice(r[0]);break;case eS.Y:e.setSlice(r[1]);break;case eS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(eS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(eS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(eS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(eS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(eS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(eS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case eS.X:n[0]=1;break;case eS.Y:n[1]=1;break;case eS.Z:n[2]=1;break;case eS.I:ea(r,[1,0,0],n);break;case eS.J:ea(r,[0,1,0],n);break;case eS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==eS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case eS.I:r[0]=a,r[1]=a;break;case eS.J:r[2]=a,r[3]=a;break;case eS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case eS.I:a[0]=s-r,a[1]=s+r;break;case eS.J:a[2]=s-r,a[3]=s+r;break;case eS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const nS={slicingMode:eS.NONE,closestIJKAxis:{ijkMode:eS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function rS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nS,n),YC(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),Ul.implementCoincidentTopologyMethods(e,t),tS(e,t)}var oS={newInstance:jt.newInstance(rS,"vtkImageMapper"),extend:rS,...QC,...JC,...sg};const{vtkErrorMacro:aS}=jt;function iS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function sS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void aS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=gu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=mc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Es.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Es.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Es.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),iS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),iS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),iS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),iS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const lS={outputPointsPrecision:js.DEFAULT};function cS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),sS(e,t)}var uS={newInstance:jt.newInstance(cS,"vtkAppendPolyData"),extend:cS};function dS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Nf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=gu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const pS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function fS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),dS(e,t)}var gS={newInstance:jt.newInstance(fS,"vtkConeSource"),extend:fS};function mS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Es.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Es.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Nf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=gu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const hS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function vS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),mS(e,t)}var yS={newInstance:jt.newInstance(vS,"vtkCylinderSource"),extend:vS};function TS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=yS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Bf().rotateZ(-90).apply(a).apply(i);const s=gS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Nf().translate(1-.5*t.tipLength,0,0).apply(c);const u=uS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Nf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Nf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Nf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const bS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function xS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),TS(e,t)}var CS={newInstance:jt.newInstance(xS,"vtkArrowSource"),extend:xS};function SS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Bf().translate(...n).apply(e.getPoints().getData())}function AS(e,t){const n=e.getPoints().getBounds(),r=[0,0,0];r[t]=-n[2*t],Bf().translate(...r).apply(e.getPoints().getData())}function IS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Es.newInstance({name:"color",numberOfComponents:3,values:a}))}function wS(e,t){t.classHierarchy.push("vtkAxesActor");const n=nc.newInstance();e.setMapper(n),e.update=()=>{const e=CS.newInstance({direction:[1,0,0],...t.config}).getOutputData();t.config.recenter?SS(e):AS(e,0),IS(e,...t.xAxisColor);const r=CS.newInstance({direction:[0,1,0],...t.config}).getOutputData();t.config.recenter?SS(r):AS(r,1),IS(r,...t.yAxisColor);const o=CS.newInstance({direction:[0,0,1],...t.config}).getOutputData();t.config.recenter?SS(o):AS(o,2),IS(o,...t.zAxisColor);const a=uS.newInstance();a.setInputData(e),a.addInputData(r),a.addInputData(o),n.setInputConnection(a.getOutputPort())},e.update();const r=jt.debounce(e.update,0),{setConfig:o,setXAxisColor:a,setYAxisColor:i,setZAxisColor:s}=e;e.setConfig=e=>!!o(e)&&(r(),!0),e.setXAxisColor=e=>!!a(e)&&(r(),!0),e.setYAxisColor=e=>!!i(e)&&(r(),!0),e.setZAxisColor=e=>!!s(e)&&(r(),!0)}const PS={config:{recenter:!0,tipResolution:60,tipRadius:.1,tipLength:.2,shaftResolution:60,shaftRadius:.03,invert:!1},xAxisColor:[255,0,0],yAxisColor:[255,255,0],zAxisColor:[0,128,0]};function OS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PS,n),hs.extend(e,t,n),jt.setGet(e,t,["config"]),jt.setGetArray(e,t,["xAxisColor","yAxisColor","zAxisColor"],3,255),wS(e,t)}var RS={newInstance:jt.newInstance(OS,"vtkAxesActor"),extend:OS};const MS="resetcamera",DS="orientation",ES={MODE_RESET_CAMERA:MS,MODE_ORIENTATION:DS,MODE_SAME:"same"};function 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i,s,l;a&&(t.mode===DS?(r[0]=a[0]-a[3],r[1]=a[1]-a[4],r[2]=a[2]-a[5],qo(r),o.setPosition(t.focalPoint[0]+t.distance*r[0],t.focalPoint[1]+t.distance*r[1],t.focalPoint[2]+t.distance*r[2]),o.setFocalPoint(t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),o.setViewUp(a[6],a[7],a[8])):(o.setPosition(a[0],a[1],a[2]),o.setFocalPoint(a[3],a[4],a[5]),o.setViewUp(a[6],a[7],a[8])),t.mode===MS&&t.dstRenderer.resetCamera())},e.delete=Vt((()=>e.setSrcRenderer(null)),e.delete),a()}const LS={mode:DS,focalPoint:[0,0,0],distance:6.8,active:!0};function BS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LS,n),yt(e,t),At(e,t,["mode","active","srcRenderer","dstRenderer","distance"]),Pt(e,t,["focalPoint"],3,0),VS(e,t)}var NS={newInstance:Et(BS,"vtkCameraSynchronizer"),extend:BS,SynchronizationMode:ES};const _S={},FS={CameraSync:class{constructor(e,t){this.ctx=e,this.behavior=NS.newInstance(this.getProperties(t)),this.behavior.update()}getProperties(e){let{actorBounds:t,srcRenderer:n,dstRenderer:r}=e;return{distance:3.4*Math.max(t[1]-t[0],t[3]-t[2],t[5]-t[4]),focalPoint:[.5*(t[0]+t[1]),.5*(t[2]+t[3]),.5*(t[4]+t[5])],mode:NS.SynchronizationMode.MODE_ORIENTATION,srcRenderer:this.ctx.getInstance(n),dstRenderer:this.ctx.getInstance(r)}}update(e){this.behavior.set(this.getProperties(e)),this.behavior.update()}delete(){this.behavior.delete()}}};var kS=function(e,t,n){const r=e.get("synchronizedViewId").synchronizedViewId;if(!t.behaviors||!r)return;_S[r]||(_S[r]={});const o=_S[r];if(t.behaviors.autoOrientation){const t=e.getRenderers();if(!o.autoOrientationAxes&&2===t.length){let e=null,n=null;for(let r=0;r<t.length;r++){const o=t[r];o.getInteractive()?e=o:n=o}e&&n&&(o.autoOrientationAxes=NS.newInstance({srcRenderer:e,dstRenderer:n}))}o.autoOrientationAxes&&2!==t.length&&(o.autoOrientationAxes.delete(),delete o.autoOrientationAxes)}const a=Object.keys(t.behaviors),i=Object.keys(o);for(let e=0;e<a.length;e++){const r=a[e];if(o[r])o[r].update(t.behaviors[r]);else{const e=t.behaviors[r];FS[e.type]?o[r]=new FS[e.type](n,e):console.log("No mapping for",e)}}for(let e=0;e<i.length;e++){const n=a[e];t.behaviors[n]||(o[n].delete(),delete o[n])}};const GS={},US=/instance:\${([^}]+)}/,zS={},WS=[],HS={},jS={vtkPoints:mc,vtkCellArray:cc,vtkDataArray:Es};function KS(e){return e.map((e=>US.exec(e))).filter((e=>e)).map((e=>e[1]))}function $S(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return e.dependencies&&e.dependencies.forEach((e=>{t.push(e.id),$S(e,t)})),t}function qS(e,t,n,r){if(!t)return Promise.reject(new Error("No instance provided."));const o=GS[e];return 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";
+var base64Str = 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n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15],y=n*s-r*i,T=n*l-o*i,b=n*c-a*i,x=r*l-o*s,C=r*c-a*s,S=o*c-a*l,A=u*m-d*g,I=u*h-p*g,w=u*v-f*g,P=d*h-p*m,O=d*v-f*m,R=p*v-f*h,M=y*R-T*O+b*P+x*w-C*I+S*A;return M?(M=1/M,e[0]=(s*R-l*O+c*P)*M,e[1]=(o*O-r*R-a*P)*M,e[2]=(m*S-h*C+v*x)*M,e[3]=(p*C-d*S-f*x)*M,e[4]=(l*w-i*R-c*I)*M,e[5]=(n*R-o*w+a*I)*M,e[6]=(h*b-g*S-v*T)*M,e[7]=(u*S-p*b+f*T)*M,e[8]=(i*O-s*w+c*A)*M,e[9]=(r*w-n*O-a*A)*M,e[10]=(g*C-m*b+v*y)*M,e[11]=(d*b-u*C-f*y)*M,e[12]=(s*I-i*P-l*A)*M,e[13]=(n*P-r*I+o*A)*M,e[14]=(m*T-g*x-h*y)*M,e[15]=(u*x-d*T+p*y)*M,e):null}function v(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15];return e[0]=s*(p*v-f*h)-d*(l*v-c*h)+m*(l*f-c*p),e[1]=-(r*(p*v-f*h)-d*(o*v-a*h)+m*(o*f-a*p)),e[2]=r*(l*v-c*h)-s*(o*v-a*h)+m*(o*c-a*l),e[3]=-(r*(l*f-c*p)-s*(o*f-a*p)+d*(o*c-a*l)),e[4]=-(i*(p*v-f*h)-u*(l*v-c*h)+g*(l*f-c*p)),e[5]=n*(p*v-f*h)-u*(o*v-a*h)+g*(o*f-a*p),e[6]=-(n*(l*v-c*h)-i*(o*v-a*h)+g*(o*c-a*l)),e[7]=n*(l*f-c*p)-i*(o*f-a*p)+u*(o*c-a*l),e[8]=i*(d*v-f*m)-u*(s*v-c*m)+g*(s*f-c*d),e[9]=-(n*(d*v-f*m)-u*(r*v-a*m)+g*(r*f-a*d)),e[10]=n*(s*v-c*m)-i*(r*v-a*m)+g*(r*c-a*s),e[11]=-(n*(s*f-c*d)-i*(r*f-a*d)+u*(r*c-a*s)),e[12]=-(i*(d*h-p*m)-u*(s*h-l*m)+g*(s*p-l*d)),e[13]=n*(d*h-p*m)-u*(r*h-o*m)+g*(r*p-o*d),e[14]=-(n*(s*h-l*m)-i*(r*h-o*m)+g*(r*l-o*s)),e[15]=n*(s*p-l*d)-i*(r*p-o*d)+u*(r*l-o*s),e}function y(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8],u=e[9],d=e[10],p=e[11],f=e[12],g=e[13],m=e[14],h=e[15];return(t*i-n*a)*(d*h-p*m)-(t*s-r*a)*(u*h-p*g)+(t*l-o*a)*(u*m-d*g)+(n*s-r*i)*(c*h-p*f)-(n*l-o*i)*(c*m-d*f)+(r*l-o*s)*(c*g-u*f)}function T(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return e[0]===t[0]&&e[1]===t[1]&&e[2]===t[2]&&e[3]===t[3]&&e[4]===t[4]&&e[5]===t[5]&&e[6]===t[6]&&e[7]===t[7]&&e[8]===t[8]&&e[9]===t[9]&&e[10]===t[10]&&e[11]===t[11]&&e[12]===t[12]&&e[13]===t[13]&&e[14]===t[14]&&e[15]===t[15]}function re(e,t){var n=e[0],r=e[1],o=e[2],i=e[3],s=e[4],l=e[5],c=e[6],u=e[7],d=e[8],p=e[9],f=e[10],g=e[11],m=e[12],h=e[13],v=e[14],y=e[15],T=t[0],b=t[1],x=t[2],C=t[3],S=t[4],A=t[5],I=t[6],w=t[7],P=t[8],O=t[9],R=t[10],M=t[11],E=t[12],D=t[13],V=t[14],L=t[15];return Math.abs(n-T)<=a*Math.max(1,Math.abs(n),Math.abs(T))&&Math.abs(r-b)<=a*Math.max(1,Math.abs(r),Math.abs(b))&&Math.abs(o-x)<=a*Math.max(1,Math.abs(o),Math.abs(x))&&Math.abs(i-C)<=a*Math.max(1,Math.abs(i),Math.abs(C))&&Math.abs(s-S)<=a*Math.max(1,Math.abs(s),Math.abs(S))&&Math.abs(l-A)<=a*Math.max(1,Math.abs(l),Math.abs(A))&&Math.abs(c-I)<=a*Math.max(1,Math.abs(c),Math.abs(I))&&Math.abs(u-w)<=a*Math.max(1,Math.abs(u),Math.abs(w))&&Math.abs(d-P)<=a*Math.max(1,Math.abs(d),Math.abs(P))&&Math.abs(p-O)<=a*Math.max(1,Math.abs(p),Math.abs(O))&&Math.abs(f-R)<=a*Math.max(1,Math.abs(f),Math.abs(R))&&Math.abs(g-M)<=a*Math.max(1,Math.abs(g),Math.abs(M))&&Math.abs(m-E)<=a*Math.max(1,Math.abs(m),Math.abs(E))&&Math.abs(h-D)<=a*Math.max(1,Math.abs(h),Math.abs(D))&&Math.abs(v-V)<=a*Math.max(1,Math.abs(v),Math.abs(V))&&Math.abs(y-L)<=a*Math.max(1,Math.abs(y),Math.abs(L))}var oe=T,ae=J;function ie(){var e=new i(9);return i!=Float32Array&&(e[1]=0,e[2]=0,e[3]=0,e[5]=0,e[6]=0,e[7]=0),e[0]=1,e[4]=1,e[8]=1,e}function se(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[4],e[4]=t[5],e[5]=t[6],e[6]=t[8],e[7]=t[9],e[8]=t[10],e}function le(e){var t=new i(9);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t}function ce(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function ue(e,t,n,r,o,a,s,l,c){var u=new i(9);return u[0]=e,u[1]=t,u[2]=n,u[3]=r,u[4]=o,u[5]=a,u[6]=s,u[7]=l,u[8]=c,u}function de(e,t,n,r,o,a,i,s,l,c){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e}function pe(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function fe(e,t){if(e===t){var n=t[1],r=t[2],o=t[5];e[1]=t[3],e[2]=t[6],e[3]=n,e[5]=t[7],e[6]=r,e[7]=o}else e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8];return e}function ge(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=u*i-s*c,p=-u*a+s*l,f=c*a-i*l,g=n*d+r*p+o*f;return g?(g=1/g,e[0]=d*g,e[1]=(-u*r+o*c)*g,e[2]=(s*r-o*i)*g,e[3]=p*g,e[4]=(u*n-o*l)*g,e[5]=(-s*n+o*a)*g,e[6]=f*g,e[7]=(-c*n+r*l)*g,e[8]=(i*n-r*a)*g,e):null}function me(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8];return e[0]=i*u-s*c,e[1]=o*c-r*u,e[2]=r*s-o*i,e[3]=s*l-a*u,e[4]=n*u-o*l,e[5]=o*a-n*s,e[6]=a*c-i*l,e[7]=r*l-n*c,e[8]=n*i-r*a,e}function he(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8];return t*(c*a-i*l)+n*(-c*o+i*s)+r*(l*o-a*s)}function ve(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=n[0],f=n[1],g=n[2],m=n[3],h=n[4],v=n[5],y=n[6],T=n[7],b=n[8];return e[0]=p*r+f*i+g*c,e[1]=p*o+f*s+g*u,e[2]=p*a+f*l+g*d,e[3]=m*r+h*i+v*c,e[4]=m*o+h*s+v*u,e[5]=m*a+h*l+v*d,e[6]=y*r+T*i+b*c,e[7]=y*o+T*s+b*u,e[8]=y*a+T*l+b*d,e}function ye(e,t,n){var 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n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const cs={color:[1,1,1],ambientColor:[1,1,1],diffuseColor:[1,1,1],specularColor:[1,1,1],edgeColor:[0,0,0],ambient:0,diffuse:1,metallic:0,roughness:.6,normalStrength:1,emission:1,baseIOR:1.45,specular:0,specularPower:1,opacity:1,interpolation:is.GOURAUD,representation:as.SURFACE,edgeVisibility:!1,backfaceCulling:!1,frontfaceCulling:!1,pointSize:1,lineWidth:1,lighting:!0,shading:!1,materialName:null};function us(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cs,n),jt.obj(e,t),jt.setGet(e,t,["lighting","interpolation","ambient","diffuse","metallic","roughness","normalStrength","emission","baseIOR","specular","specularPower","opacity","edgeVisibility","lineWidth","pointSize","backfaceCulling","frontfaceCulling","representation","diffuseTexture","metallicTexture","roughnessTexture","normalTexture","ambientOcclusionTexture","emissionTexture"]),jt.setGetArray(e,t,["ambientColor","specularColor","diffuseColor","edgeColor"],3),ls(e,t)}var ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||n[0]!==t.mapperBounds[0]||n[1]!==t.mapperBounds[1]||n[2]!==t.mapperBounds[2]||n[3]!==t.mapperBounds[3]||n[4]!==t.mapperBounds[4]||n[5]!==t.mapperBounds[5]||e.getMTime()>t.boundsMTime.getMTime()){ps("Recomputing bounds..."),t.mapperBounds=n.concat();const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}return t.bounds},e.getMTime=()=>{let e=n.getMTime();if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}if(null!==t.backfaceProperty){const n=t.backfaceProperty.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection(),e.processSelectorPixelBuffers=(e,n)=>{t.mapper&&t.mapper.processSelectorPixelBuffers&&t.mapper.processSelectorPixelBuffers(e,n)}}const gs={mapper:null,property:null,backfaceProperty:null,forceOpaque:!1,forceTranslucent:!1,bounds:[1,-1,1,-1,1,-1]};function ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gs,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["backfaceProperty","forceOpaque","forceTranslucent","mapper"]),fs(e,t)}var hs={newInstance:jt.newInstance(ms,"vtkActor"),extend:ms};const vs={Int8Array:1,Uint8Array:1,Uint8ClampedArray:1,Int16Array:2,Uint16Array:2,Int32Array:4,Uint32Array:4,Float32Array:4,Float64Array:8},ys={VOID:"",CHAR:"Int8Array",SIGNED_CHAR:"Int8Array",UNSIGNED_CHAR:"Uint8Array",UNSIGNED_CHAR_CLAMPED:"Uint8ClampedArray",SHORT:"Int16Array",UNSIGNED_SHORT:"Uint16Array",INT:"Int32Array",UNSIGNED_INT:"Uint32Array",FLOAT:"Float32Array",DOUBLE:"Float64Array"};var Ts={DefaultDataType:ys.FLOAT,DataTypeByteSize:vs,VtkDataTypes:ys};const{vtkErrorMacro:bs}=Kt,{DefaultDataType:xs}=Ts,Cs=1e-6;function Ss(e,t,n){const r=e.length;let o,a,i=Number.MAX_VALUE,s=-Number.MAX_VALUE;for(a=t;a<r;a+=n)if(!Number.isNaN(e[a])){i=e[a],s=i;break}for(;a<r;a+=n)o=e[a],o<i?i=o:o>s&&(s=o);return{min:i,max:s}}function As(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;if(t<0&&n>1){const t=e.length/n,r=new Float64Array(t);for(let o=0,a=0;o<t;++o){for(let t=a+n;a<t;++a)r[o]+=e[a]*e[a];r[o]**=.5}return 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t.size>n*r&&(t.size=n*r,e.dataChange()),!0}t.classHierarchy.push("vtkDataArray"),e.dataChange=()=>{t.ranges=null,e.modified()},e.resize=r=>{n(r);const o=r*e.getNumberOfComponents();return t.size!==o&&(t.size=o,e.dataChange(),!0)},e.initialize=()=>{e.resize(0)},e.getElementComponentSize=()=>t.values.BYTES_PER_ELEMENT,e.getComponent=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return t.values[e*t.numberOfComponents+n]},e.setComponent=(n,r,o)=>{o!==t.values[n*t.numberOfComponents+r]&&(t.values[n*t.numberOfComponents+r]=o,e.dataChange())},e.getValue=n=>{const r=n/t.numberOfComponents,o=n%t.numberOfComponents;return e.getComponent(r,o)},e.setValue=(n,r)=>{const o=n/t.numberOfComponents,a=n%t.numberOfComponents;e.setComponent(o,a,r)},e.getData=()=>t.size===t.values.length?t.values:t.values.subarray(0,t.size),e.getRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,r=n;r<0&&(r=1===t.numberOfComponents?0:t.numberOfComponents);let o=null;return t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents)),o=t.ranges[r],o?(t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple):(o=As(e.getData(),n,t.numberOfComponents),t.ranges[r]=o,t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple)},e.setRange=(e,n)=>{t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents));const r={min:e.min,max:e.max};return t.ranges[n]=r,t.rangeTuple[0]=r.min,t.rangeTuple[1]=r.max,t.rangeTuple},e.setTuple=(e,n)=>{const r=e*t.numberOfComponents;for(let e=0;e<t.numberOfComponents;e++)t.values[r+e]=n[e]},e.setTuples=(e,n)=>{let r=e*t.numberOfComponents;const o=Math.min(n.length,t.size-r);for(let e=0;e<o;)t.values[r++]=n[e++]},e.insertTuple=(r,o)=>(t.size<=r*t.numberOfComponents&&(t.size=(r+1)*t.numberOfComponents,n(r+1)),e.setTuple(r,o),r),e.insertTuples=(r,o)=>{const a=r+o.length/t.numberOfComponents;return t.size<a*t.numberOfComponents&&(t.size=a*t.numberOfComponents,n(a)),e.setTuples(r,o),a},e.insertNextTuple=n=>{const 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0!==arguments[0]?arguments[0]:1;return e*t.numberOfComponents},e.getNumberOfComponents=()=>t.numberOfComponents,e.getNumberOfValues=()=>t.size,e.getNumberOfTuples=()=>t.size/t.numberOfComponents,e.getDataType=()=>t.dataType,e.newClone=()=>Es({empty:!0,name:t.name,dataType:t.dataType,numberOfComponents:t.numberOfComponents}),e.getName=()=>(t.name||(e.modified(),t.name=`vtkDataArray${e.getMTime()}`),t.name),e.setData=(n,r)=>{t.values=n,t.size=n.length,t.dataType=ws(n),r&&(t.numberOfComponents=r),t.size%t.numberOfComponents!=0&&(t.numberOfComponents=1),e.dataChange()},e.getState=()=>{if(t.deleted)return null;const n={...t,vtkClass:e.getClassName()};n.values=Array.from(n.values),delete n.buffer,Object.keys(n).forEach((e=>{n[e]||delete n[e]}));const r={};return Object.keys(n).sort().forEach((e=>{r[e]=n[e]})),r.mtime&&delete r.mtime,r},e.deepCopy=n=>{const r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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r=t.clippingPlanes.indexOf(n);return-1!==r&&(t.clippingPlanes.splice(r,1),e.modified(),!0)},e.getClippingPlanes=()=>t.clippingPlanes,e.setClippingPlanes=t=>{if(t)if(Array.isArray(t)){const n=t.length;for(let r=0;r<n&&r<6;r++)e.addClippingPlane(t[r])}else e.addClippingPlane(t)},e.getClippingPlaneInDataCoords=(e,n,r)=>{const o=t.clippingPlanes,a=e;if(o){const e=o.length;if(n>=0&&n<e){const e=o[n],t=e.getNormal(),i=e.getOrigin(),s=t[0],l=t[1],c=t[2],u=-(s*i[0]+l*i[1]+c*i[2]);return r[0]=s*a[0]+l*a[4]+c*a[8]+u*a[12],r[1]=s*a[1]+l*a[5]+c*a[9]+u*a[13],r[2]=s*a[2]+l*a[6]+c*a[10]+u*a[14],void(r[3]=s*a[3]+l*a[7]+c*a[11]+u*a[15])}}jt.vtkErrorMacro(`Clipping plane index ${n} is out of range.`)}}const Ls={clippingPlanes:[]};var Bs=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ls,n),jt.obj(e,t),jt.algo(e,t,1,0),t.clippingPlanes||(t.clippingPlanes=[]),Vs(e,t)};function Ns(e,t){e.getBounds=()=>(jt.vtkErrorMacro("vtkAbstractMapper3D.getBounds - NOT 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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e=a>-1?a:r;t.interpolateTuple(e,s,r,s,o,i),Gs("Unexpected case in interpolateData")}else t.insertTuples(s.getTuples());else ks("Unhandled case in interpolateData");else if(r<0||o<0||r>s.getNumberOfTuples())e.addArray(s),n.getAttributes(s).forEach((t=>{e.setAttribute(s,t)}));else{const l=s.getNumberOfComponents();let c=s.getNumberOfValues();const u=a>-1?a:r;c<=u*l&&(c=(u+1)*l),t=Ds.newInstance({name:s.getName(),dataType:s.getDataType(),numberOfComponents:l,values:jt.newTypedArray(s.getDataType(),c),size:0}),t.interpolateTuple(u,s,r,s,o,i),e.addArray(t),n.getAttributes(s).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.copyFieldOn=e=>{t.copyFieldFlags[e]=!0},e.copyFieldOff=e=>{t.copyFieldFlags[e]=!1},e.copyAllOn=()=>{t.doCopyAllOn&&!t.doCopyAllOff||(t.doCopyAllOn=!0,t.doCopyAllOff=!1,e.modified())},e.copyAllOff=()=>{!t.doCopyAllOn&&t.doCopyAllOff||(t.doCopyAllOn=!1,t.doCopyAllOff=!0,e.modified())},e.clearFieldFlags=()=>{t.copyFieldFlags={}},e.deepCopy=e=>{t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. The attribute must be a vtkDataArray.`),-1;if(n&&!e.checkNumberOfComponents(n,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1;let i=t[`active${a}`];if(i>=0&&i<t.arrays.length){if(t.arrays[i]===n)return i;e.removeArrayByIndex(i)}return n?(i=e.addArray(n),t[`active${a}`]=i):t[`active${a}`]=-1,e.modified(),t[`active${a}`]},e.getAttributes=t=>n.filter((n=>e[`get${n}`]()===t)),e.setActiveAttributeByName=(t,n)=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e.setActiveAttributeByIndex=(n,o)=>{const a=r(o);if(n>=0&&n<t.arrays.length){if("PEDIGREEIDS"!==a.toUpperCase()){const t=e.getArrayByIndex(n);if(!t.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. Only vtkDataArray subclasses can be set as active attributes.`),-1;if(!e.checkNumberOfComponents(t,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1}return t[`active${a}`]=n,e.modified(),n}return-1===n&&(t[`active${a}`]=n,e.modified()),-1},e.getActiveAttribute=t=>{const n=r(t);return e[`get${n}`]()},e.removeAllArrays=()=>{n.forEach((e=>{t[`active${e}`]=-1})),o.removeAllArrays()},e.removeArrayByIndex=e=>(-1!==e&&n.forEach((n=>{e===t[`active${n}`]?t[`active${n}`]=-1:e<t[`active${n}`]&&(t[`active${n}`]-=1)})),o.removeArrayByIndex(e)),n.forEach((n=>{const r=`active${n}`;e[`get${n}`]=()=>e.getArrayByIndex(t[r]),e[`set${n}`]=t=>e.setAttribute(t,n),e[`setActive${n}`]=t=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e[`copy${n}Off`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!1},e[`copy${n}On`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!0}})),e.initializeAttributeCopyFlags=()=>{t.copyAttributeFlags=[],Object.keys(qs).filter((e=>"ALLCOPY"!==e)).forEach((e=>{t.copyAttributeFlags[qs[e]]=Object.keys($s).filter((e=>"NUM_ATTRIBUTES"!==e)).reduce(((e,t)=>(e[$s[t]]=!0,e)),[])})),t.copyAttributeFlags[qs.COPYTUPLE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.INTERPOLATE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.COPYTUPLE][$s.PEDIGREEIDS]=!1},e.initialize=jt.chain(e.initialize,e.initializeAttributeCopyFlags),t.dataArrays&&Object.keys(t.dataArrays).length&&Object.keys(t.dataArrays).forEach((n=>{t.dataArrays[n].ref||"vtkDataArray"!==t.dataArrays[n].type||e.addArray(Ds.newInstance(t.dataArrays[n]))}));const a=e.shallowCopy;e.shallowCopy=(e,n)=>{a(e,n),t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.shallowCopy(e,n),{data:t}}))},e.initializeAttributeCopyFlags()}const Zs={activeScalars:-1,activeVectors:-1,activeTensors:-1,activeNormals:-1,activeTCoords:-1,activeGlobalIds:-1,activePedigreeIds:-1};function Qs(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zs,n),Hs.extend(e,t,n),jt.setGet(e,t,["activeScalars","activeNormals","activeTCoords","activeVectors","activeTensors","activeGlobalIds","activePedigreeIds"]),t.arrays||(t.arrays={}),Ys(e,t)}var Js={newInstance:jt.newInstance(Qs,"vtkDataSetAttributes"),extend:Qs,...Ks};const el=["pointData","cellData","fieldData"];function tl(e,t){t.classHierarchy.push("vtkDataSet"),el.forEach((e=>{t[e]?t[e]=We(t[e]):t[e]=Js.newInstance()}));const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),el.forEach((n=>{t[n]=Js.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))}}const nl={};function rl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nl,n),jt.obj(e,t),jt.setGet(e,t,el),tl(e,t)}var ol={newInstance:jt.newInstance(rl,"vtkDataSet"),extend:rl,FieldDataTypes:{UNIFORM:0,DATA_OBJECT_FIELD:0,COORDINATE:1,POINT_DATA:1,POINT:2,POINT_FIELD_DATA:2,CELL:3,CELL_FIELD_DATA:3,VERTEX:4,VERTEX_FIELD_DATA:4,EDGE:5,EDGE_FIELD_DATA:5,ROW:6,ROW_DATA:6},FieldAssociations:{FIELD_ASSOCIATION_POINTS:0,FIELD_ASSOCIATION_CELLS:1,FIELD_ASSOCIATION_NONE:2,FIELD_ASSOCIATION_POINTS_THEN_CELLS:3,FIELD_ASSOCIATION_VERTICES:4,FIELD_ASSOCIATION_EDGES:5,FIELD_ASSOCIATION_ROWS:6,NUMBER_OF_ASSOCIATIONS:7}};const al={UNCHANGED:0,SINGLE_POINT:1,X_LINE:2,Y_LINE:3,Z_LINE:4,XY_PLANE:5,YZ_PLANE:6,XZ_PLANE:7,XYZ_GRID:8,EMPTY:9};var il={StructuredType:al};const{StructuredType:sl}=il;var ll={getDataDescriptionFromExtent:function(e){let t=0;for(let n=0;n<3;++n)e[2*n]<e[2*n+1]&&t++;return e[0]>e[1]||e[2]>e[3]||e[4]>e[5]?sl.EMPTY:3===t?sl.XYZ_GRID:2===t?e[0]===e[1]?sl.YZ_PLANE:e[2]===e[3]?sl.XZ_PLANE:sl.XY_PLANE:1===t?e[0]<e[1]?sl.X_LINE:e[2]<e[3]?sl.Y_LINE:sl.Z_LINE:sl.SINGLE_POINT},...il};const{vtkErrorMacro:cl}=jt;function ul(e,t){t.classHierarchy.push("vtkImageData"),e.setExtent=function(){if(t.deleted)return cl("instance deleted - cannot call any method"),!1;for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];const a=1===r.length?r[0]:r;if(6!==a.length)return!1;const i=t.extent.some(((e,t)=>e!==a[t]));return i&&(t.extent=a.slice(),t.dataDescription=ll.getDataDescriptionFromExtent(t.extent),e.modified()),i},e.setDimensions=function(){let n,r,o;if(t.deleted)cl("instance deleted - cannot call any method");else{if(1===arguments.length){const e=arguments.length<=0?void 0:arguments[0];n=e[0],r=e[1],o=e[2]}else{if(3!==arguments.length)return void cl("Bad dimension specification");n=arguments.length<=0?void 0:arguments[0],r=arguments.length<=1?void 0:arguments[1],o=arguments.length<=2?void 0:arguments[2]}e.setExtent(0,n-1,0,r-1,0,o-1)}},e.getDimensions=()=>[t.extent[1]-t.extent[0]+1,t.extent[3]-t.extent[2]+1,t.extent[5]-t.extent[4]+1],e.getNumberOfCells=()=>{const t=e.getDimensions();let n=1;for(let e=0;e<3;e++){if(0===t[e])return 0;t[e]>1&&(n*=t[e]-1)}return n},e.getNumberOfPoints=()=>{const t=e.getDimensions();return t[0]*t[1]*t[2]},e.getPoint=n=>{const r=e.getDimensions();if(0===r[0]||0===r[1]||0===r[2])return cl("Requesting a point from an empty image."),null;const o=new Float64Array(3);switch(t.dataDescription){case al.EMPTY:return null;case al.SINGLE_POINT:break;case al.X_LINE:o[0]=n;break;case al.Y_LINE:o[1]=n;break;case al.Z_LINE:o[2]=n;break;case al.XY_PLANE:o[0]=n%r[0],o[1]=n/r[0];break;case al.YZ_PLANE:o[1]=n%r[1],o[2]=n/r[1];break;case al.XZ_PLANE:o[0]=n%r[0],o[2]=n/r[0];break;case al.XYZ_GRID:o[0]=n%r[0],o[1]=n/r[0]%r[1],o[2]=n/(r[0]*r[1]);break;default:cl("Invalid dataDescription")}const a=[0,0,0];return e.indexToWorld(o,a),a},e.getBounds=()=>e.extentToBounds(e.getSpatialExtent()),e.extentToBounds=e=>ki.transformBounds(e,t.indexToWorld),e.getSpatialExtent=()=>ki.inflate([...t.extent],.5),e.computeTransforms=()=>{w(t.indexToWorld,t.origin),t.indexToWorld[0]=t.direction[0],t.indexToWorld[1]=t.direction[1],t.indexToWorld[2]=t.direction[2],t.indexToWorld[4]=t.direction[3],t.indexToWorld[5]=t.direction[4],t.indexToWorld[6]=t.direction[5],t.indexToWorld[8]=t.direction[6],t.indexToWorld[9]=t.direction[7],t.indexToWorld[10]=t.direction[8],x(t.indexToWorld,t.indexToWorld,t.spacing),h(t.worldToIndex,t.indexToWorld)},e.indexToWorld=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.indexToWorld),n},e.indexToWorldVec3=e.indexToWorld,e.worldToIndex=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.worldToIndex),n},e.worldToIndexVec3=e.worldToIndex,e.indexToWorldBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.indexToWorld,n)},e.worldToIndexBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.worldToIndex,n)},e.onModified(e.computeTransforms),e.computeTransforms(),e.getCenter=()=>ki.getCenter(e.getBounds()),e.computeHistogram=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=[0,0,0,0,0,0];e.worldToIndexBounds(t,r);const o=[0,0,0],a=[0,0,0];ki.computeCornerPoints(r,o,a),ua(o,o),ua(a,a);const i=e.getDimensions();Ea(o,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],o),Ea(a,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],a);const s=i[0],l=i[0]*i[1],c=e.getPointData().getScalars().getData();let u=-1/0,d=1/0,p=0,f=0,g=0;for(let e=o[2];e<=a[2];e++)for(let t=o[1];t<=a[1];t++){let i=o[0]+t*s+e*l;for(let s=o[0];s<=a[0];s++){if(!n||n([s,t,e],r)){const e=c[i];e>u&&(u=e),e<d&&(d=e),p+=e*e,f+=e,g+=1}++i}}const m=g>0?f/g:0,h=g?Math.abs(p/g-m*m):0,v=Math.sqrt(h);return{minimum:d,maximum:u,average:m,variance:h,sigma:v,count:g}},e.computeIncrements=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;const n=[];let r=t;for(let t=0;t<3;++t)n[t]=r,r*=e[2*t+1]-e[2*t]+1;return n},e.computeOffsetIndex=t=>{let[n,r,o]=t;const a=e.getExtent(),i=e.getPointData().getScalars().getNumberOfComponents(),s=e.computeIncrements(a,i);return Math.floor((Math.round(n)-a[0])*s[0]+(Math.round(r)-a[2])*s[1]+(Math.round(o)-a[4])*s[2])},e.getOffsetIndexFromWorld=t=>{const n=e.getExtent(),r=e.worldToIndex(t);for(let e=0;e<3;++e)if(r[e]<n[2*e]||r[e]>n[2*e+1])return cl(`GetScalarPointer: Pixel ${r} is not in memory. Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. Current Scalar numberOfComponents: ${r}`),NaN;const o=e.getOffsetIndexFromWorld(t);return Number.isNaN(o)?o:e.getPointData().getScalars().getComponent(o,n)}}const dl={direction:null,indexToWorld:null,worldToIndex:null,spacing:[1,1,1],origin:[0,0,0],extent:[0,-1,0,-1,0,-1],dataDescription:al.EMPTY};function pl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dl,n),ol.extend(e,t,n),t.direction?Array.isArray(t.direction)&&(t.direction=new Float64Array(t.direction.slice(0,9))):t.direction=pe(new Float64Array(9)),t.indexToWorld=new Float64Array(16),t.worldToIndex=new Float64Array(16),jt.get(e,t,["indexToWorld","worldToIndex"]),jt.setGetArray(e,t,["origin","spacing"],3),jt.setGetArray(e,t,["direction"],9),jt.getArray(e,t,["extent"],6),ul(e,t)}var fl={newInstance:jt.newInstance(pl,"vtkImageData"),extend:pl};const gl={LUMINANCE:1,LUMINANCE_ALPHA:2,RGB:3,RGBA:4};var ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const 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u=(n[1]-n[0])/(r-1),[d,p]=[n[0]-u,n[1]+u],f=d-.5*u,g=1/(p-d+u),m=d,h=(r+1)/(p-d),v=e.getData(),y=e.getNumberOfTuples(),T=e.getNumberOfComponents(),b=t<0||t>=T,x=o[2]<=1?2:3,C=Ds.newInstance({numberOfComponents:x,values:new Float32Array(y*x)}),S=C.getData(),A=[0,0,0];tc(A,r+2,o);let I=0,w=0;const P=[.5,.5,.5];for(let e=0;e<y;++e){let e;if(b){let t=0;for(let e=0;e<T;++e){const n=v[I+e];t+=n*n}e=Math.sqrt(t)}else e=v[I+t];if(I+=T,a&&(e=Dl.applyLogScale(e,n,n)),_a(e))P[0]=A[0],P[1]=A[1],P[2]=A[2];else if(i){let t=(e-m)*h;t<1?t=0:t>r&&(t=r+1),tc(P,t,o)}else{P[1]=.49;const t=(e-f)*g;P[0]=t>1e3?1e3:t<-1e3?-1e3:t}for(let e=0;e<x;++e)S[w++]=P[e]}return nc.set(e,{stringHash:l,textureCoordinates:C}),C}(n,u,a,t.numberOfColorsInRange,t.colorTextureMap.getDimensions(),i,r)},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const 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fc={newInstance:jt.newInstance(pc,"vtkCellArray"),extend:pc,...cc};const{vtkErrorMacro:gc}=jt,mc=[1,-1,1,-1,1,-1];function hc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return gc(`getBounds called on an array with components of\n        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n},e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.deepCopy=e=>{e.initialize(t.points,t.pointsIds)},e.getCellDimension=()=>{},e.intersectWithLine=(e,t,n,r,o,a,i)=>{},e.evaluatePosition=(e,t,n,r,o,a)=>{jt.vtkErrorMacro("vtkCell.evaluatePosition is not implemented.")}}const xc={bounds:[-1,-1,-1,-1,-1,-1],pointsIds:[]};function Cc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xc,n),jt.obj(e,t),t.points||(t.points=Tc.newInstance()),jt.get(e,t,["points","pointsIds"]),bc(e,t)}var Sc={newInstance:jt.newInstance(Cc,"vtkCell"),extend:Cc};function Ac(e,t){t.classHierarchy.push("vtkCellLinks"),e.buildLinks=n=>{const r=n.getPoints().getNumberOfPoints(),o=n.getNumberOfCells(),a=new Uint32Array(r);if(n.isA("vtkPolyData")){for(let t=0;t<o;++t){const{cellPointIds:r}=n.getCellPoints(t);r.forEach((t=>{e.incrementLinkCount(t)}))}e.allocateLinks(r),t.maxId=r-1;for(let 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let n=t;for(t>=e.array.length&&(n+=e.array.length);n>e.array.length;)e.array.push({ncells:0,cells:null});e.array.length=n}(t,t.maxId+1)},e.reset=()=>{t.maxId=-1},e.deepCopy=e=>{t.array=[...e.array],t.extend=e.extend,t.maxId=e.maxId},e.incrementLinkCount=e=>{++t.array[e].ncells},e.allocateLinks=e=>{for(let n=0;n<e;++n)t.array[n].cells=new Array(t.array[n].ncells)},e.insertCellReference=(e,n,r)=>{t.array[e].cells[n]=r}}const Ic={array:null,maxId:0,extend:0};function wc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ic,n),jt.obj(e,t),Ac(e,t)}var Pc={newInstance:jt.newInstance(wc,"vtkCellLinks"),extend:wc};const Oc=0,Rc=1,Mc=2,Ec=3,Dc=4,Vc=5,Lc=6,Bc=7,Nc=9,_c=21,Fc=41,kc=42,Gc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],Uc={getClassNameFromTypeId:function(e){return e<Gc.length?Gc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Gc.findIndex(e)},isLinear:function(e){return e<_c||e===Fc||e===kc},hasSubCells:function(e){return e===Lc||e===Dc||e===Mc}};function zc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Oc},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let r=0;r<n;++r)if(t===e.getCellType(r))return!0;return!1},e.insertNextType=t=>e.insertNextCell(t,-1),e.getCellType=e=>t.typeArray[e],e.reset=()=>{t.maxId=-1},e.deepCopy=n=>{e.allocate(n.getSize(),n.getExtend()),t.typeArray.set(n.getTypeArray()),t.locationArray.set(n.getLocationArray()),t.maxId=n.getMaxId()}}const Wc={size:0,maxId:-1,extend:1e3};function Hc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wc,n),jt.obj(e,t),jt.get(e,t,["size","maxId","extend"]),jt.getArray(e,t,["typeArray","locationArray"]),zc(e,t)}var jc={newInstance:jt.newInstance(Hc,"vtkCellTypes"),extend:Hc,...Uc},Kc={IntersectionState:{NO_INTERSECTION:0,YES_INTERSECTION:1,ON_LINE:2}};const{IntersectionState:$c}=Kc;function qc(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;const o={t:Number.MIN_VALUE,distance:0},a=[];let i;a[0]=n[0]-t[0],a[1]=n[1]-t[1],a[2]=n[2]-t[2];const s=a[0]*(e[0]-t[0])+a[1]*(e[1]-t[1])+a[2]*(e[2]-t[2]),l=jo(a,a);let c=1e-5*s;return 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u=[],d=[],p=Xc(e,n,l,c,u,d);var f;if(i.t=u[0],i.betweenPoints=(f=i.t)>=0&&f<=1,a[0]=d[0],p===$c.YES_INTERSECTION){for(let t=0;t<3;t++)o[t]=l[t]+a[0]*(c[t]-l[t]),s[t]=e[t]+i.t*(n[t]-e[t]);if(Yo(o,s)<=r*r)return i.intersect=1,i}else{let t;if(i.t<0)return t=qc(e,l,c,o),t.distance<=r*r?(i.t=0,i.intersect=1,i.betweenPoints=!0,i):i;if(i.t>1)return t=qc(n,l,c,o),t.distance<=r*r?(i.t=1,i.intersect=1,i.betweenPoints=!0,i):i;if(a[0]<0)return a[0]=0,t=qc(l,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i;if(a[0]>1)return a[0]=1,t=qc(c,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i}return i},e.evaluateLocation=(e,n,r)=>{const o=[],a=[];t.points.getPoint(0,o),t.points.getPoint(1,a);for(let t=0;t<3;t++)n[t]=o[t]+e[0]*(a[t]-o[t]);r[0]=1-e[0],r[1]=e[0]},e.evaluateOrientation=(e,n,r)=>!!t.orientations&&(function(e,t,n,r){var o,i,s,l,c,u=t[0],d=t[1],p=t[2],f=t[3],g=n[0],m=n[1],h=n[2],v=n[3];(i=u*g+d*m+p*h+f*v)<0&&(i=-i,g=-g,m=-m,h=-h,v=-v),1-i>a?(o=Math.acos(i),s=Math.sin(o),l=Math.sin((1-r)*o)/s,c=Math.sin(r*o)/s):(l=1-r,c=r),e[0]=l*u+c*g,e[1]=l*d+c*m,e[2]=l*p+c*h,e[3]=l*f+c*v}(n,t.orientations[0],t.orientations[1],e[0]),r[0]=1-e[0],r[1]=e[0],!0)}const Qc={orientations:null};function Jc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Qc,n),Sc.extend(e,t,n),jt.setGet(e,t,["orientations"]),Zc(e,t)}var eu={newInstance:jt.newInstance(Jc,"vtkLine"),extend:Jc,...Yc,...Kc};function tu(e,t){t.classHierarchy.push("vtkPointSet"),t.points?t.points=We(t.points):t.points=Tc.newInstance(),e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.getBounds=()=>t.points.getBounds(),e.computeBounds=()=>{e.getBounds()};const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),t.points=Tc.newInstance(),t.points.shallowCopy(e.getPoints())}}const nu={};function ru(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nu,n),ol.extend(e,t,n),jt.setGet(e,t,["points"]),tu(e,t)}var ou={newInstance:jt.newInstance(ru,"vtkPointSet"),extend:ru};function au(e,t,n,r){const o=n[0]-t[0],a=n[1]-t[1],i=n[2]-t[2],s=e[0]-t[0],l=e[1]-t[1],c=e[2]-t[2];r[0]=a*c-i*l,r[1]=i*s-o*c,r[2]=o*l-a*s}function iu(e,t,n,r){au(e,t,n,r);const o=Math.sqrt(r[0]*r[0]+r[1]*r[1]+r[2]*r[2]);0!==o&&(r[0]/=o,r[1]/=o,r[2]/=o)}const su={computeNormalDirection:au,computeNormal:iu,intersectWithTriangle:function(e,t,n,r,o,a){let i=arguments.length>6&&void 0!==arguments[6]?arguments[6]:1e-6,s=!1;const l=[],c=[],u=[],d=[],p=[];iu(e,t,n,d),iu(r,o,a,p);const f=-jo(d,e),g=-jo(p,r),m=[jo(p,e)+g,jo(p,t)+g,jo(p,n)+g];if(m[0]*m[1]>i&&m[0]*m[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible buffer.",!1),e.bind=()=>!!o&&(t.context.bindBuffer(n(r),o),!0),e.release=()=>!!o&&(t.context.bindBuffer(n(r),null),!0),e.releaseGraphicsResources=()=>{null!==o&&(t.context.bindBuffer(n(r),null),t.context.deleteBuffer(o),o=null,t.allocatedGPUMemoryInBytes=0)},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getError=()=>i}const Zu={objectType:Xu.ARRAY_BUFFER,context:null,allocatedGPUMemoryInBytes:0};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),jt.obj(e,t),jt.get(e,t,["_openGLRenderWindow","allocatedGPUMemoryInBytes"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu,...qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkOpenGLCellArrayBufferObject"),e.setType($u.ARRAY_BUFFER),e.createVBO=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * 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vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=mg(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.oglObject?.getHandle()&&m?.hash===g)t.colorTexture=m.oglObject;else{t.colorTexture=Ud.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(n*p*3);if(c===og.NEAREST?(t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST)):(t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR)),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader 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Image;n.crossOrigin&&(o.crossOrigin=n.crossOrigin),o.onload=()=>e(o),o.onerror=r,o.src=t}))}};zm("http",(e=>kh));const{vtkDebugMacro:Gh}=jt;function Uh(e,t){t.classHierarchy.push("vtkCamera");const n=new Float64Array(3),r=new Float64Array([0,0,-1]),o=new Float64Array([0,1,0]),a=g(new Float64Array(16)),i=g(new Float64Array(16)),s=new Float64Array(3),l=new Float64Array(3),c=new Float64Array(3),u=g(new Float64Array(16)),p=g(new Float64Array(16)),f=new Float64Array(3),v=new Float64Array(3);function y(){t.viewPlaneNormal[0]=-t.directionOfProjection[0],t.viewPlaneNormal[1]=-t.directionOfProjection[1],t.viewPlaneNormal[2]=-t.directionOfProjection[2]}e.orthogonalizeViewUp=()=>{const n=e.getViewMatrix();t.viewUp[0]=n[4],t.viewUp[1]=n[5],t.viewUp[2]=n[6],e.modified()},e.setPosition=(n,r,o)=>{n===t.position[0]&&r===t.position[1]&&o===t.position[2]||(t.position[0]=n,t.position[1]=r,t.position[2]=o,e.computeDistance(),e.modified())},e.setFocalPoint=(n,r,o)=>{n===t.focalPoint[0]&&r===t.focalPoint[1]&&o===t.focalPoint[2]||(t.focalPoint[0]=n,t.focalPoint[1]=r,t.focalPoint[2]=o,e.computeDistance(),e.modified())},e.setDistance=n=>{if(t.distance===n)return;t.distance=n,t.distance<1e-20&&(t.distance=1e-20,Gh("Distance is set to minimum."));const r=t.directionOfProjection;t.focalPoint[0]=t.position[0]+r[0]*t.distance,t.focalPoint[1]=t.position[1]+r[1]*t.distance,t.focalPoint[2]=t.position[2]+r[2]*t.distance,e.modified()},e.computeDistance=()=>{const e=t.focalPoint[0]-t.position[0],n=t.focalPoint[1]-t.position[1],r=t.focalPoint[2]-t.position[2];if(t.distance=Math.sqrt(e*e+n*n+r*r),t.distance<1e-20){t.distance=1e-20,Gh("Distance is set to minimum.");const e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let 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Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId 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pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an 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t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const 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o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const Mv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Ev(e,t){let n=arguments.length>2&&void 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let 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n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = 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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],D=r[2],E=Math.hypot(R,M,D);return E<a?null:(R*=E=1/E,M*=E,D*=E,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+D*o,C=D*R*s-M*o,S=R*M*s-D*o,A=M*M*s+i,I=D*M*s+R*o,w=R*D*s+M*o,P=M*D*s-R*o,O=D*D*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),E(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,D=(1-(p+v))*C,E=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=D,e[6]=E,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+D*I+L*w),e[14]=n[2]+w-(R*A+E*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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Qi(e,t){t.classHierarchy.push("vtkProp3D"),e.addPosition=n=>{t.position=t.position.map(((e,t)=>e+n[t])),e.modified()},e.getOrientationWXYZ=()=>{const e=Ui();N(e,t.rotation);const n=new Float64Array(3),r=function(e,t){var n=2*Math.acos(t[3]),r=Math.sin(n/2);return r>a?(e[0]=t[0]/r,e[1]=t[1]/r,e[2]=t[2]/r):(e[0]=1,e[1]=0,e[2]=0),n}(n,e);return[Ro(r),n[0],n[1],n[2]]},e.rotateX=n=>{0!==n&&(S(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateY=n=>{0!==n&&(A(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateZ=n=>{0!==n&&(I(t.rotation,t.rotation,Oo(n)),e.modified())},e.rotateWXYZ=(n,r,o,a)=>{if(0===n||0===r&&0===o&&0===a)return;const i=Oo(n),s=Ui();zi(s,[r,o,a],i);const l=new 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Es,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&t.getDataType()===Tl.UNSIGNED_CHAR||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Es.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Es.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Es.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function Dl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var El={newInstance:jt.newInstance(Dl,"vtkLookupTable"),extend:Dl};let Vl=1,Ll=0;const Bl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function Nl(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;Ll=e}var _l={getResolveCoincidentTopologyAsString:function(){return Bl[Ll]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Vl},getResolveCoincidentTopology:function(){return Ll},setResolveCoincidentTopology:Nl,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){Vl=e},setResolveCoincidentTopologyToDefault:function(){Nl(0)},setResolveCoincidentTopologyToOff:function(){Nl(0)},setResolveCoincidentTopologyToPolygonOffset:function(){Nl(1)}};function Fl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=(e,r)=>{t[n.key]={factor:e,offset:r}}}))}const kl=["Polygon","Line","Point"],Gl={};Fl(Gl,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},kl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var Ul={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(_l).forEach((t=>{e[t]=_l[t]})),Object.keys(Gl).forEach((t=>{e[t]=Gl[t]})),Fl(e,t.topologyOffset,kl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Gl.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Gl.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Gl,otherStaticMethods:_l,CATEGORIES:kl};const zl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Wl={PassTypes:zl};const{FieldAssociations:Hl}=ol,{staticOffsetAPI:jl,otherStaticMethods:Kl}=Ul,{ColorMode:$l,ScalarMode:ql,GetArray:Xl}=hl,{VectorMode:Yl}=ml,{VtkDataTypes:Zl}=Es;function Ql(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function Jl(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString($l,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(ql,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===ql.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===ql.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===ql.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===ql.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===ql.USE_FIELD_DATA){const t=e.getFieldData();i=r===Xl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==a){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(n))e.mapScalarsToTexture(o,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},e.scalarToTextureCoordinate=(e,t,n)=>{let r=.5,o=1;return _a(e)||(o=.49,r=(e-t)*n,r>1e3?r=1e3:r<-1e3&&(r=-1e3)),{texCoordS:r,texCoordT:o}},e.createColorTextureCoordinates=(t,n,r,o,a,i,s,l,c)=>{const u=(i[1]-i[0])/l,d=[];d[0]=i[0]-u,d[1]=i[1]+u;const p=1/(d[1]-d[0]),f=n.getData(),g=t.getData();let m=0,h=0;if(a<0||a>=o)for(let t=0;t<r;++t){let t=0;for(let e=0;e<o;++e)t+=g[m]*g[m],m++;let n=Math.sqrt(t);c&&(n=El.applyLogScale(n,s,i));const r=e.scalarToTextureCoordinate(n,d[0],p);f[h]=r.texCoordS,f[h+1]=r.texCoordT,h+=2}else{m+=a;for(let t=0;t<r;++t){let t=g[m];c&&(t=El.applyLogScale(t,s,i));const n=e.scalarToTextureCoordinate(t,d[0],p);f[h]=n.texCoordS,f[h+1]=n.texCoordT,h+=2,m+=o}}},e.mapScalarsToTexture=(n,r)=>{const o=t.lookupTable.getRange(),a=t.lookupTable.usingLogScale();a&&El.getLogRange(o,o);const i=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==r){t.lookupTable.setAlpha(r),t.colorTextureMap=null,t.lookupTable.build();let e=t.lookupTable.getNumberOfAvailableColors();e>4094&&(e=4094),e<64&&(e=64),e+=2;const n=(o[1]-o[0])/(e-2),s=new Float64Array(2*e);for(let t=0;t<e;++t)s[t]=o[0]+t*n-n/2,a&&(s[t]=10**s[t]);for(let t=0;t<e;++t)s[t+e]=NaN;t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setExtent(0,e-1,0,1,0,0);const l=Es.newInstance({numberOfComponents:1,values:s});t.colorTextureMap.getPointData().setScalars(t.lookupTable.mapScalars(l,t.colorMode,0)),t.lookupTable.setAlpha(i)}if(!t.colorCoordinates||e.getMTime()>t.colorCoordinates.getMTime()||e.getInputData(0).getMTime()>t.colorCoordinates.getMTime()||t.lookupTable.getMTime()>t.colorCoordinates.getMTime()){t.colorCoordinates=null;const r=n.getNumberOfComponents(),i=n.getNumberOfTuples();t.colorCoordinates=Es.newInstance({numberOfComponents:2,values:new Float32Array(2*i)});let s=t.lookupTable.getVectorComponent();t.lookupTable.getVectorMode()===Yl.MAGNITUDE&&n.getNumberOfComponents()>1&&(s=-1),e.createColorTextureCoordinates(n,t.colorCoordinates,i,r,s,o,t.lookupTable.getRange(),t.colorTextureMap.getPointData().getScalars().getNumberOfTuples()/2-2,a)}},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=n=>{if(!t.interpolateScalarsBeforeMapping)return!1;if(t.lookupTable&&t.lookupTable.getIndexedLookup())return!1;const r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName),o=r.scalars;return!(!o||r.cellFlag||t.colorMode===$l.DEFAULT&&o.getDataType()===Zl.UNSIGNED_CHAR||t.colorMode===$l.DIRECT_SCALARS)},e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Ql("AcquireInvertibleLookupTable"),e.valueToColor=Ql("ValueToColor"),e.colorToValue=Ql("ColorToValue"),e.useInvertibleColorFor=Ql("UseInvertibleColorFor"),e.clearInvertibleColor=Ql("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(zl.ID_LOW24),o=e.getRawPixelBuffer(zl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===Hl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===Hl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===zl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(zl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===zl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(zl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const ec={colorMapColors:null,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function tc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ec,n),Fs(e,t,n),jt.get(e,t,["colorCoordinates","colorMapColors","colorTextureMap","selectionWebGLIdsToVTKIds"]),jt.setGet(e,t,["colorByArrayName","arrayAccessMode","colorMode","fieldDataTupleId","interpolateScalarsBeforeMapping","lookupTable","populateSelectionSettings","renderTime","scalarMode","scalarVisibility","static","useLookupTableScalarRange","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),Ul.implementCoincidentTopologyMethods(e,t),Jl(e,t)}var nc={newInstance:jt.newInstance(tc,"vtkMapper"),extend:tc,...jl,...Kl,...hl};function rc(e){let t=0;return e.filter(((e,n)=>n===t&&(t+=e+1,!0)))}function oc(e){let t=0;for(let n=0;n<e.length;)n+=e[n]+1,t++;return t}const ac={extractCellSizes:rc,getNumberOfCells:oc};function ic(e,t){t.classHierarchy.push("vtkCellArray");const n={...e};e.getNumberOfCells=n=>void 0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=oc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=rc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function sc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function lc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Es.extend(e,t,sc(n)),ic(e,t)}var cc={newInstance:jt.newInstance(lc,"vtkCellArray"),extend:lc,...ac};const{vtkErrorMacro:uc}=jt,dc=[1,-1,1,-1,1,-1];function pc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return uc(`getBounds called on an array with components of\n        ${e.getNumberOfComponents()}`),dc;const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);return t.bounds[2]=r[0],t.bounds[3]=r[1],t.bounds[4]=0,t.bounds[5]=0,t.bounds},e.computeBounds=e.getBounds,e.setNumberOfComponents(t.numberOfComponents<2?3:t.numberOfComponents)}const fc={empty:!0,numberOfComponents:3,dataType:ys.FLOAT,bounds:[1,-1,1,-1,1,-1]};function gc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fc,n),Es.extend(e,t,n),pc(e,t)}var mc={newInstance:jt.newInstance(gc,"vtkPoints"),extend:gc};function hc(e,t){t.classHierarchy.push("vtkCell"),e.initialize=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n){t.pointsIds=n;let r=t.points.getData();r.length!==3*t.pointsIds.length&&(r=jt.newTypedArray(e.getDataType(),3*t.pointsIds.length));const o=e.getData();t.pointsIds.forEach(((e,t)=>{let n=3*e,a=3*t;r[a]=o[n],r[++a]=o[++n],r[++a]=o[++n]})),t.points.setData(r)}else{t.points=e,t.pointsIds=new 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let 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Ac=0,Ic=1,wc=2,Pc=3,Oc=4,Rc=5,Mc=6,Dc=7,Ec=9,Vc=21,Lc=41,Bc=42,Nc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],_c={getClassNameFromTypeId:function(e){return e<Nc.length?Nc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Nc.findIndex(e)},isLinear:function(e){return e<Vc||e===Lc||e===Bc},hasSubCells:function(e){return e===Mc||e===Oc||e===wc}};function Fc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Ac},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let 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e=P[2];P[2]=P[D],P[D]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function ou(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(nu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=Yc.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),tu(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=Yc.distanceToLine(e,l,u,t,S),x=Yc.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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su={newInstance:jt.newInstance(iu,"vtkTriangle"),extend:iu,...ru};const lu=["verts","lines","polys","strips"],{vtkWarningMacro:cu}=jt,uu={[Pc]:Yc,[Oc]:Yc,[Rc]:su};function du(e,t){t.classHierarchy.push("vtkPolyData"),lu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=cc.newInstance()})),e.getNumberOfCells=()=>lu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),lu.forEach((n=>{t[n]=cc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?wc:Ic,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Oc:Pc,1===t&&cu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. 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o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||uu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const pu={};function fu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pu,n),eu.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),du(e,t)}var gu={newInstance:jt.newInstance(fu,"vtkPolyData"),extend:fu};function mu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const hu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function vu(e,t){let n=arguments.length>2&&void 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Tu={newInstance:jt.newInstance(vu,"vtkTexture"),extend:vu,...yu};const bu=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],xu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],Cu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Su=[0,1,0,1,0,1,0,1,2,2,2,2],Au=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Iu=new Float64Array(3),wu=new Float64Array(3),Pu=new Float64Array(3),Ou=new Float64Array(3),Ru=new Float64Array(3),Mu=new Float64Array(3),Du=new Float64Array(16);function Eu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function Vu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],Vn(Pu,Iu,r),Pu[0]+=.1,Vn(wu,Pu,o),Pn(Ru,wu,Iu),Pu[0]-=.1,Pu[1]+=.1,Vn(wu,Pu,o),Pn(Mu,wu,Iu);for(let e=0;e<3;e++)Ru[e]/=.05*u[0],Mu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Iu[0]=c[3*n],Iu[1]=c[3*n+1],Iu[2]=c[3*n+2],a[0]<-.5?On(Pu,Ru,a[0]*i-l.width):a[0]>.5?On(Pu,Ru,a[0]*i):On(Pu,Ru,a[0]*i-l.width/2),wn(Iu,Iu,Pu),On(Pu,Mu,a[1]*i-l.height/2),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Pu,Ru,l.width),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Pu,Mu,l.height),wn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Pu,Ru,l.width),Pn(Iu,Iu,Pu),s.points[3*d]=Iu[0],s.points[3*d+1]=Iu[1],s.points[3*d+2]=Iu[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Du,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Iu[0]=f[3*u],Iu[1]=f[3*u+1],Iu[2]=f[3*u+2],Vn(Pu,Iu,n),Iu[0]=f[3*u+3],Iu[1]=f[3*u+4],Iu[2]=f[3*u+5],Vn(Ou,Iu,n),Pn(Pu,Pu,Ou);const r=[Pu[0],Pu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Du,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const Lu=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Vu(e,t)}),"vtkCubeAxesActorHelper");function Bu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Iu[s]=t.dataBounds[a]-.1*r*bu[a][s],Iu[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Iu[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Pu,Iu,e),Iu[s]=t.dataBounds[a],Vn(Ou,Iu,e),Pn(Pu,Ou,Pu),Mn(Pu,Pu),i=Pu[2]>o,t.camera.getParallelProjection()||(Mn(Ou,Ou),i=Dn(Ou,Pu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Au[t][0]].length+2*r[Au[t][1]].length,a+=r[Au[t][0]].length+r[Au[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let 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e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[xu[e][t]]++;const a=[],i=[];for(let e=0;e<3;e++){const n=go().domain([t.dataBounds[2*e],t.dataBounds[2*e+1]]);a[e]=n.ticks(5);const r=n.tickFormat(5);i[e]=a[e].map(r)}e.updatePolyData(r,n,a),e.updateTextData(r,n,a,i),(o||t.forceUpdate)&&e.updateTextureAtlas(i)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Eu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Eu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Eu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Nu(e){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:12,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},...e}}function _u(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nu(n)),hs.extend(e,t,n),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Tu.newInstance(),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=nc.newInstance(),t.polyData=gu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=gu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Bu(e,t)}var Fu={newInstance:jt.newInstance(_u,"vtkCubeAxesActor"),extend:_u,newCubeAxesActorHelper:Lu};function ku(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Gu={};const Uu=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gu,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Fu.newCubeAxesActorHelper(),ku(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Uu);const zu={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var Wu={ObjectType:zu};const{ObjectType:Hu}=Wu;function ju(e,t){function n(e){switch(e){case Hu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Hu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Hu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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L=a.vertexOffset;f=function(e){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;)C(A[e],A,e+1),e+=A[e]+1,T++;return t.elementCount=w,e.upload(O,zu.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,zu.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):Xu("Wrong type for coordScale, expected vec3 or null"):Xu("Wrong type for coordShift, expected vec3 or null")}}const Zu={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),qu.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ed(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const nd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),td(e,t)}var od={newInstance:jt.newInstance(rd,"vtkShader"),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ad(`Links failed: ${t.error}`),0):(ad(t.error),0):(ad(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.fragmentShader.getError()),0):(ad(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ad(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ad(`Error linking 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No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . 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No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ad("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ad("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const sd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=od.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=od.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=od.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShaderProgram"),extend:ld,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function ud(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==zu.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const dd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function pd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),ud(e,t)}var fd={newInstance:jt.newInstance(pd,"vtkOpenGLVertexArrayObject"),extend:pd};const gd={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function md(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===gd.Points?t.context.POINTS:e===rs.WIREFRAME||n===gd.Lines||n===gd.TrisEdges||n===gd.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===gd.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=cd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===gd.Points?2:t.primitiveType===gd.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const hd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=cd.newInstance(),t.VAO=fd.newInstance(),t.CABO=Ju.newInstance(),md(e,t)}var yd={newInstance:jt.newInstance(vd),extend:vd,primTypes:gd};const Td={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},bd={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var xd={Wrap:Td,Filter:bd};const Cd=new Float32Array(1),Sd=new Int32Array(Cd.buffer);var Ad={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Cd[0]=e;const t=Sd[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Id,Filter:wd}=xd,{VtkDataTypes:Pd}=Es,{vtkDebugMacro:Od,vtkErrorMacro:Rd,vtkWarningMacro:Md}=Kt,{toHalf:Dd}=Ad;function Ed(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Pd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Pd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Dd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Ad.toHalf(Ad.fromHalf(e[u][l+s+t])*h*(1-v)+Ad.fromHalf(e[u][l+p+t])*h*v+Ad.fromHalf(e[u][c+s+t])*m*(1-v)+Ad.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Pd.UNSIGNED_SHORT&&e!==Pd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent()),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(wd.LINEAR),e.setMagnificationFilter(wd.LINEAR)):(e.setMinificationFilter(wd.NEAREST),e.setMagnificationFilter(wd.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Id.REPEAT),e.setWrapS(Id.REPEAT),e.setWrapT(Id.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Pd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(wd.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Md("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Od(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Md("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Od("Unsupported internal texture type!"),Od(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Pd.SHORT:case t.useHalfFloat&&Pd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Pd.FLOAT:case Pd.VOID:default:return t.context.FLOAT}switch(e){case Pd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Pd.FLOAT:case Pd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case wd.NEAREST:return t.context.NEAREST;case wd.LINEAR:return t.context.LINEAR;case wd.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case wd.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case wd.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case wd.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Id.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Id.REPEAT:return t.context.REPEAT;case Id.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Pd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Rd("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Pd.UNSIGNED_CHAR),e.getInternalFormat(Pd.UNSIGNED_CHAR,4),e.getFormat(Pd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Pd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Pd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Es.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Es.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Pd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Pd.FLOAT||t.useHalfFloat&&(u===Pd.SHORT||u===Pd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Pd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Pd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Pd.UNSIGNED_CHAR;if(u===Pd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Pd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Pd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Rd("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Pd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Pd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Vd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Id.CLAMP_TO_EDGE,wrapT:Id.CLAMP_TO_EDGE,wrapR:Id.CLAMP_TO_EDGE,minificationFilter:wd.NEAREST,magnificationFilter:wd.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function Ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),Ed(e,t)}const Bd=Et(Ld,"vtkOpenGLTexture");var Nd={newInstance:Bd,extend:Ld,...xd};rn("vtkTexture",Bd);var _d="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Fd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",kd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=cd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=cd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=cd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},Gd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=cd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Ud}=ol,{primTypes:zd}=yd,{Representation:Wd,Shading:Hd}=ds,{ScalarMode:jd}=nc,{Filter:Kd,Wrap:$d}=Nd,{vtkErrorMacro:qd}=Kt,Xd={type:"StartEvent"},Yd={type:"EndEvent"},{CoordinateSystem:Zd}=Zi;function Qd(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=zd.Start;e<zd.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=cd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?t.renderable.getInterpolateScalarsBeforeMapping()&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=cd.substitute(i,"//VTK::Color::Impl",c).result):i=cd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=cd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=cd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=cd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=cd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=cd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:qd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=cd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=cd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=cd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=cd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=cd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=cd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=cd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=cd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=cd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=cd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=cd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=cd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=cd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=cd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=cd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()||o.getEdgeVisibility()&&o.getRepresentation()===Wd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===zd.Points||o.getRepresentation()===Wd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===zd.Lines||o.getRepresentation()===Wd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==zd.Tris&&e!==zd.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==zd.TrisEdges&&e!==zd.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=cd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==zl.ID_LOW24&&t.lastSelectionState!==zl.ID_HIGH24||(a=cd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=cd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=cd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=cd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case zl.ID_LOW24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case zl.ID_HIGH24:o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=cd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=cd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Hd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||qd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||qd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||qd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||qd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===Zd.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():zl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===Wd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Ud.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===zl.ID_LOW24||t.lastSelectionState===zl.ID_HIGH24);for(let i=zd.Start;i<zd.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===zd.TrisEdges||i===zd.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(Xd),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(Yd),!t.currentInput)return void qd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==jd.USE_CELL_DATA&&i!==jd.USE_CELL_FIELD_DATA&&i!==jd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===jd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Hd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();if(t.openGLActor.getActiveTextures()||(c=null),t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),t.internalColorTexture||(t.internalColorTexture=Nd.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Kd.NEAREST),e.setMagnificationFilter(Kd.NEAREST),e.setWrapS($d.CLAMP_TO_EDGE),e.setWrapT($d.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const u=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==u){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],d=r.getProperty().getEdgeVisibility()&&l===Wd.SURFACE;for(let e=zd.Start;e<zd.End;e++)e!==zd.TrisEdges&&e!==zd.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):d?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,Wd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Jd={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:zl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ep(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jd,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=zd,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=zd.Start;e<zd.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),Qd(e,t)}const tp=Et(ep,"vtkOpenGLPolyDataMapper");var np={newInstance:tp,extend:ep};rn("vtkMapper",tp);const{ColorMode:rp,ScalarMode:op,GetArray:ap}=hl;function ip(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=El.newInstance()},e.getColorModeAsString=()=>jt.enumToString(rp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(op,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===op.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===op.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===op.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===op.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===op.USE_FIELD_DATA){const t=e.getFieldData();i=r===ap.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const sp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function lp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),ip(e,t)}var cp={newInstance:jt.newInstance(lp,"vtkMapper2D"),extend:lp};const up=1,{primTypes:dp}=yd,{ScalarMode:pp}=cp,{vtkErrorMacro:fp}=Kt,gp={type:"StartEvent"},mp={type:"EndEvent"};function hp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=dp.Start;e<dp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(gp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(mp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==pp.USE_CELL_DATA&&a!==pp.USE_CELL_FIELD_DATA&&a!==pp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===pp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=mc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[dp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[dp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[dp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[dp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=dp.Start;a<dp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=cd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=cd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=cd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=cd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=cd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=cd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=cd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=cd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=cd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=cd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=cd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=cd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=cd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=cd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=cd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=cd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=cd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||fp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||fp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||fp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===up?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const vp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const yp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vp,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.primitives=[],t.primTypes=dp,t.tmpMat4=g(new Float64Array(16));for(let e=dp.Start;e<dp.End;e++)t.primitives[e]=yd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),hp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",yp);const{VectorMode:Tp}=Pl;function bp(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function xp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Cp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Sp(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;bp(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,bp(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{bp(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Es.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Tp.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Es.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Ap=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=nc.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=gu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=gu.newInstance(),t.tmMapper=nc.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Tu.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Sp(e,t)}),"vtkScalarBarActorHelper");function Ip(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(xp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Cp())}}function wp(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Pp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wp(n)),t.autoLayout||(t.autoLayout=xp(0,t)),t.generateTicks||(t.generateTicks=Cp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Ip(e,t)}var Op={newInstance:jt.newInstance(Pp,"vtkScalarBarActor"),extend:Pp,newScalarBarActorHelper:Ap};function Rp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Mp={};const Dp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Mp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Op.newScalarBarActorHelper(),Rp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Dp);const{vtkErrorMacro:Ep}=Kt;function Vp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Es.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ep("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||Ep("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const Lp={context:null};const Bp=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lp,n),Zt.extend(e,t,n),t.openGLTexture=Nd.newInstance(),t.tris=yd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Vp(e,t)}));rn("vtkSkybox",Bp);const{FieldAssociations:Np}=ol;function _p(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Fp={fieldAssociation:Np.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function kp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),_p(e,t)}var Gp={newInstance:jt.newInstance(kp,"vtkHardwareSelector"),extend:kp};function Up(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>{const e=[0,0];return null!==t.glFramebuffer&&(e[0]=t.glFramebuffer.width,e[1]=t.glFramebuffer.height),e},e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Nd.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(bd.LINEAR),r.setMagnificationFilter(bd.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const zp={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function Wp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zp,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Up(e,t)}var Hp={newInstance:Et(Wp,"vtkFramebuffer"),extend:Wp};function jp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Kp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function $p(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),jp(e,t)}var qp={newInstance:jt.newInstance($p,"vtkSelectionNode"),extend:$p,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:Xp}=Wl,{SelectionContent:Yp,SelectionField:Zp}=qp,{FieldAssociations:Qp}=ol,{vtkErrorMacro:Jp}=jt;function ef(e){return`${e.propID} ${e.compositeID}`}function tf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function nf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function rf(e,t){let n=t;return n<<=24,n|=e,n}function of(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=nf(n[0],n[1],e.pixBuffer[Xp.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=nf(n[0],n[1],e.pixBuffer[Xp.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[Xp.ID_LOW24]&&0===tf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area))return a;const s=nf(n[0],n[1],e.pixBuffer[Xp.ID_LOW24],e.area),l=nf(n[0],n[1],e.pixBuffer[Xp.ID_HIGH24],e.area);return a.attributeID=rf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=of(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=of(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=of(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=of(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function af(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=qp.newInstance();switch(l.setContentType(Yp.INDICES),e){case Qp.FIELD_ASSOCIATION_CELLS:l.setFieldType(Zp.CELL);break;case Qp.FIELD_ASSOCIATION_POINTS:l.setFieldType(Zp.POINT);break;default:Jp("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function sf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=of(e,[n,t],0,u);if(r&&r.valid){const t=ef(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return af(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function lf(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e[0]!==n[0]||e[1]!==n[1]?(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer()):t.framebuffer.bind()}else t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return sf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return Jp("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=Xp.MIN_KNOWN_PASS;t.currentPass<=Xp.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===Xp.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===Xp.ID_HIGH24){if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===Qp.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===Xp.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=nf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===Xp.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===Xp.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(Xp,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=nf(e[0],e[1],t.pixBuffer[Xp.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=nf(e[0],e[1],t.pixBuffer[Xp.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[Xp.ID_LOW24]&&0===tf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area))return a;const s=nf(e[0],e[1],t.pixBuffer[Xp.ID_LOW24],t.area),l=nf(e[0],e[1],t.pixBuffer[Xp.ID_HIGH24],t.area);return a.attributeID=rf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=ef(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return af(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const cf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cf,n),Gp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),lf(e,t)}var df={newInstance:jt.newInstance(uf,"vtkOpenGLHardwareSelector"),extend:uf,...Wl};const{vtkErrorMacro:pf}=Kt,{Representation:ff}=ds,{ObjectType:gf}=qu,{PassTypes:mf}=df,hf={type:"StartEvent"},vf={type:"EndEvent"};function yf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(hf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(vf),!t.currentInput)return void pf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=cd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=cd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=cd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=cd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=cd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=cd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=cd.substitute(o,"//VTK::Color::Impl",s).result,o=cd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=cd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=cd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=cd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=cd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=cd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=cd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===ff.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===mf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||pf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||pf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||pf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=qu.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=qu.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=qu.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=qu.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,gf.ARRAY_BUFFER),t.normalBuffer.upload(n,gf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),gf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,gf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Tf={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const bf=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tf,n),np.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),yf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",bf);const{vtkErrorMacro:xf}=jt;class Cf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Sf(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void xf("Invalid or missing input");const r=gu.newInstance();r.shallowCopy(n);const o=new Cf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(cc.newInstance({values:c,name:"faces"})),t[0]=r}}const Af={};function If(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),jt.obj(e,t),jt.algo(e,t,1,1),Sf(e,t)}var wf={newInstance:jt.newInstance(If,"vtkClosedPolyLineToSurfaceFilter"),extend:If};const{vtkErrorMacro:Pf}=Kt;function Of(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Pf("Invalid or missing input");if(!t.cutFunction)return void Pf("Missing cut function");const o=gu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const 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o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Rf={cutFunction:null,cutScalars:null,cutValue:0};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Of(e,t)}var Df={newInstance:Et(Mf,"vtkCutter"),extend:Mf};const Ef=e=>e,Vf=1e-6;class Lf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:Ef}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=Dn(n,r);return a>=1||(En(this.tmp,n,r),Sn(this.tmp)<Vf&&(En(this.tmp,[1,0,0],e),Sn(this.tmp)<Vf&&En(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Bf=function(){return new Lf(!0)},Nf=function(){return new Lf(!1)};const _f=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Ff=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function kf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=gu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Es.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Es.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Bf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Nf().translate(...t.center).apply(o),t.matrix){Nf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Nf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const Gf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Uf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=cc.newInstance({values:Uint16Array.from(Ff)}),t._lineCells=cc.newInstance({values:Uint16Array.from(_f)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),kf(e,t)}var zf={newInstance:jt.newInstance(Uf,"vtkCubeSource"),extend:Uf};const{vtkErrorMacro:Wf}=jt;function Hf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void Wf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):Wf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const jf={};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=zf.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkImageDataOutlineFilter"),extend:Kf};let qf;function Xf(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>qf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const Yf={preMultiplyFlag:!1,matrix:[...vo]};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),Xf(e,t)}qf=jt.newInstance(Zf,"vtkTransform");var Qf={newInstance:qf,extend:Zf};const Jf={NEAREST:0,LINEAR:1};var eg={InterpolationType:Jf};const{vtkErrorMacro:tg}=Kt;function ng(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function rg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function og(e,t){t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const n=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):tg("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Jf.NEAREST?(t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.openGLTexture.setMinificationFilter(bd.LINEAR),t.openGLTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime(),e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(!r)return;const o=r.getPointData()?.getScalars();if(!o)return;t._scalars!==o&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=o);const a=o.getNumberOfComponents();let i=`${r.getMTime()}A${o.getMTime()}`;const s=t._openGLRenderWindow.getGraphicsResourceForObject(o);if(s?.vtkObj&&s?.hash===i&&t.openGLTextureString===i)t.openGLTexture=s.vtkObj,t.openGLTextureString=s.hash;else{t.openGLTexture||(t.openGLTexture=Nd.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const e=r.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],o),t.openGLTextureString=i,o&&t._openGLRenderWindow.setGraphicsResourceForObject(o,t.openGLTexture,t.openGLTextureString)}const l=n.getProperty(),c=l.getIndependentComponents(),u=c?a:1,d=c?2*u:1,p=l.getRGBTransferFunction();i=ng(l,p,u);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===i&&t.colorTextureString===i)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(e*d*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),p){const r=new Float32Array(3*e);for(let t=0;t<u;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),c)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=i,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}const g=l.getPiecewiseFunction();i=ng(l,g,u);const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);if(m?.vtkObj&&m?.hash===i&&t.pwfTextureString===i)t.pwfTexture=m.vtkObj,t.pwfTextureString=m.hash;else{const e=1024,n=e*d,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),g){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<u;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=i,g&&t._openGLRenderWindow.setGraphicsResourceForObject(g,t.pwfTexture,t.pwfTextureString)}const h=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==h){const e=Es.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Es.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:tg("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=h,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||tg("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",rg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",rg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=cd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=cd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=cd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=cd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:tg("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=cd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:tg("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=cd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=cd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=cd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`));const t=ue(n?.getDirection());ge(t,t);const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getDimensions(),l=[0,i[0]-1,0,i[1]-1,0,i[2]-1];let c=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[c+a]=r[a],e[c+o[0]]=l[2*o[0]+n],e[c+o[1]]=l[2*o[1]+t],c+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const u=new Uint16Array(8);u[0]=3,u[1]=0,u[2]=1,u[3]=3,u[4]=3,u[5]=0,u[6]=3,u[7]=2;const d=s.getNormal();za.normalize(d);const p=new Float32Array(12);for(let e=0;e<4;++e)p[3*e]=d[0],p[3*e+1]=d[1],p[3*e+2]=d[2];t.resliceGeom||(t.resliceGeom=gu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(u,1);const f=Es.newInstance({numberOfComponents:3,values:p,name:"Normals"});t.resliceGeom.getPointData().setNormals(f)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=gu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Es.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else tg("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)}}const ag={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,openGLTextureString:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1,_scalars:null};const ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ag,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=$f.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=gu.newInstance(),t.cutter=Df.newInstance(),t.lineToSurfaceFilter=wf.newInstance(),t.transform=Qf.newInstance(),bt(e,t,["openGLTexture"]),og(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",ig);var sg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:lg}=Kt,{SlicingMode:cg}=sg;function ug(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function dg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function pg(e,t){t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=n=>{if(n){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(n.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=cd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=cd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:lg("Unsupported number of independent coordinates.")}}if(i=cd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=cd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=cd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=cd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:lg("Unsupported number of independent coordinates.")}i=cd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=cd.substitute(i,"//VTK::TCoord::Impl",[...dg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); \n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (actualThickness == 0) {\n                    gl_FragData[0] = vec4(0.0, 0.0, 1.0, 1.0);\n                    return;\n                  }\n                  if (opacityToUse > 0.01) {\n                    for (int i = -actualThickness; i <= actualThickness; i++) {\n                      for (int j = -actualThickness; j <= actualThickness; j++) {\n                        if (i == 0 || j == 0) {\n                          continue;\n                        }\n                        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                          gl_FragCoord.y + float(j),\n                          gl_FragCoord.z, gl_FragCoord.w);\n                        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                        float value = texture2D(texture1, neighborPosIS.xy).r;\n                        if (value != centerValue) {\n                          pixelOnBorder = true;\n                          break;\n                        }\n                      }\n                      if (pixelOnBorder == true) {\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      gl_FragData[0] = vec4(tColor, outlineOpacity);\n                    }\n                    else {\n                      gl_FragData[0] = vec4(tColor, opacityToUse);\n                    }\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=cd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||lg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||lg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):lg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=ug(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.vtkObj&&m?.hash===g&&t.colorTextureString===g)t.colorTexture=m.vtkObj,t.colorTextureString=m.hash;else{const e=1024,n=new Uint8ClampedArray(e*p*3);if(t.colorTexture||(t.colorTexture=Nd.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST)):(t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR)),f){const r=new Float32Array(3*e);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],e,r,1),u)for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o];else for(let o=0;o<3*e;o++)n[t*e*6+o]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,p,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=g,f&&t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,t.colorTextureString)}const h=l.getPiecewiseFunction(),v=ug(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.vtkObj&&y?.hash===v&&t.pwfTextureString===v)t.pwfTexture=y.vtkObj,t.pwfTextureString=y.hash;else{const e=1024,n=e*p,r=new Uint8ClampedArray(n);if(t.pwfTexture||(t.pwfTexture=Nd.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),c===Jf.NEAREST?(t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR)),h){const r=new Float32Array(n),o=new Float32Array(e);for(let t=0;t<d;++t){const n=l.getPiecewiseFunction(t);if(null===n)r.fill(1);else{const a=n.getRange();if(n.getTable(a[0],a[1],e,o,1),u)for(let n=0;n<e;n++)r[t*e*2+n]=o[n],r[t*e*2+n+e]=o[n];else for(let n=0;n<e;n++)r[t*e*2+n]=o[n]}}t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(e,p,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=v,h&&t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,t.pwfTextureString)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===cg.I&&(S=x-C[0]),T===cg.J&&(S=x-C[2]),T!==cg.K&&T!==cg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Nd.newInstance({resizable:!0}),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow)),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===Jf.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.NEAREST)):t.openGLTexture.setMinificationFilter(bd.NEAREST),t.openGLTexture.setMagnificationFilter(bd.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(bd.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(bd.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(bd.LINEAR)),t.openGLTexture.setWrapS(Td.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Td.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[cg.X,cg.Y,cg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===cg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===cg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===cg.K||T===cg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):lg("Reformat slicing not yet supported.");const m=t._openGLRenderWindow.getGraphicsResourceForObject(g);m?.vtkObj?(t.openGLTexture=m.vtkObj,t.VBOBuildString=m.hash):(t._scalars!==g&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=g),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t._openGLRenderWindow.setGraphicsResourceForObject(g,t.openGLTexture,t.VBOBuildString)),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const h=Es.newInstance({numberOfComponents:3,values:r});h.setName("points");const v=Es.newInstance({numberOfComponents:2,values:l});v.setName("tcoords");const y=new Uint16Array(8);y[0]=3,y[1]=0,y[2]=1,y[3]=3,y[4]=3,y[5]=0,y[6]=3,y[7]=2;const C=Es.newInstance({numberOfComponents:1,values:y});t.tris.getCABO().createVBO(C,"polys",rs.SURFACE,{points:h,tcoords:v,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=e=>{t.labelOutlineThicknessTexture||(t.labelOutlineThicknessTexture=Nd.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow));const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]}}const fg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,_scalars:null};const gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fg,n),Zt.extend(e,t,n),kd(e,t,n),Gd(e,t,n),t.tris=yd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),pg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",gg);const{vtkErrorMacro:mg}=jt;function hg(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function vg(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===Jf.NEAREST?(t.volumeTexture.setMinificationFilter(bd.NEAREST),t.volumeTexture.setMagnificationFilter(bd.NEAREST),t.colorTexture.setMinificationFilter(bd.NEAREST),t.colorTexture.setMagnificationFilter(bd.NEAREST),t.pwfTexture.setMinificationFilter(bd.NEAREST),t.pwfTexture.setMagnificationFilter(bd.NEAREST)):(t.volumeTexture.setMinificationFilter(bd.LINEAR),t.volumeTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.pwfTexture.setMinificationFilter(bd.LINEAR),t.pwfTexture.setMagnificationFilter(bd.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=hg(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=hg(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Es.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Es.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Es.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Es.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getCenterlineTangentDirections(),r=new Float32Array(3*i),o=new Float32Array(3*i);for(let t=0,a=0;t<n;++t){const n=3*t;for(let t=0;t<4;++t)r[a+0]=e[n+0],r[a+1]=e[n+1],r[a+2]=e[n+2],o[a+0]=e[n+3],o[a+1]=e[n+4],o[a+2]=e[n+5],a+=3}const a=Es.newInstance({numberOfComponents:3,values:r,name:"centerlineTopDirection"}),s=Es.newInstance({numberOfComponents:3,values:o,name:"centerlineBotDirection"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=cd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const s=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],l=t.renderable.getUseUniformOrientation();l?s.push("out vec3 centerlineDirVSOutput;","uniform vec3 centerlineDirection;"):s.push("out vec3 centerlineTopDirVSOutput;","out vec3 centerlineBotDirVSOutput;","out float centerlineAngleVSOutput;","attribute vec3 centerlineTopDirection;","attribute vec3 centerlineBotDirection;"),a=cd.substitute(a,"//VTK::Color::Dec",s).result;const c=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];l?c.push("centerlineDirVSOutput = centerlineDirection;"):c.push("vec3 sumVec = centerlineTopDirection + centerlineBotDirection;","float sumLen2 = dot(sumVec, sumVec);","float diffLen2 = 4.0 - sumLen2;","if (diffLen2 < 0.001) {","  // vectors are too close to each other, use lerp","  centerlineAngleVSOutput = -1.0; // use negative angle as a flag for lerp","  centerlineTopDirVSOutput = centerlineTopDirection;","  centerlineBotDirVSOutput = centerlineBotDirection;","} else if (sumLen2 == 0.0) {","  // vector are opposite to each other, don't make a choice for the user","  // use slerp without direction, it will display the centerline color on each row of pixel","  centerlineAngleVSOutput = 0.0;","  centerlineTopDirVSOutput = vec3(0.0);","  centerlineBotDirVSOutput = vec3(0.0);","} else {","  // use slerp","  centerlineAngleVSOutput = 2.0 * atan(sqrt(diffLen2/sumLen2));","  float sinAngle = sin(centerlineAngleVSOutput);","  centerlineTopDirVSOutput = centerlineTopDirection / sinAngle;","  centerlineBotDirVSOutput = centerlineBotDirection / sinAngle;","}"),a=cd.substitute(a,"//VTK::Color::Impl",c).result;const u=t.volumeTexture.getComponents(),d=o.getProperty().getIndependentComponents();let p=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];l?p.push("in vec3 centerlineDirVSOutput;"):p.push("in vec3 centerlineTopDirVSOutput;","in vec3 centerlineBotDirVSOutput;","in float centerlineAngleVSOutput;");const f=t.renderable.getCenterPoint();if(f&&p.push("uniform vec3 globalCenterPoint;"),d){for(let e=1;e<u;e++)p=p.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(u){case 1:p=p.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:p=p.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:p=p.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:mg("Unsupported number of independent coordinates.")}}i=cd.substitute(i,"//VTK::TCoord::Dec",p).result;let g=[];if(l?g.push("vec3 interpolatedCenterlineDir = centerlineDirVSOutput;"):g.push("vec3 interpolatedCenterlineDir;","if (centerlineAngleVSOutput < 0.0) {","  // Lerp","  interpolatedCenterlineDir = quadOffsetVSOutput.y * centerlineTopDirVSOutput + (1.0 - quadOffsetVSOutput.y) * centerlineBotDirVSOutput;","} else {","  // Slerp","  float topInterpolationAngle = quadOffsetVSOutput.y * centerlineAngleVSOutput;","  float botInterpolationAngle = centerlineAngleVSOutput - topInterpolationAngle;","  interpolatedCenterlineDir = sin(topInterpolationAngle) * centerlineTopDirVSOutput + sin(botInterpolationAngle) * centerlineBotDirVSOutput;","}","// Slerp should give a normalized vector but when sin(angle) is small, rounding error occurs","// Normalize for both lerp and slerp","interpolatedCenterlineDir = normalize(interpolatedCenterlineDir);"),f?g.push("float baseOffset = dot(interpolatedCenterlineDir, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):g.push("float horizontalOffset = quadOffsetVSOutput.x;"),g.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * interpolatedCenterlineDir;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(volumeTexture, volumePosTC);"),d){const e=["r","g","b","a"];for(let t=0;t<u;++t)g=g.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(u){case 1:g=g.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:g=g.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:g=g.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:mg("Unsupported number of independent coordinates.")}}else switch(u){case 1:g=g.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:g=g.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:g=g.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=cd.substitute(i,"//VTK::TCoord::Impl",g).result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=cd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=cd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=cd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=cd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=_d,e.Fragment=Fd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||mg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&n.getProgram().isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),e.name,e.offset,n.getCABO().getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&mg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const a=t.volumeTexture.getTextureUnit();if(n.getProgram().setUniformi("volumeTexture",a),n.getProgram().setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4f("backgroundColor",...t.renderable.getBackgroundColor()),n.getProgram().isUniformUsed("centerlineDirection")){const e=t.renderable.getUniformDirection();n.getProgram().setUniform3fArray("centerlineDirection",e)}if(n.getProgram().isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();n.getProgram().setUniform3fArray("globalCenterPoint",e)}const i=t.currentImageDataInput,s=i.getWorldToIndex(),l=P(new Float32Array(16),Rn([],i.getDimensions())),c=oe(l,l,s);if(n.getProgram().setUniformMatrix("MCTCMatrix",c),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const yg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Tg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),kd(e,t,n),jt.algo(e,t,2,0),t.tris=yd.newInstance(),t.volumeTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.pwfTexture=Nd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),vg(e,t)}),"vtkOpenGLImageCPRMapper");function bg(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Tg);const xg={context:null,keyMatrixTime:null,keyMatrices:null};const Cg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),bg(e,t)}),"vtkOpenGLImageSlice");function Sg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Cg);const Ag={};const Ig=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ag,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Sg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Ig);const wg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Pg={FRACTIONAL:0,PROPORTIONAL:1};var Og={InterpolationType:wg,OpacityMode:Pg};const Rg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5};var Mg={BlendMode:Rg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Dg,vtkErrorMacro:Eg}=Kt;function Vg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function Lg(e,t){t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(n,r)=>{if(n){t._openGLRenderWindow=e.getFirstAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const n=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera()),e.renderPiece(r,n)}},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define vtkIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n#ifdef vtkImageLabelOutlineOn\n\nuniform float outlineOpacity;\nuniform float vpWidth;\nuniform float vpHeight;\nuniform float vpOffsetX;\nuniform float vpOffsetY;\nuniform mat4 PCWCMatrix;\nuniform mat4 vWCtoIDX;\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents >= 3\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#endif\n#if vtkNumComponents >= 4\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef vtkIndependentComponentsOn\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.a = tmp.g;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n#if defined(vtkGradientOpacityOn)\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n#else\n  return 1.0;\n#endif\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n#ifdef vtkClippingPlanesOn\n  void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n  {\n    vec3 g1VC[3];\n    for (int i = 0; i < 3; ++i)\n    {\n      g1VC[i] = IStoVC(texPos[i]);\n    }\n    vec3 posVC = IStoVC(pos);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      for (int j = 0; j < 3; ++j)\n      {\n        if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n        {\n          g1[j] = 0.0;\n        }\n      }\n    }\n  }\n#endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    #ifdef vtkGradientOpacityOn\n      vec4 computeDensityNormal(float gradientMag, vec3 scalarInterp[2])\n      {\n    #else\n      //if gradient opacity not on but using density gradient\n      vec4 computeDensityNormal(vec3 scalarInterp[2])\n      {\n    #endif\n        vec3 opacityG1, opacityG2;\n        opacityG1.x = texture2D(otexture, vec2(scalarInterp[0].x * oscale0 + oshift0, 0.5)).r;\n        opacityG1.y = texture2D(otexture, vec2(scalarInterp[0].y * oscale0 + oshift0, 0.5)).r;\n        opacityG1.z = texture2D(otexture, vec2(scalarInterp[0].z * oscale0 + oshift0, 0.5)).r;\n        opacityG2.x = texture2D(otexture, vec2(scalarInterp[1].x * oscale0 + oshift0, 0.5)).r;\n        opacityG2.y = texture2D(otexture, vec2(scalarInterp[1].y * oscale0 + oshift0, 0.5)).r;\n        opacityG2.z = texture2D(otexture, vec2(scalarInterp[1].z * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n        float gradOpacityFactor = 1.0f;\n        if (gradientMag >= 0.0){\n          gradOpacityFactor = computeGradientOpacityFactor(gradientMag, goscale0, goshift0, gomin0, gomax0);\n        }\n        opacityG1.xyz *= gradOpacityFactor;\n        opacityG2.xyz *= gradOpacityFactor;\n    #endif\n\n        vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n        // divide by spacing\n        opacityG.xyz /= vSpacing;\n        opacityG.w = length(opacityG.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(opacityG.xyz);\n        if (length(opacityG.xyz) > 0.0) {\n          return vec4(normalize(opacityG.xyz),opacityG.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n\n      vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2])\n      {\n        vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n        vec3 yvec = vec3(0.0, tstep.y, 0.0);\n        vec3 zvec = vec3(0.0, 0.0, tstep.z);\n        vec3 texPosPVec[3];\n        texPosPVec[0] = pos + xvec;\n        texPosPVec[1] = pos + yvec;\n        texPosPVec[2] = pos + zvec;\n        vec3 texPosNVec[3];\n        texPosNVec[0] = pos - xvec;\n        texPosNVec[1] = pos - yvec;\n        texPosNVec[2] = pos - zvec;\n        vec3 g1, g2;\n\n        scalarInterp[0].x = getTextureValue(texPosPVec[0]).a;\n        scalarInterp[0].y = getTextureValue(texPosPVec[1]).a;\n        scalarInterp[0].z = getTextureValue(texPosPVec[2]).a;\n        scalarInterp[1].x = getTextureValue(texPosNVec[0]).a;\n        scalarInterp[1].y = getTextureValue(texPosNVec[1]).a;\n        scalarInterp[1].z = getTextureValue(texPosNVec[2]).a;\n\n        #ifdef vtkClippingPlanesOn\n          adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n          adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n        #endif\n        vec4 result;\n        result.x = scalarInterp[0].x - scalarInterp[1].x;\n        result.y = scalarInterp[0].y - scalarInterp[1].y;\n        result.z = scalarInterp[0].z - scalarInterp[1].z;\n        // divide by spacing\n        result.xyz /= vSpacing;\n        result.w = length(result.xyz);\n        // rotate to View Coords\n        rotateToViewCoord(result.xyz);\n        if (length(result.xyz) > 0.0) {\n          return vec4(normalize(result.xyz),result.w);\n        } else {\n          return vec4(0.0);\n        }\n      }\n  #endif\n\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n#ifdef vtkImageLabelOutlineOn\nvec3 fragCoordToIndexSpace(vec4 fragCoord) {\n  vec4 pcPos = vec4(\n    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n    (fragCoord.z - 0.5) * 2.0,\n    1.0);\n\n  vec4 worldCoord = PCWCMatrix * pcPos;\n  vec4 vertex = (worldCoord/worldCoord.w);\n\n  vec3 index = (vWCtoIDX * vertex).xyz;\n\n  // half voxel fix for labelmapOutline\n  return (index + vec3(0.5)) / vec3(volumeDimensions);\n}\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  // support gradient opacity\n  #ifdef vtkGradientOpacityOn\n    vec4 normal;\n  #endif\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #ifdef vtkGradientOpacityOn\n      normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n//=======================================================================\n// Given a texture value compute the color and opacity\n//\nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n#ifdef vtkImageLabelOutlineOn\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n  if (actualThickness == 0) {\n    return vec4(0, 0, 1, 1);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  if (float(tColor.a) > 0.01) {\n    for (int i = -actualThickness; i <= actualThickness; i++) {\n      for (int j = -actualThickness; j <= actualThickness; j++) {\n        if (i == 0 || j == 0) {\n          continue;\n        }\n\n        vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n          gl_FragCoord.y + float(j),\n          gl_FragCoord.z, gl_FragCoord.w);\n\n        vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n        vec4 value = getTextureValue(neighborPosIS);\n\n        // If any of my neighbours are not the same value as I\n        // am, this means I am on the border of the segment.\n        // We can break the loops\n        if (any(notEqual(value, centerValue))) {\n          pixelOnBorder = true;\n          break;\n        }\n      }\n\n      if (pixelOnBorder == true) {\n        break;\n      }\n    }\n\n    // If I am on the border, I am displayed at full opacity\n    if (pixelOnBorder == true) {\n      tColor.a = outlineOpacity;\n    }\n  }\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n  //\n  vec4 goFactor = vec4(1.0,1.0,1.0,1.0);\n\n  // compute the normal vectors as needed\n  #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #if !defined(vtkComponent0Proportional)\n        vec4 normal0 = normalMat[0];\n      #endif\n      #if !defined(vtkComponent1Proportional)\n        vec4 normal1 = normalMat[1];\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          vec4 normal2 = normalMat[2];\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            vec4 normal3 = normalMat[3];\n          #endif\n        #endif\n      #endif\n    #else\n      vec4 normalLight;\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp);\n        if (length(normal0)>0.0){\n          #ifdef vtkGradientOpacityOn\n            normalLight = computeDensityNormal(normal0.w, scalarInterp);\n          #else\n            normalLight = computeDensityNormal(scalarInterp);\n          #endif\n          if (length(normalLight) == 0.0){\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n        normalLight = normal0;\n      #endif\n    #endif\n  #endif\n\n  // compute gradient opacity factors as needed\n  #if defined(vtkGradientOpacityOn)\n    #if !defined(vtkComponent0Proportional)\n      goFactor.x =\n        computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    #if defined(vtkIndependentComponentsOn) && (vtkNumComponents > 1)\n      #if !defined(vtkComponent1Proportional)\n        goFactor.y =\n          computeGradientOpacityFactor(normal1.a, goscale1, goshift1, gomin1, gomax1);\n      #endif\n      #if vtkNumComponents > 2\n        #if !defined(vtkComponent2Proportional)\n          goFactor.z =\n            computeGradientOpacityFactor(normal2.a, goscale2, goshift2, gomin2, gomax2);\n        #endif\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            goFactor.w =\n              computeGradientOpacityFactor(normal3.a, goscale3, goshift3, gomin3, gomax3);\n          #endif\n        #endif\n      #endif\n    #endif\n  #endif\n\n  // single component is always independent\n  #if vtkNumComponents == 1\n    vec4 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5));\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n    if (tColor.a < EPSILON){\n      return vec4(0.0);\n    }\n  #endif\n\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n    vec4 tColor = mix0*texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0));\n    #if !defined(vtkComponent0Proportional)\n      tColor.a = goFactor.x*mix0*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n      tColor *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix0));\n    #endif\n\n    vec3 tColor1 = mix1*texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n    #if !defined(vtkComponent1Proportional)\n      tColor.a += goFactor.y*mix1*texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n    #else\n      float pwfValue = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n      tColor1 *= pwfValue;\n      tColor.a *= mix(pwfValue, 1.0, (1.0 - mix1));\n    #endif\n\n    #if vtkNumComponents >= 3\n      vec3 tColor2 = mix2*texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n      #if !defined(vtkComponent2Proportional)\n        tColor.a += goFactor.z*mix2*texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n      #else\n        float pwfValue = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n        tColor2 *= pwfValue;\n        tColor.a *= mix(pwfValue, 1.0, (1.0 - mix2));\n      #endif\n\n      #if vtkNumComponents >= 4\n        vec3 tColor3 = mix3*texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n        #if !defined(vtkComponent3Proportional)\n          tColor.a += goFactor.w*mix3*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #else\n          float pwfValue = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n          tColor3 *= pwfValue;\n          tColor.a *= mix(pwfValue, 1.0, (1.0 - mix3));\n        #endif\n      #endif\n    #endif\n  #else // then not independent\n\n  #if vtkNumComponents == 2\n    float lum = tValue.r * cscale0 + cshift0;\n    float alpha = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    vec4 tColor = vec4(lum, lum, lum, alpha);\n  #endif\n  #if vtkNumComponents == 3\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n  #endif\n  #if vtkNumComponents == 4\n    vec4 tColor;\n    tColor.r = tValue.r * cscale0 + cshift0;\n    tColor.g = tValue.g * cscale1 + cshift1;\n    tColor.b = tValue.b * cscale2 + cshift2;\n    tColor.a = goFactor.x*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n  #endif\n  #endif // dependent\n\n  // apply lighting if requested as appropriate\n  #if vtkLightComplexity > 0\n    #if !defined(vtkComponent0Proportional)\n      #if vtkNumComponents == 1\n        #ifdef SurfaceShadowOn\n            #if vtkLightComplexity < 3\n                vec3 tColorS = applyLightingDirectional(posIS, tColor, normalLight);\n            #else\n                vec3 tColorS = applyLightingPositional(posIS, tColor, normalLight, IStoVC(posIS));\n            #endif\n        #endif\n\n        #ifdef VolumeShadowOn\n          vec3 tColorVS = applyShadowRay(tColor.rgb, posIS, rayDirVC);\n          #ifdef SurfaceShadowOn\n            float vol_coef = volumetricScatteringBlending * (1.0 - tColor.a / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n            tColor.rgb = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n          #else\n            tColor.rgb = tColorVS;\n          #endif\n        #else\n            tColor.rgb = tColorS;\n        #endif\n\n      #else\n        applyLighting(tColor.rgb, normal0);\n      #endif\n    #endif\n\n    #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n      #if !defined(vtkComponent1Proportional)\n        applyLighting(tColor1, normal1);\n      #endif\n    #if vtkNumComponents >= 3\n      #if !defined(vtkComponent2Proportional)\n        applyLighting(tColor2, normal2);\n      #endif\n    #if vtkNumComponents >= 4\n      #if !defined(vtkComponent3Proportional)\n        applyLighting(tColor3, normal3);\n      #endif\n    #endif\n    #endif\n    #endif\n  #endif\n\n// perform final independent blend as needed\n#if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 2\n  tColor.rgb += tColor1;\n#if vtkNumComponents >= 3\n  tColor.rgb += tColor2;\n#if vtkNumComponents >= 4\n  tColor.rgb += tColor3;\n#endif\n#endif\n#endif\n\n#endif\nreturn tColor;\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents == 2\n     if (val.r >= min.r && val.r <= max.r &&\n        val.g >= min.g && val.g <= max.g) {\n      withinRange = true;\n    }\n  #endif\n  #if defined(vtkIndependentComponentsOn) && vtkNumComponents >= 3\n    if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n        all(lessThanEqual(val, ipScalarRangeMax))) {\n      withinRange = true;\n    }\n  #endif\n  return withinRange;\n}\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Fragment;o.getProperty().getInterpolationType()===wg.LINEAR&&(a=cd.substitute(a,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===o.getProperty().getUseLabelOutline()&&(a=cd.substitute(a,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result);const i=t.scalarTexture.getComponents();a=cd.substitute(a,"//VTK::NumComponents",`#define vtkNumComponents ${i}`).result;const s=o.getProperty().getIndependentComponents();if(s){a=cd.substitute(a,"//VTK::IndependentComponentsOn","#define vtkIndependentComponentsOn").result;const e=[];for(let t=0;t<i;t++)o.getProperty().getOpacityMode(t)===Pg.PROPORTIONAL&&e.push(`#define vtkComponent${t}Proportional`);e.length>0&&(a=cd.substitute(a,"//VTK::vtkProportionalComponents",e.join("\n")).result)}const l=t.currentInput.getSpatialExtent(),c=t.currentInput.getSpacing(),u=new Float64Array(3);In(u,(l[1]-l[0])*c[0],(l[3]-l[2])*c[1],(l[5]-l[4])*c[2]);const d=Sn(u)/e.getCurrentSampleDistance(r);a=cd.substitute(a,"//VTK::MaximumSamplesValue",`${Math.ceil(d)}`).result,a=cd.substitute(a,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lastLightComplexity}`).result,t.lastLightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(a=cd.substitute(a,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(a=cd.substitute(a,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0&&(a=cd.substitute(a,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result)),t.gopacity=o.getProperty().getUseGradientOpacity(0);for(let e=1;s&&!t.gopacity&&e<i;++e)o.getProperty().getUseGradientOpacity(e)&&(t.gopacity=!0);t.gopacity&&(a=cd.substitute(a,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(a=cd.substitute(a,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(a=cd.substitute(a,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,a=cd.substitute(a,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),a=cd.substitute(a,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=a,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lastLightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=cd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lastLightComplexity&&(o=cd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=cd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=cd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=cd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=cd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{let a=0;o.getProperty().getShade()&&t.renderable.getBlendMode()===Rg.COMPOSITE_BLEND&&(a=0,t.numberOfLights=0,r.getLights().forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===a&&(a=1)),1===a&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(a=2),a<3&&e.getPositional()&&(a=3)})));let i=!1;t.lastLightComplexity!==a&&(t.lastLightComplexity=a,i=!0);const s=t.scalarTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=!1;const u=[];if(l){for(let e=0;e<s;e++)u.push(o.getProperty().getOpacityMode(e));u.length>0&&(c=!0)}const d=t.currentInput.getSpatialExtent(),p=t.currentInput.getSpacing(),f=new Float64Array(3);In(f,(d[1]-d[0])*p[0],(d[3]-d[2])*p[1],(d[5]-d[4])*p[2]);const g=Sn(f)/e.getCurrentSampleDistance(r),m={interpolationType:o.getProperty().getInterpolationType(),useLabelOutline:o.getProperty().getUseLabelOutline(),numComp:s,usesProportionalComponents:c,iComps:l,maxSamples:g,useGradientOpacity:o.getProperty().getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),proportionalComponents:u};return t.previousState&&t.previousState.interpolationType===m.interpolationType&&t.previousState.useLabelOutline===m.useLabelOutline&&t.previousState.numComp===m.numComp&&t.previousState.usesProportionalComponents===m.usesProportionalComponents&&t.previousState.iComps===m.iComps&&t.previousState.maxSamples===m.maxSamples&&t.previousState.useGradientOpacity===m.useGradientOpacity&&t.previousState.blendMode===m.blendMode&&function(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;++n)if(e[n]!==t[n])return!1;return!0}(t.previousState.proportionalComponents,m.proportionalComponents)?!!(0===n.getProgram()?.getHandle()||i||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||!!t.lastZBufferTexture!=!!t.zBufferTexture||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime())&&(t.lastZBufferTexture=t.zBufferTexture,!0):(t.previousState={...m},!0)},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Dg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*Dn(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(o.getProperty().getUseLabelOutline()){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lastLightComplexity)return;let O=0;const R=[],M=[],D=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],D[0+3*O]=-.5*w[0],D[1+3*O]=-.5*w[1],D[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",D),3===t.lastLightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();o.setUniformi("ctexture",t.colorTexture.getTextureUnit()),o.setUniformi("otexture",t.opacityTexture.getTextureUnit()),o.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),o.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const a=t.scalarTexture.getVolumeInfo(),i=r.getProperty(),s=t.scalarTexture.getComponents(),l=r.getProperty().getIndependentComponents();if(l&&s>=2)for(let e=0;e<s;e++)o.setUniformf(`mix${e}`,r.getProperty().getComponentWeight(e));for(let e=0;e<s;e++){const t=l?e:0,n=a.scale[e],r=i.getScalarOpacity(t).getRange(),s=n/(r[1]-r[0]),c=(a.offset[e]-r[0])/(r[1]-r[0]);o.setUniformf(`oshift${e}`,c),o.setUniformf(`oscale${e}`,s);const u=i.getRGBTransferFunction(t).getRange(),d=(a.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);o.setUniformf(`cshift${e}`,d),o.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(l)for(let e=0;e<s;++e){const t=a.scale[e];if(i.getUseGradientOpacity(e)){const n=i.getGradientOpacityMinimumOpacity(e),r=i.getGradientOpacityMaximumOpacity(e);o.setUniformf(`gomin${e}`,n),o.setUniformf(`gomax${e}`,r);const a=[i.getGradientOpacityMinimumValue(e),i.getGradientOpacityMaximumValue(e)];o.setUniformf(`goscale${e}`,t*(r-n)/(a[1]-a[0])),o.setUniformf(`goshift${e}`,-a[0]*(r-n)/(a[1]-a[0])+n)}else o.setUniformf(`gomin${e}`,1),o.setUniformf(`gomax${e}`,1),o.setUniformf(`goscale${e}`,0),o.setUniformf(`goshift${e}`,1)}else{const e=a.scale[s-1],t=i.getGradientOpacityMinimumOpacity(0),n=i.getGradientOpacityMaximumOpacity(0);o.setUniformf("gomin0",t),o.setUniformf("gomax0",n);const r=[i.getGradientOpacityMinimumValue(0),i.getGradientOpacityMaximumValue(0)];o.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),o.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===r.getProperty().getUseLabelOutline()){const e=r.getProperty().getLabelOutlineOpacity();o.setUniformf("outlineOpacity",e)}t.lastLightComplexity>0&&(o.setUniformf("vAmbient",i.getAmbient()),o.setUniformf("vDiffuse",i.getDiffuse()),o.setUniformf("vSpecular",i.getSpecular()),o.setUniformf("vSpecularPower",i.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*Dn(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===wg.NEAREST?(t.scalarTexture.setMinificationFilter(bd.NEAREST),t.scalarTexture.setMagnificationFilter(bd.NEAREST)):(t.scalarTexture.setMinificationFilter(bd.LINEAR),t.scalarTexture.setMagnificationFilter(bd.LINEAR)),t.lastBoundBO=null,null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=fd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Eg("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Eg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;t._scalars!==a&&(t._openGLRenderWindow.releaseGraphicsResourcesForObject(t._scalars),t._scalars=a);const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(bd.LINEAR),t.jitterTexture.setMagnificationFilter(bd.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=i.getIndependentComponents()?s:1,c=i.getScalarOpacity(),u=t._openGLRenderWindow.getGraphicsResourceForObject(c);let d=Vg(i,c,l);if(u.vtkObj&&u.hash===d&&t.opacityTextureString===d)t.opacityTexture=u.vtkObj,t.opacityTextureString=u.hash;else{const r=1024,o=2*r*l,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<l;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.releaseGraphicsResources(t._openGLRenderWindow),t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(bd.LINEAR),t.opacityTexture.setMagnificationFilter(bd.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*l,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*l,1,ys.UNSIGNED_CHAR,e)}t.opacityTextureString=d,c&&t._openGLRenderWindow.setGraphicsResourceForObject(c,t.opacityTexture,t.opacityTextureString)}const p=i.getRGBTransferFunction();d=Vg(i,p,l);const f=t._openGLRenderWindow.getGraphicsResourceForObject(p);if(f?.vtkObj&&f?.hash===d&&t.colorTextureString===d)t.colorTexture=f.vtkObj,t.colorTextureString=f.hash;else{const e=1024,n=new Uint8ClampedArray(2*e*l*3),r=new Float32Array(3*e);for(let t=0;t<l;++t){const o=i.getRGBTransferFunction(t),a=o.getRange();o.getTable(a[0],a[1],e,r,1);for(let o=0;o<3*e;++o)n[t*e*6+o]=255*r[o],n[t*e*6+o+3*e]=255*r[o]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(bd.LINEAR),t.colorTexture.setMagnificationFilter(bd.LINEAR),t.colorTexture.create2DFromRaw(e,2*l,3,ys.UNSIGNED_CHAR,n),t.colorTextureString=d,p&&t._openGLRenderWindow.setGraphicsResourceForObject(p,t.colorTexture,t.colorTextureString)}e.updateLabelOutlineThicknessTexture(r);const g=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(d=`${o.getMTime()}A${a.getMTime()}`,g?.vtkObj&&g?.hash===d&&t.scalarTextureString===d)t.scalarTexture=g.vtkObj,t.scalarTextureString=g.hash;else{const e=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.releaseGraphicsResources(t._openGLRenderWindow),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(e[0],e[1],e[2],a,t.renderable.getPreferSizeOverAccuracy()),t.scalarTextureString=d,a&&t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,t.scalarTextureString)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Es.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Es.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=e=>{const n=e.getProperty().getLabelOutlineThickness(),r=t._openGLRenderWindow.getGraphicsResourceForObject(n),o=`${n.join("-")}`;if(r?.vtkObj&&r?.hash===o&&t.labelOutlineThicknessTextureString===o)t.labelOutlineThicknessTexture=r.vtkObj,t.labelOutlineThicknessTextureString=r.hash;else{const e=1024,r=1,a=new Uint8Array(e*r);for(let t=0;t<e;++t){const e=n[t]||n[0];a[t]=e}t.labelOutlineThicknessTexture.releaseGraphicsResources(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(bd.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(e,r,1,ys.UNSIGNED_CHAR,a),t.labelOutlineThicknessTextureString=o,n&&t._openGLRenderWindow.setGraphicsResourceForObject(n,t.labelOutlineThicknessTexture,t.labelOutlineThicknessTextureString)}}}const Bg={context:null,VBOBuildTime:null,scalarTexture:null,scalarTextureString:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lastLightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0,_scalars:null};const Ng=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bg,n),Zt.extend(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=yd.newInstance(),t.scalarTexture=Nd.newInstance(),t.opacityTexture=Nd.newInstance(),t.colorTexture=Nd.newInstance(),t.jitterTexture=Nd.newInstance(),t.jitterTexture.setWrapS(Td.REPEAT),t.jitterTexture.setWrapT(Td.REPEAT),t.labelOutlineThicknessTexture=Nd.newInstance(),t.framebuffer=Hp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Lg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",Ng);const{vtkDebugMacro:_g}=Kt;function Fg(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent();const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():_g("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const kg={};const Gg=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kg,n),Zt.extend(e,t,n),Fg(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Gg);var Ug="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:zg}=ds;function Wg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Nd.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Hg(e,t){return Wg(e,t,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE)}function jg(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Hg(t._openGLRenderWindow,t.size),t.seedTexture0=Hg(t._openGLRenderWindow,t.size),t.licTexture1=Hg(t._openGLRenderWindow,t.size),t.seedTexture1=Hg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Wg(t._openGLRenderWindow,t.size,Nd.Filter.NEAREST,Nd.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Wg(e,t,Nd.Filter.LINEAR,Nd.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const Kg={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function $g(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kg,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),jg(e,t)}var qg={newInstance:jt.newInstance($g,"vtkLICPingPongBufferManager"),extend:$g};const Xg=0,Yg=1,Zg=2,Qg=3,Jg=1;function em(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Hp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=cd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;f&&o[0]===f.getSize()[0]&&o[1]===f.getSize()[1]||(f=Hp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=qg.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const g=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],m=1/o[0],h=1/o[1],v=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;v.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:y}=t;v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",0),y.setUniformf("uMaskThreshold",t.maskThreshold),y.setUniform2f("uNoiseBoundsPt1",...g),y.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),y.setUniformi("texLIC",t.bufs.getLICTextureUnit()),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,y);const{LICIShaderProgram:T}=t;v.readyShaderProgram(T),T.setUniformi("uPassNo",0),T.setUniformf("uStepSize",-d),T.setUniform2f("uNoiseBoundsPt1",...g),T.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:b}=t;if(v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,b),t.enhancedLIC){t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",m),a.setUniformf("uDy",h),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),v.readyShaderProgram(y),y.setUniformi("uStepNo",0),y.setUniformi("uPassNo",1),n(y,t.bufs),y.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformi("uPassNo",1),T.setUniformf("uStepSize",-d),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);v.readyShaderProgram(y),y.setUniformi("uStepNo",1),n(y,t.bufs),r(t.bufs,o,y),v.readyShaderProgram(T),T.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(T,t.bufs),r(t.bufs,o,T);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),v.readyShaderProgram(b),b.setUniformi("texLIC",t.bufs.getLICTextureUnit()),b.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,b)}if(t.antiAlias){const e=t.AAHProgram;v.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",m);const a=t.AAVProgram;v.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",h);for(let i=0;i<t.antiAlias;++i)v.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),v.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==Yg&&t.enhanceContrast!==Qg||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const tm={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLineIntegralConvolution2D"),extend:nm};function om(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const am={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:Xg,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:Jg,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function im(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,am,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),om(0,t)}var sm={newInstance:jt.newInstance(im,"vtkSurfaceLICInterface"),extend:im};const{Representation:lm}=ds;function cm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=fd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===Jg?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Nd.newInstance({wrapS:Nd.Wrap.REPEAT,wrapT:Nd.Wrap.REPEAT,minificationFilter:Nd.Filter.NEAREST,magnificationFilter:Nd.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Ug,e,""),e.allocateTextures=()=>{const n=Nd.Filter.NEAREST,r=Nd.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Nd.newInstance({wrapS:Nd.Wrap.CLAMP_TO_EDGE,wrapT:Nd.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Nd.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Hp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=yd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Es.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Es.newInstance({numberOfComponents:1,values:o}),s=Es.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",lm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=rm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===Zg||u===Qg){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const um={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),sm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkSurfaceLICInterface"),extend:dm};const{vtkErrorMacro:fm}=Kt;function gm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=cd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=cd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=cd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=cd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=cd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(fm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(fm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(fm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=sm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=pm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(fm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const mm={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const hm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mm,n),np.extend(e,t,n),gm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",hm);const{vtkErrorMacro:vm}=Kt;function ym(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||vm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,zu.ARRAY_BUFFER),o&&a.getColorBO().upload(f,zu.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Tm={};const bm=Et((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),np.extend(e,t,n),ym(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",bm);const{vtkErrorMacro:xm}=Kt;function Cm(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Fd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=cd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=cd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=cd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=cd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=cd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=cd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=cd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||xm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||xm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||xm("Error setting 'radiusMC' in shader 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o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(qu.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():xm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let 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e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Dh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const Vh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Lh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Eh(e,t)}var Bh={newInstance:jt.newInstance(Lh,"vtkCamera"),extend:Lh};function Nh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const _h={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function Fh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_h,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),Nh(e,t)}var kh={newInstance:jt.newInstance(Fh,"vtkLight"),extend:Fh,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Gh}=jt;function Uh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Gh("call displayToView on your view instead"),e.viewToDisplay=()=>Gh("callviewtodisplay on your view instead"),e.getSize=()=>Gh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Gh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkViewport"),extend:Wh};const{vtkDebugMacro:jh,vtkErrorMacro:Kh,vtkWarningMacro:$h}=Kt;function qh(e){return()=>Kh(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function Xh(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(jh("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):Kh("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=qh("allocateTime"),e.updateGeometry=qh("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{jh(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Bh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=kh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return Kh("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return Kh("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return Kh("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),jh("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return jh("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return Kh("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&($h("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return jh("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return Kh("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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Yh={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function Zh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,Yh,n),Hh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),Xh(e,t)}var Qh={newInstance:Et(Zh,"vtkRenderer"),extend:Zh};const Jh=Object.create(null);function ev(e,t){Jh[e]=t}function tv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return Jh[e]&&Jh[e](t)}function nv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return tv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))}}const rv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1};function ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),nv(e,t)}var av={newInstance:jt.newInstance(ov,"vtkRenderWindow"),extend:ov,registerViewConstructor:ev,listViewAPIs:function(){return Object.keys(Jh)},newAPISpecificView:tv};const iv={Unknown:0,LeftController:1,RightController:2},sv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var lv={Device:iv,Input:sv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:cv,Input:uv}=lv,{vtkWarningMacro:dv,vtkErrorMacro:pv,normalizeWheel:fv,vtkOnceErrorMacro:gv}=jt,mv={ctrlKey:!1,altKey:!1,shiftKey:!1},hv={"xr-standard":[uv.Trigger,uv.Grip,uv.TrackPad,uv.Thumbstick,uv.A,uv.B]},vv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function yv(e){e.cancelable&&e.preventDefault()}function Tv(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function bv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{pv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>dv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",yv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",yv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&yv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&dv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&yv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:pv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?dv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):pv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;dv(`${e} did not request an 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t.lastGamepadValues[i.index][s].buttons||(t.lastGamepadValues[i.index][s].buttons[r]=!1),t.lastGamepadValues[i.index][s].buttons[r]!==i.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,pressed:i.buttons[r].pressed,device:"left"===n.handedness?cv.LeftController:cv.RightController,input:hv[i.mapping]&&hv[i.mapping][r]?hv[i.mapping][r]:uv.Trigger}),t.lastGamepadValues[i.index][s].buttons[r]=i.buttons[r].pressed),t.lastGamepadValues[i.index][s].buttons[r]&&null!=a&&e.move3DEvent({gamepad:i,position:a.transform.position,orientation:a.transform.orientation,device:"left"===n.handedness?cv.LeftController:cv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{yv(n);const r={...fv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:pv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);if(2===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(mv),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Tv(o);e.recognizeGesture("TouchEnd",t);const a={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Tv(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(mv),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},vv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void gv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const xv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Cv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xv,n),jt.obj(e,t),t._animationExtendedEnd=0,jt.event(e,t,"RenderEvent"),vv.forEach((n=>jt.event(e,t,n))),jt.get(e,t,["initialized","interactorStyle","lastFrameTime","recentAnimationFrameRate","_view"]),jt.setGet(e,t,["container","lightFollowCamera","enabled","enableRender","recognizeGestures","desiredUpdateRate","stillUpdateRate","picker","preventDefaultOnPointerDown","preventDefaultOnPointerUp","mouseScrollDebounceByPass"]),jt.moveToProtected(e,t,["view"]),bv(e,t)}var Sv={newInstance:jt.newInstance(Cv,"vtkRenderWindowInteractor"),extend:Cv,handledEvents:vv,...lv};const{vtkErrorMacro:Av,VOID:Iv}=jt;function wv(e,t){t.classHierarchy.push("vtkInteractorObserver");const n={...e};function r(){for(;t.subscribedEvents.length;)t.subscribedEvents.pop().unsubscribe()}function o(){Sv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Iv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Av("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Pv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function Ov(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),wv(e,t)}var Rv={newInstance:jt.newInstance(Ov,"vtkInteractorObserver"),extend:Ov,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},Mv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Dv}=Mv,Ev={Rotate:Dv.IS_ROTATE,Pan:Dv.IS_PAN,Spin:Dv.IS_SPIN,Dolly:Dv.IS_DOLLY,CameraPose:Dv.IS_CAMERA_POSE,WindowLevel:Dv.IS_WINDOW_LEVEL,Slice:Dv.IS_SLICE};function Vv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Ev).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Dv.IS_NONE&&(t.state=Ev[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Ev[n]&&(t.state=Dv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":e.pokedRenderer.resetCamera(),n.render();break;case"w":case"W":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=e.pokedRenderer.getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Lv={state:Dv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Bv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lv,n),Rv.extend(e,t,n),Vv(e,t)}var Nv={newInstance:jt.newInstance(Bv,"vtkInteractorStyle"),extend:Bv,...Mv};const{States:_v}=Mv;function Fv(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=n.pokedRenderer;switch(t.state){case _v.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case _v.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad?!n||n.pressed||n.device!==iv.RightController||n.input!==sv.Trigger&&n.input!==sv.TrackPad||t.state!==_v.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===_v.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const t=e.pokedRenderer.getActiveCamera(),n=t.getPhysicalTranslation(),r=.025*t.getPhysicalScale(),o=t.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);t.setPhysicalTranslation(n[0]+o[0]*r,n[1]+o[1]*r,n[2]+o[2]*r)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case _v.IS_DOLLY:e.endDolly();break;case _v.IS_PAN:e.endPan();break;case _v.IS_SPIN:e.endSpin();break;case _v.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(n.pokedRenderer,n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=n.pokedRenderer.getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(n.pokedRenderer,o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(n.pokedRenderer,o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(n.pokedRenderer,o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&n.pokedRenderer.updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=e.pokedRenderer.getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(n.pokedRenderer,r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const kv={motionFactor:10,zoomFactor:10};function Gv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kv,n),Nv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),Fv(e,t)}var Uv={newInstance:jt.newInstance(Gv,"vtkInteractorStyleTrackballCamera"),extend:Gv};function zv(e){return e}function Wv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Wv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Hv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Wv:zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function jv(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const Kv={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function $v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kv,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),jv(e,t)}var qv={newInstance:jt.newInstance($v,"vtkRenderPass"),extend:$v};const{Representation:Xv}=ds,{vtkErrorMacro:Yv}=jt;function Zv(e){const t=cd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function Qv(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Es.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Es.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Es.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",Xv.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Hp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Nd.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Nd.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Nd.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||Yv("Error setting vertexDC in copy shader 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CT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ST(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group 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0!==arguments[2]?arguments[2]:{};Object.assign(t,AT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ST(e,t)}var wT={newInstance:Et(IT,"vtkWebGPURenderEncoder"),extend:IT},PT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const OT=["getMappedRange","mapAsync","unmap"];function RT(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<OT.length;n++)e[OT[n]]=function(){return t.handle[OT[n]](...arguments)}}const MT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function DT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,MT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),RT(e,t)}var ET={newInstance:jt.newInstance(DT),extend:DT,...PT};const{Representation:VT}=ds,{PrimitiveTypes:LT}=PT;class BT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function NT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function _T(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=NT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=NT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=NT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=NT(n,o,t),n.ibo[n.iboId++]=a}function FT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let kT;const GT=new Uint32Array(1),UT=new Uint32Array(2),zT=new Uint32Array(3),WT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)GT[0]=t[n+a],kT(GT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)UT[0]=t[n+a],UT[1]=t[n+(a+1)%e],kT(UT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)UT[0]=t[n+a],UT[1]=t[n+a+1],kT(UT,r,o);for(let a=0;a<e-2;a++)UT[0]=t[n+a],UT[1]=t[n+a+2],kT(UT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n],zT[1]=t[n+a+1],zT[2]=t[n+a+2],kT(zT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)zT[0]=t[n+a],zT[1]=t[n+a+1+a%2],zT[2]=t[n+a+1+(a+1)%2],kT(zT,r,o)}};function HT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case LT.Points:return"points";case LT.Lines:return"lines";case LT.Triangles:case LT.TriangleEdges:return"polys";case LT.TriangleStripEdges:case LT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new BT};let d=null;d=o===VT.POINTS||r===LT.Points?WT.anythingToPoints:o===VT.WIREFRAME||r===LT.Lines?WT[`${l}ToWireframe`]:WT[`${l}ToSurface`],kT=FT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),kT=_T,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const jT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function KT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jT,n),ET.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),HT(e,t)}var $T={newInstance:jt.newInstance(KT),extend:KT,...PT};const{BufferUsage:qT}=PT,{vtkErrorMacro:XT}=Kt,{VtkDataTypes:YT}=Es;function ZT(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function QT(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function JT(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===qT.Index&&(n=$T.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=ET.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===qT.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===qT.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===qT.PointArray){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=ZT(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===qT.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=rT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=QT(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ZT(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===qT.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(nT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case YT.UNSIGNED_CHAR:t="uint8";break;case YT.FLOAT:t="float32";break;case YT.UNSIGNED_INT:t="uint32";break;case YT.INT:t="sint32";break;case YT.DOUBLE:t="float32";break;case YT.UNSIGNED_SHORT:t="uint16";break;case YT.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||XT(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:qT.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=ET.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const eb={device:null,fullScreenQuadBuffer:null};function tb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eb,n),yt(e,t),At(e,t,["device"]),JT(e,t)}var nb={newInstance:Et(tb),extend:tb,...PT};function rb(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const ob={device:null,handle:null,label:null,options:null};function ab(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ob,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),rb(e,t)}var ib={newInstance:jt.newInstance(ab),extend:ab};function sb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(t.texture.getFormat());t.bindGroupLayoutEntry.texture.sampleType=r.sampleType},e.createFromTextureHandle=(e,n)=>{t.texture=null,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e,t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const r=tT(n.format);t.bindGroupLayoutEntry.texture.sampleType=r.sampleType,t.bindGroupTime.modified()},e.getBindGroupEntry=()=>({resource:e.getHandle()}),e.getShaderCode=(e,n)=>{let r="f32";"sint"===t.bindGroupLayoutEntry.texture.sampleType?r="i32":"uint"===t.bindGroupLayoutEntry.texture.sampleType&&(r="u32");let o=`@binding(${e}) @group(${n}) var ${t.label}: texture_${t.options.dimension}<${r}>;`;return"depth"===t.bindGroupLayoutEntry.texture.sampleType&&(o=`@binding(${e}) @group(${n}) var ${t.label}: texture_depth_${t.options.dimension};`),o},e.addSampler=(n,r)=>{const o=ib.newInstance({label:`${t.label}Sampler`});o.create(n,r),e.setSampler(o)},e.getBindGroupTime=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.bindGroupTime),e.getHandle=()=>(t.texture&&t.texture.getHandle()!==t.textureHandle&&(t.textureHandle=t.texture.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupTime.modified()),t.handle)}const lb={texture:null,handle:null,sampler:null,label:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),jt.obj(e,t),t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,texture:{sampleType:"float",viewDimension:"2d"}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","texture"]),jt.setGet(e,t,["bindGroupLayoutEntry","label","sampler"]),sb(e,t)}var ub={newInstance:jt.newInstance(cb),extend:cb};const{BufferUsage:db}=nb;function pb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return t.device.getHandle().queue.copyExternalImageToTexture({source:n.canvas,flipY:n.flip},{texture:t.handle,premultipliedAlpha:!0},[t.width,t.height,t.depth]),void(t.ready=!0);n.jsImageData&&!n.nativeArray&&(n.width=n.jsImageData.width,n.height=n.jsImageData.height,n.depth=1,n.format="rgba8unorm",n.flip=!0,n.nativeArray=n.jsImageData.data);const o=tT(t.format);let a=t.width*o.stride;const i=(e,t,n)=>{const r=e.length/(t*n)*e.BYTES_PER_ELEMENT,a=2===o.elementSize&&"float"===o.sampleType;if(a||r%256){const i=e,s=r/i.BYTES_PER_ELEMENT,l=o.elementSize,c=256*Math.floor((s*l+255)/256),u=c/l,d=jt.newTypedArray(a?"Uint16Array":i.constructor.name,u*t*n);for(let e=0;e<t*n;e++)if(a)for(let t=0;t<s;t++)d[e*u+t]=Ad.toHalf(i[e*s+t]);else d.set(i.subarray(e*s,(e+1)*s),e*u);return[d,c]}return[e,r]};if(n.nativeArray&&(r=n.nativeArray),n.image){const e=document.createElement("canvas");e.width=n.image.width,e.height=n.image.height;const t=e.getContext("2d");t.translate(0,e.height),t.scale(1,-1),t.drawImage(n.image,0,0,n.image.width,n.image.height,0,0,e.width,e.height),r=t.getImageData(0,0,n.image.width,n.image.height).data}const s=t.device.createCommandEncoder();if(3!==e.getDimensionality()){const e=Tu.generateMipmaps(r,t.width,t.height,t.mipLevel);let o=t.width,l=t.height;for(let r=0;r<=t.mipLevel;r++){const c=i(e[r],l,1);a=c[1];const u={dataArray:n.dataArray?n.dataArray:null,nativeArray:c[0],usage:db.Texture},d=t.device.getBufferManager().getBuffer(u);s.copyBufferToTexture({buffer:d.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:l},{texture:t.handle,mipLevel:r},[o,l,1]),o/=2,l/=2}t.device.submitCommandEncoder(s),t.ready=!0}else{const e=i(r,t.height,t.depth);a=e[1];const o={dataArray:n.dataArray?n.dataArray:null,usage:db.Texture};o.nativeArray=e[0];const l=t.device.getBufferManager().getBuffer(o);s.copyBufferToTexture({buffer:l.getHandle(),offset:0,bytesPerRow:a,rowsPerImage:t.height},{texture:t.handle},[t.width,t.height,t.depth]),t.device.submitCommandEncoder(s),t.ready=!0}},e.getScale=()=>{const e=tT(t.format);return 2===e.elementSize&&"float"===e.sampleType?1:255},e.getNumberOfComponents=()=>tT(t.format).numComponents,e.getDimensionality=()=>{let e=0;return t.width>1&&e++,t.height>1&&e++,t.depth>1&&e++,e},e.resizeToMatch=e=>{e.getWidth()===t.width&&e.getHeight()===t.height&&e.getDepth()===t.depth||(t.width=e.getWidth(),t.height=e.getHeight(),t.depth=e.getDepth(),t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.resize=function(e,n){let r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;e===t.width&&n===t.height&&r===t.depth||(t.width=e,t.height=n,t.depth=r,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label}))},e.createView=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};r.dimension||(r.dimension=1===t.depth?"2d":"3d");const o=ub.newInstance({label:n});return o.create(e,r),o}}const fb={device:null,handle:null,buffer:null,ready:!1,label:null};function gb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fb,n),jt.obj(e,t),jt.get(e,t,["handle","ready","width","height","depth","format","usage"]),jt.setGet(e,t,["device","label"]),pb(e,t)}var mb={newInstance:jt.newInstance(gb),extend:gb};function hb(e,t){t.classHierarchy.push("vtkWebGPUOpaquePass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.renderEncoder)t.colorTexture.resize(r.getCanvas().width,r.getCanvas().height),t.depthTexture.resize(r.getCanvas().width,r.getCanvas().height);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"opaquePassColor"}),t.colorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const n=t.colorTexture.createView("opaquePassColorTexture");t.renderEncoder.setColorTextureView(0,n),t.depthFormat="depth32float",t.depthTexture=mb.newInstance({label:"opaquePassDepth"}),t.depthTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:t.depthFormat,usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("opaquePassDepthTexture");t.renderEncoder.setDepthTextureView(a)}t.renderEncoder.attachTextureViews(),e.setCurrentOperation("opaquePass"),n.setRenderEncoder(t.renderEncoder),n.traverse(e)},e.getColorTextureView=()=>t.renderEncoder.getColorTextureViews()[0],e.getDepthTextureView=()=>t.renderEncoder.getDepthTextureView(),e.createRenderEncoder=()=>{t.renderEncoder=wT.newInstance({label:"OpaquePass"}),t.renderEncoder.setPipelineHash("op")}}const vb={renderEncoder:null,colorTexture:null,depthTexture:null};function yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vb,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),hb(e,t)}var Tb={newInstance:jt.newInstance(yb,"vtkWebGPUOpaquePass"),extend:yb};function bb(e,t){t.classHierarchy.push("vtkWebGPUOrderIndependentTranslucentPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r;const o=r.getDevice();if(t.translucentRenderEncoder)t.translucentColorTexture.resizeToMatch(t.colorTextureView.getTexture()),t.translucentAccumulateTexture.resizeToMatch(t.colorTextureView.getTexture());else{e.createRenderEncoder(),e.createFinalEncoder(),t.translucentColorTexture=mb.newInstance({label:"translucentPassColor"}),t.translucentColorTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"rgba16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const n=t.translucentColorTexture.createView("oitpColorTexture");t.translucentRenderEncoder.setColorTextureView(0,n),t.translucentAccumulateTexture=mb.newInstance({label:"translucentPassAccumulate"}),t.translucentAccumulateTexture.create(o,{width:r.getCanvas().width,height:r.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=xT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n")}t.translucentRenderEncoder.setDepthTextureView(t.depthTextureView),t.translucentRenderEncoder.attachTextureViews(),e.setCurrentOperation("translucentPass"),n.setRenderEncoder(t.translucentRenderEncoder),n.traverse(e),e.finalPass(r,n)},e.finalPass=(e,n)=>{t.translucentFinalEncoder.setColorTextureView(0,t.colorTextureView),t.translucentFinalEncoder.attachTextureViews(),t.translucentFinalEncoder.begin(e.getCommandEncoder()),n.scissorAndViewport(t.translucentFinalEncoder),t.fullScreenQuad.prepareAndDraw(t.translucentFinalEncoder),t.translucentFinalEncoder.end()},e.getTextures=()=>[t.translucentColorTexture,t.translucentAccumulateTexture],e.createRenderEncoder=()=>{t.translucentRenderEncoder=wT.newInstance({label:"translucentRender"});const e=t.translucentRenderEncoder.getDescription();e.colorAttachments=[{view:void 0,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:void 0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = computedColor.a;"]).result,t.setCode(n)})),t.translucentRenderEncoder.setPipelineHash("oitpr"),t.translucentRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!1,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one"},alpha:{srcfactor:"one",dstFactor:"one"}}},{format:"r16float",blend:{color:{srcFactor:"zero",dstFactor:"one-minus-src"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createFinalEncoder=()=>{t.translucentFinalEncoder=wT.newInstance({label:"translucentFinal"}),t.translucentFinalEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t.translucentFinalEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t.translucentFinalEncoder.setPipelineHash("oitpf"),t.translucentFinalEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})}}const xb={colorTextureView:null,depthTextureView:null};function Cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xb,n),qv.extend(e,t,n),jt.setGet(e,t,["colorTextureView","depthTextureView"]),bb(e,t)}var Sb={newInstance:jt.newInstance(Cb,"vtkWebGPUOrderIndependentTranslucentPass"),extend:Cb};const{BufferUsage:Ab}=nb,{vtkErrorMacro:Ib}=jt;function wb(e,t){t.classHierarchy.push("vtkWebGPUUniformBuffer"),e.addEntry=(e,n)=>{t._bufferEntryNames.has(e)?Ib(`entry named ${e} already 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o=t.bufferEntries[r];o.packed||(o.packed=!0,o.offset=e,n.push(o),e+=o.sizeInBytes)}t.bufferEntries=n,t._bufferEntryNames.clear();for(let e=0;e<t.bufferEntries.length;e++)t._bufferEntryNames.set(t.bufferEntries[e].name,e);t.sizeInBytes=e,t.sizeInBytes=r*Math.ceil(t.sizeInBytes/r),t.sortDirty=!1},e.sendIfNeeded=e=>{if(!t.UBO){const n={nativeArray:t.Float32Array,usage:Ab.UniformArray,label:t.label};t.UBO=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),t.sendDirty=!1}t.sendDirty&&(e.getHandle().queue.writeBuffer(t.UBO.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes),t.sendDirty=!1),t.sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];a.lastValue!==r&&(i[a.offset/i.BYTES_PER_ELEMENT]=r,t.sendDirty=!0),a.lastValue=r},e.setArray=(n,r)=>{e.sortBufferEntries();const o=t._bufferEntryNames.get(n);if(void 0===o)return void Ib(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];let s=!1;for(let e=0;e<r.length;e++)a.lastValue&&a.lastValue[e]===r[e]||(i[a.offset/i.BYTES_PER_ELEMENT+e]=r[e],s=!0);s&&(t.sendDirty=!0,a.lastValue=[...r])},e.getBindGroupEntry=()=>({resource:{buffer:t.UBO.getHandle()}}),e.getSendTime=()=>t.sendTime.getMTime(),e.getShaderCode=(n,r)=>{e.sortBufferEntries();const o=[`struct ${t.label}Struct\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];o.push(`  ${n.name}: ${n.type},`)}return o.push(`};\n@binding(${n}) @group(${r}) var<uniform> ${t.label}: ${t.label}Struct;`),o.join("\n")}}const Pb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Ob(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Pb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),wb(e,t)}var Rb={newInstance:jt.newInstance(Ob,"vtkWebGPUUniformBuffer"),extend:Ob};const{BufferUsage:Mb}=nb,{vtkErrorMacro:Db}=jt;function Eb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void Db(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void Db(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Vb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function Lb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Vb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Eb(e,t)}var Bb={newInstance:jt.newInstance(Lb,"vtkWebGPUStorageBuffer"),extend:Lb};const Nb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",_b=new Float64Array(16),Fb=new Float64Array(16);function kb(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Uy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Rg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Uy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Uy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Uy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Rg.COMPOSITE_BLEND?l=!0:n===Rg.MAXIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.MINIMUM_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.AVERAGE_INTENSITY_BLEND?a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Rg.ADDITIVE_INTENSITY_BLEND&&(a=Uy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Uy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(_b),b(_b,_b,o);const p=n.getMatrix();m(Fb,p),h(Fb,Fb),T(_b,Fb,_b);const f=d.getWorldToIndex();T(_b,f,_b);const v=d.getDimensions();g(Fb),x(Fb,Fb,[1/v[0],1/v[1],1/v[2]]),T(_b,Fb,_b);for(let t=0;t<16;t++)a[16*e+t]=_b[t];h(_b,_b);for(let t=0;t<4;t++)i[16*e+4*t]=_b[4*t],i[16*e+4*t+1]=_b[4*t+1],i[16*e+4*t+2]=_b[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=tT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=ib.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const Gb={volumes:null,rowLength:1024,lastVolumeLength:0};function Ub(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Gb,n),xT.extend(e,t,n),t.fragmentShaderTemplate=Nb,t.UBO=Rb.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Bb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Bb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),kb(e,t)}var zb={newInstance:jt.newInstance(Ub,"vtkWebGPUVolumePassFSQ"),extend:Ub};const{Representation:Wb}=ds,{BufferUsage:Hb,PrimitiveTypes:jb}=nb,Kb=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],$b="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function qb(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=zb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=xT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=Rb.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Hb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:jb.Triangles,representation:Wb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Hb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+Kb[e][0],l[n++]=r+Kb[e][1],l[n++]=r+Kb[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=wT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=mb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=mb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=mb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=wT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=wT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=wT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const Xb={colorTextureView:null,depthTextureView:null,volumes:null};function Yb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Xb,n),qv.extend(e,t,n),t._mapper=vT.newInstance(),t._mapper.setFragmentShaderTemplate($b),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=gu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),qb(e,t)}var Zb={newInstance:jt.newInstance(Yb,"vtkWebGPUVolumePass"),extend:Yb};function Qb(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Tb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Sb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=Zb.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=wT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=ib.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=xT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=ub.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const Jb={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function ex(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Jb,n),qv.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),Qb(e,t)}var tx={newInstance:jt.newInstance(ex,"vtkForwardPass"),extend:ex};const{VtkDataTypes:nx}=Es;function rx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case nx.UNSIGNED_CHAR:e.format+="8unorm";break;case nx.FLOAT:case nx.UNSIGNED_INT:case nx.INT:case nx.DOUBLE:case nx.UNSIGNED_SHORT:case nx.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=mb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const ox={handle:null,device:null};function ax(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ox,n),jt.obj(e,t),jt.setGet(e,t,["device"]),rx(e,t)}var ix={newInstance:jt.newInstance(ax),extend:ax};class sx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function lx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const cx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function ux(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new sx,t.shaderCache=Uy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=nb.newInstance(),t.bufferManager.setDevice(e),t.textureManager=ix.newInstance(),t.textureManager.setDevice(e),t.pipelines={},lx(e,t)}var dx={newInstance:Et(ux,"vtkWebGPUDevice"),extend:ux};function px(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=mb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=mb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=wT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Uy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const fx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function gx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,fx,n),qv.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),px(e,t)}var mx={newInstance:jt.newInstance(gx,"vtkWebGPUHardwareSelectionPass"),extend:gx};const{SelectionContent:hx,SelectionField:vx}=qp,{FieldAssociations:yx}=ol,{vtkErrorMacro:Tx}=jt;function bx(e){return`${e.propID} ${e.compositeID}`}function xx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Cx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=xx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=xx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Cx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Cx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Cx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Cx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Cx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Sx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Tx("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=ET.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=ET.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Cx(e,[n,t],0,u);if(r){const t=bx(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=qp.newInstance();switch(i.setContentType(hx.INDICES),e){case yx.FIELD_ASSOCIATION_CELLS:i.setFieldType(vx.CELL);break;case yx.FIELD_ASSOCIATION_POINTS:i.setFieldType(vx.POINT);break;default:Tx("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Ax={};function Ix(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ax,n),Gp.extend(e,t,n),t._selectionPass=mx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Sx(e,t)}var wx={newInstance:jt.newInstance(Ix,"vtkWebGPUHardwareSelector"),extend:Ix};const Px=Object.create(null);function Ox(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const Rx={};function Mx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rx,n),t.overrides=Px,tn.extend(e,t,n),Ox(0,t)}var Dx={newInstance:jt.newInstance(Mx,"vtkWebGPUViewNodeFactory"),extend:Mx};const{vtkErrorMacro:Ex}=jt,Vx={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function Lx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Ex("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Ex("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=dx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=qv.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Vx,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=ET.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Ad.fromHalf(r.colorValues[o]),s[n+1]=255*Ad.fromHalf(r.colorValues[o+1]),s[n+2]=255*Ad.fromHalf(r.colorValues[o+2]),s[n+3]=255*Ad.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=wx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Bx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Nx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",xy.extend(e,t,n),t.myFactory=Dx.newInstance(),t.myFactory.registerOverride("vtkRenderWindow",Nx),t.renderPasses[0]=tx.newInstance(),t.selector||(t.selector=wx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),Lx(e,t)}),"vtkWebGPURenderWindow");ev("WebGPU",Nx);const _x=Hv(),Fx={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},kx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Gx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Ux(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Gx(t.container,t.containerStyle||Fx),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=av.newInstance(),t.renderer=Qh.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(_x.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Sv.newInstance(),t.interactor.setInteractorStyle(Uv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Gx(t.controlContainer,t.controlPanelStyle||kx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",(t=>{"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}))},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Wx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Ux(e,t)}var Hx={newInstance:jt.newInstance(Wx),extend:Wx},jx={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:Kx,Scale:$x}=jx,{ScalarMappingTarget:qx}=Pl,{vtkDebugMacro:Xx,vtkErrorMacro:Yx,vtkWarningMacro:Zx}=jt;function Qx(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function Jx(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function eC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];Qx(o,i),Qx(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=Jx(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=Jx(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function tC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return Yx("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return Yx("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===$x.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Do(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===Kx.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===Kx.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===Kx.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===Kx.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===Kx.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===Kx.DIVERGING?(eC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):Yx("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return Yx("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(Yx("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return Yx("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>t.indexedLookup&&e.getSize()?e.getSize():t.tableSize?t.tableSize:16777216,e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void Yx("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):Xx("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void Zx("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===qx.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===qx.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===qx.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===qx.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=Kx[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(Yx(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=Kx.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const nC={clamping:!0,colorSpace:Kx.RGB,hSVWrap:!0,scale:$x.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function rC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","colorSpace","discretize","numberOfValues"]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),tC(e,t)}var oC={newInstance:jt.newInstance(rC,"vtkColorTransferFunction"),extend:rC,...jx},aC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:iC,ScaleModes:sC}=aC,{vtkErrorMacro:lC}=jt;function cC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(iC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(iC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(iC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(iC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(sC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(sC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(sC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(sC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===sC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(lC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case iC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case iC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case iC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case sC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case sC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case sC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const uC={orient:!0,orientationMode:iC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:sC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function dC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,uC,n),nc.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),cC(e,t)}var pC={newInstance:jt.newInstance(dC,"vtkGlyph3DMapper"),extend:dC,...aC};const{vtkErrorMacro:fC}=jt;function gC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(fC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return fC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return fC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return fC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const i={x:n,y:r,midpoint:o,sharpness:a};let s;for(t.nodes.push(i),e.sortAndUpdateRange(),s=0;s<t.nodes.length&&t.nodes[s].x!==n;s++);return s<t.nodes.length?s:-1},e.setNodes=n=>{t.nodes!==n&&(t.nodes=n,e.sortAndUpdateRange())},e.sortAndUpdateRange=()=>{t.nodes.sort(((e,t)=>e.x-t.x)),e.updateRange()||e.modified()},e.updateRange=()=>{const n=t.range.slice(),r=t.nodes.length;return r?(t.range[0]=t.nodes[0].x,t.range[1]=t.nodes[r-1].x):(t.range[0]=0,t.range[1]=0),(n[0]!==t.range[0]||n[1]!==t.range[1])&&(e.modified(),!0)},e.removePoint=n=>{let r;for(r=0;r<t.nodes.length&&t.nodes[r].x!==n;r++);if(r>=t.nodes.length)return-1;const o=r;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addSegment=(n,r,o,a)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=o?t.nodes.splice(e,1):e++;e.addPoint(n,r,.5,0),e.addPoint(o,a,.5,0)},e.getValue=t=>{const n=[];return e.getTable(t,t,1,n),n[0]},e.adjustRange=n=>{if(n.length<2)return 0;const r=e.getRange();r[0]<n[0]?e.addPoint(n[0],e.getValue(n[0])):e.addPoint(n[0],e.getValue(r[0])),r[1]>n[1]?e.addPoint(n[1],e.getValue(n[1])):e.addPoint(n[1],e.getValue(r[1])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return e.sortAndUpdateRange(),1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{const e=t.nodes.length;if(e<2)return-1;let n=t.nodes[1].x-t.nodes[0].x;for(let r=0;r<e-1;r++){const e=t.nodes[r+1].x-t.nodes[r].x;e<n&&(n=e)}return n},e.getTable=function(e,n,r,o){let a,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=0;const l=t.nodes.length;let c=0;0!==l&&(c=t.nodes[l-1].y);let u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const mC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function hC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,mC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),gC(e,t)}var vC={newInstance:jt.newInstance(hC,"vtkPiecewiseFunction"),extend:hC};const{InterpolationType:yC,OpacityMode:TC}=Og,{vtkErrorMacro:bC}=jt;function xC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(bC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=vC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=oC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=vC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return bC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(bC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(yC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(yC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(yC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(yC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const CC={independentComponents:!0,interpolationType:yC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function SC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,CC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:TC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),xC(e,t)}var AC={newInstance:jt.newInstance(SC,"vtkVolumeProperty"),extend:SC,...Og};const{vtkDebugMacro:IC}=jt;function wC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=AC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){IC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const PC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function OC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),wC(e,t)}var RC={newInstance:jt.newInstance(OC,"vtkVolume"),extend:OC};const{BlendMode:MC,FilterMode:DC}=Mg,EC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=vC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function VC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(MC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(MC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(MC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(MC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(MC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(MC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(MC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(DC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(DC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(DC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(DC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Do(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const LC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:MC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:DC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function BC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LC,n),Bs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),VC(e,t)}var NC={newInstance:jt.newInstance(BC,"vtkVolumeMapper"),extend:BC,...EC};const{InterpolationType:_C}=eg,{vtkErrorMacro:FC}=jt;function kC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return FC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(FC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(_C.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(_C.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(_C,t.interpolationType)}const GC={independentComponents:!1,interpolationType:_C.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function UC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,GC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),kC(e,t)}var zC={newInstance:jt.newInstance(UC,"vtkImageProperty"),extend:UC};const{vtkDebugMacro:WC}=jt;function HC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=zC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){WC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const jC={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function KC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),HC(e,t)}var $C={newInstance:jt.newInstance(KC,"vtkImageSlice"),extend:KC};function qC(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const XC={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var YC=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,XC,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),qC(e,t)};function ZC(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:QC,otherStaticMethods:JC}=Ul,{SlicingMode:eS}=sg;function tS(e,t){function n(){let n;switch(t.slicingMode){case eS.X:n=0;break;case eS.Y:n=1;break;case eS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case eS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case eS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case eS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case eS.I:l=Ma(a[0],i[0],i[1]);break;case eS.J:l=Ma(a[1],i[2],i[3]);break;case eS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case eS.I:case eS.J:case eS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case eS.X:e.setSlice(r[0]);break;case eS.Y:e.setSlice(r[1]);break;case eS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(eS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(eS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(eS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(eS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(eS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(eS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case eS.X:n[0]=1;break;case eS.Y:n[1]=1;break;case eS.Z:n[2]=1;break;case eS.I:ea(r,[1,0,0],n);break;case eS.J:ea(r,[0,1,0],n);break;case eS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==eS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case eS.I:r[0]=a,r[1]=a;break;case eS.J:r[2]=a,r[3]=a;break;case eS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case eS.I:a[0]=s-r,a[1]=s+r;break;case eS.J:a[2]=s-r,a[3]=s+r;break;case eS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=ZC(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const nS={slicingMode:eS.NONE,closestIJKAxis:{ijkMode:eS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function rS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nS,n),YC(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),Ul.implementCoincidentTopologyMethods(e,t),tS(e,t)}var oS={newInstance:jt.newInstance(rS,"vtkImageMapper"),extend:rS,...QC,...JC,...sg};const{vtkErrorMacro:aS}=jt;function iS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function sS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void aS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=gu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=mc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Es.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Es.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Es.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),iS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),iS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),iS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),iS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const lS={outputPointsPrecision:js.DEFAULT};function cS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),sS(e,t)}var uS={newInstance:jt.newInstance(cS,"vtkAppendPolyData"),extend:cS};function dS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Nf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=gu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const pS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function fS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),dS(e,t)}var gS={newInstance:jt.newInstance(fS,"vtkConeSource"),extend:fS};function mS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Es.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Es.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Nf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=gu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const hS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function vS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),mS(e,t)}var yS={newInstance:jt.newInstance(vS,"vtkCylinderSource"),extend:vS};function TS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=yS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Bf().rotateZ(-90).apply(a).apply(i);const s=gS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Nf().translate(1-.5*t.tipLength,0,0).apply(c);const u=uS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Nf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Nf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Nf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const bS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function xS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),TS(e,t)}var CS={newInstance:jt.newInstance(xS,"vtkArrowSource"),extend:xS};function SS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Bf().translate(...n).apply(e.getPoints().getData())}function AS(e,t){const n=e.getPoints().getBounds(),r=[0,0,0];r[t]=-n[2*t],Bf().translate(...r).apply(e.getPoints().getData())}function IS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Es.newInstance({name:"color",numberOfComponents:3,values:a}))}function wS(e,t){t.classHierarchy.push("vtkAxesActor");const n=nc.newInstance();e.setMapper(n),e.update=()=>{const e=CS.newInstance({direction:[1,0,0],...t.config}).getOutputData();t.config.recenter?SS(e):AS(e,0),IS(e,...t.xAxisColor);const r=CS.newInstance({direction:[0,1,0],...t.config}).getOutputData();t.config.recenter?SS(r):AS(r,1),IS(r,...t.yAxisColor);const o=CS.newInstance({direction:[0,0,1],...t.config}).getOutputData();t.config.recenter?SS(o):AS(o,2),IS(o,...t.zAxisColor);const a=uS.newInstance();a.setInputData(e),a.addInputData(r),a.addInputData(o),n.setInputConnection(a.getOutputPort())},e.update();const r=jt.debounce(e.update,0),{setConfig:o,setXAxisColor:a,setYAxisColor:i,setZAxisColor:s}=e;e.setConfig=e=>!!o(e)&&(r(),!0),e.setXAxisColor=e=>!!a(e)&&(r(),!0),e.setYAxisColor=e=>!!i(e)&&(r(),!0),e.setZAxisColor=e=>!!s(e)&&(r(),!0)}const PS={config:{recenter:!0,tipResolution:60,tipRadius:.1,tipLength:.2,shaftResolution:60,shaftRadius:.03,invert:!1},xAxisColor:[255,0,0],yAxisColor:[255,255,0],zAxisColor:[0,128,0]};function OS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PS,n),hs.extend(e,t,n),jt.setGet(e,t,["config"]),jt.setGetArray(e,t,["xAxisColor","yAxisColor","zAxisColor"],3,255),wS(e,t)}var RS={newInstance:jt.newInstance(OS,"vtkAxesActor"),extend:OS};const MS="resetcamera",DS="orientation",ES={MODE_RESET_CAMERA:MS,MODE_ORIENTATION:DS,MODE_SAME:"same"};function 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i,s,l;a&&(t.mode===DS?(r[0]=a[0]-a[3],r[1]=a[1]-a[4],r[2]=a[2]-a[5],qo(r),o.setPosition(t.focalPoint[0]+t.distance*r[0],t.focalPoint[1]+t.distance*r[1],t.focalPoint[2]+t.distance*r[2]),o.setFocalPoint(t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),o.setViewUp(a[6],a[7],a[8])):(o.setPosition(a[0],a[1],a[2]),o.setFocalPoint(a[3],a[4],a[5]),o.setViewUp(a[6],a[7],a[8])),t.mode===MS&&t.dstRenderer.resetCamera())},e.delete=Vt((()=>e.setSrcRenderer(null)),e.delete),a()}const LS={mode:DS,focalPoint:[0,0,0],distance:6.8,active:!0};function BS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,LS,n),yt(e,t),At(e,t,["mode","active","srcRenderer","dstRenderer","distance"]),Pt(e,t,["focalPoint"],3,0),VS(e,t)}var NS={newInstance:Et(BS,"vtkCameraSynchronizer"),extend:BS,SynchronizationMode:ES};const _S={},FS={CameraSync:class{constructor(e,t){this.ctx=e,this.behavior=NS.newInstance(this.getProperties(t)),this.behavior.update()}getProperties(e){let{actorBounds:t,srcRenderer:n,dstRenderer:r}=e;return{distance:3.4*Math.max(t[1]-t[0],t[3]-t[2],t[5]-t[4]),focalPoint:[.5*(t[0]+t[1]),.5*(t[2]+t[3]),.5*(t[4]+t[5])],mode:NS.SynchronizationMode.MODE_ORIENTATION,srcRenderer:this.ctx.getInstance(n),dstRenderer:this.ctx.getInstance(r)}}update(e){this.behavior.set(this.getProperties(e)),this.behavior.update()}delete(){this.behavior.delete()}}};var kS=function(e,t,n){const r=e.get("synchronizedViewId").synchronizedViewId;if(!t.behaviors||!r)return;_S[r]||(_S[r]={});const o=_S[r];if(t.behaviors.autoOrientation){const t=e.getRenderers();if(!o.autoOrientationAxes&&2===t.length){let e=null,n=null;for(let r=0;r<t.length;r++){const o=t[r];o.getInteractive()?e=o:n=o}e&&n&&(o.autoOrientationAxes=NS.newInstance({srcRenderer:e,dstRenderer:n}))}o.autoOrientationAxes&&2!==t.length&&(o.autoOrientationAxes.delete(),delete o.autoOrientationAxes)}const a=Object.keys(t.behaviors),i=Object.keys(o);for(let e=0;e<a.length;e++){const r=a[e];if(o[r])o[r].update(t.behaviors[r]);else{const e=t.behaviors[r];FS[e.type]?o[r]=new FS[e.type](n,e):console.log("No mapping for",e)}}for(let e=0;e<i.length;e++){const n=a[e];t.behaviors[n]||(o[n].delete(),delete o[n])}};const GS={},US=/instance:\${([^}]+)}/,zS={},WS=[],HS={},jS={vtkPoints:mc,vtkCellArray:cc,vtkDataArray:Es};function KS(e){return e.map((e=>US.exec(e))).filter((e=>e)).map((e=>e[1]))}function $S(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return e.dependencies&&e.dependencies.forEach((e=>{t.push(e.id),$S(e,t)})),t}function qS(e,t,n,r){if(!t)return Promise.reject(new Error("No instance provided."));const o=GS[e];return 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";
+var base64Str = 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diff --git a/docs/user/_quarto.yml b/docs/user/_quarto.yml
index f4cb564d4..246d7d696 100644
--- a/docs/user/_quarto.yml
+++ b/docs/user/_quarto.yml
@@ -8,15 +8,23 @@ book:
   date: "15/06/2024"
   bread-crumbs: false
   chapters:
-    - index.qmd
-    - Notebooks/1-Meshes.ipynb
-    - Notebooks/2-Variables.ipynb
-    - Notebooks/3-Symbolic_Forms.ipynb
-    - Notebooks/4-Solvers.ipynb
-    - Notebooks/5-Solvers.ipynb
-    - Notebooks/6-Timestepping.ipynb
-    - Notebooks/7-Unsteady_Flow.ipynb
-    - NextSteps.qmd
+    - part: "About UW3"
+      chapters:
+        - index.qmd
+        - NextSteps.qmd
+        - Installation.qmd
+
+    - part: "Notebooks"
+      chapters:
+        - Notebooks/Notebook_Index.ipynb
+        - Notebooks/1-Meshes.ipynb
+        - Notebooks/2-Variables.ipynb
+        - Notebooks/3-Symbolic_Forms.ipynb
+        - Notebooks/4-Solvers.ipynb
+        - Notebooks/5-Solvers.ipynb
+        - Notebooks/6-Timestepping.ipynb
+        - Notebooks/7-Unsteady_Flow.ipynb
+        - Notebooks/8-Particle_Swarms.ipynb
 
   navbar:
     search: true
@@ -47,7 +55,7 @@ format:
     other-links:
       - text: Github Repository
         icon: github
-        href: https://github.com/underworld/underworld3
+        href: https://github.com/underworldcode/underworld3
 
   # pdf:
   #   documentclass: scrreprt
diff --git a/docs/user/index.qmd b/docs/user/index.qmd
index e7b05c53f..a1a0b993c 100644
--- a/docs/user/index.qmd
+++ b/docs/user/index.qmd
@@ -11,55 +11,62 @@ exports:
 
 ```{=html}
 <center>
-<iframe src="Notebooks/html5/stokes_sphere_plot.html" width="500" height="300"></iframe>
+<iframe src="media/pyvista/SphereStreamlinesS.html" width="500" height="300">
+</iframe>
 </center>
+
+<i>
+    Live image: Flow solution in an sphere with prescribed buoyancy
+    and free slip boundaries.
+</i>
+
 ```
 ---
 
 Welcome to `Underworld3`, a mathematically self-describing, finite-element code for geodynamic modelling. This quick-start guide has basic installation instructions and a brief introduction to some of the concepts in the `Underworld3` code.
 
-It's a good first step after you install the code to look at these notebooks. When you are happy that things are working OK, you can move along to look at the full documentation or the benchmarking repository.
+All `Underworld3` source code is released under the LGPL-3 open source licence. This covers all files in `underworld3` constituting the Underworld3 Python module. Notebooks, stand-alone documentation and Python scripts which show how the code is used and run are licensed under the Creative Commons Attribution 4.0 International License.
 
 ## Installation Guide
 
-HERE
-
-
-## Notebooks
-
-Notebook 1 - [Meshes](Notebooks/1-Meshes.html): Introduces the mesh discretisation that we use in `Underworld3` and how you can build one of the pre-defined meshes. This notebook also show you how to use the `pyvista` visualisation tools for `Underworld3` objects. The mesh holds information on the mesh geometry, boundaries and coordinate systems.
+The quickest option is **not to install** anything but click on the rocket icon (top right) to launch our notebook examples on mybinder.org.
 
-Notebook 2 - [Variables](Notebooks/2-Variables.html): Introduces the concept of `meshVariables` in `Underworld3`. These are both data containers and `sympy` symbolic objects. We show you how to inspect a `meshVariable`, set the data values in the `meshVariable` and visualise them.
+If do you want to install the code on your own machine, we recommend using `miniconda` and `mamba`. Create a separate virtual environment and install the code from source. More details on how to do that are in the [Installation Instructions](Installation.qmd)
 
-Notebook 3 - [Symbols](Notebooks/3-Symbolic_Forms.html): `meshVariables` are `sympy` objects
-that can be composed with other symbolic objects and evaluated numerically when required. They can also be differentiated. Most importantly, `sympy` can manipulate expressions, simplify them and cancel terms.
+## Sample Code
 
-Notebook 4 - [Diffusion Solver](Notebooks/4-Solvers.html): Introduces the various solver templates that are available in Underworld, starting with a steady-state diffusion problem. The template requires you to set some constitutive properties and define the unknowns. These are handled through subsitution into symbolic forms and the template equation can be inspected before you need to supply concrete expressions.
+Underworld is designed to be run in the jupyter notebook environment where you can take advantage of jupyter's rich display capabilities to explore the mathematics of your problem, visualise results and query classes or live objects.
 
-Notebook 5 - [Stokes Solver](Notebooks/5-Solvers.html): Stokes equation is a more complicated system of equations to solve. This complexity is mostly hidden when you set the problem up. There are some interesting ways to constrain boundary values which are demonstrated using an annulus mesh (curved, free-slip boundaries) and a $\delta$ function buoyancy source.
+It's a good first step after you install the code to look at the [examples notebooks](Notebooks/Notebook_Index.ipynb). When you are happy that things are working OK, you can move along to look at the full documentation or the benchmarking repository.
 
-Notebook 6 - [Timestepping](Notebooks/6-Timestepping.html): *Coupled* Stokes flow plus thermal advection-diffusion gives a simple convection solver. The timestepping loop is written by hand because usually you will want to do some analysis or output some checkpoints.
+The notebooks introduce the concept of meshing to describe the domain and boundary conditions for a model and progress through Eulerian data containers, symbolic operators (functions, compositions, derivatives, evaluations).
+The examples move on to the template PDE solvers for scalar and vector problems (in this case the Poisson equation and the Stokes equation) and time dependent, coupled problems (a simple convection example in an annulus geometry). They cover constitutive models, boundary conditions and visualisation.
+The final examples deal with Lagrangian data containers (particle swarms), first transporting them in a velocity field, and second, accessing the symbolic forms of their data for use in underworld expressions.
 
-Notebook 7 - Unsteady flow   Using a passive swarm to track the pattern of flow developing in a pipe after an impulsive application of a boundary condition at the inflow. Particles need to be added to the passive swarm close to the inflow at each timestep.
+## Learn More
 
-Notebook 8 - Swarm variables - exploring how they work for specifying material properties with a swarm used to determine element viscosity. We learn how to use swarm variables in expressions generally and for boundary conditions.
+`underworld3` is a research code under continual development and the examples in the notebooks provided with the code only touch upon the extent of its capabilities. Read the [Next Steps](NextSteps.qmd) page to find out how to learn more about `underworld3`, how to get in touch with the development team, and how to become part of the **underworld community**.
 
 
+## Acknowledgements
 
+The Underworld codes represent more than two decades of programming effort by many developers including a wide community of contributors from the open-source community.
 
+As a community-driven development team, we first acknowledge many contributions made through pull-requests to our repositories and direct submission of codes and examples. The underworldcode organisation on GitHub records all such contributions to the software and the underworld community on GitHub tracks the exchange of information and models.
 
-## Next Steps
+Direct funding support has been provided by:
 
-Some of the cool things about UW3 that you can do with a bit of effort (at the moment)
+ - AuScope provides direct support for the core development team behind the underworld codes and the underworld cloud suite of tools. AuScope is funded by the Australian Government through the National Collaborative Research Infrastructure Strategy, NCRIS.
 
-  - gmsh (anything - Pramoda's work)
-  - deformed meshes, interesting boundary conditions
-  - free surfaces, relaxation etc
-  -
+ - The development and testing of our codes is also supported by computational resources provided by the Australian Government through the National Computing Infrastructure (NCI) under the National Computational Merit Allocation Scheme.
 
-## Documentation
+ - The Australian Research Council (ARC) supported the development of novel algorithms, computational methods and applications under the Discovery Project and Linkage Project programs. AuScope funding was used to make these methods widely and freely available in the underworld codes. Direct support for Underworld was provided by ARC Industrial Transformation Research Hub Program (The Basin Genesis Hub)
 
+In-kind support has also come from the institution that host Underworld developers including: the Australian National University, Monash University, The University of Melbourne, The University of Sydney and the CSIRO.
 
+## Licensing and re-use
 
+All of our software is released under open source licence and all documentation and worked examples are released with a creative commons licence. We are passionate about having our software used and code reuse is strongly encouraged in the interest of scientific reproducibility and replicability.
+The developers are active researchers and scientists whose livelihood depends  on their creative talents and the acknowledgement of their work. Please respect the effort that goes into developing software and tutorials by citing our work and collaborating on publication with new applications of our software when you can.
 
 ## References
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u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return 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ts={newInstance:jt.newInstance(es,"vtkProp3D"),extend:es};const ns={FLAT:0,GOURAUD:1,PHONG:2},rs={POINTS:0,WIREFRAME:1,SURFACE:2};var os={Shading:ns,Representation:rs,Interpolation:ns};const{Representation:as,Interpolation:is}=os;function ss(e){return()=>jt.vtkErrorMacro(`vtkProperty::${e} - NOT IMPLEMENTED`)}function ls(e,t){t.classHierarchy.push("vtkProperty"),e.setColor=(n,r,o)=>{Array.isArray(n)?t.color[0]===n[0]&&t.color[1]===n[1]&&t.color[2]===n[2]||(t.color[0]=n[0],t.color[1]=n[1],t.color[2]=n[2],e.modified()):t.color[0]===n&&t.color[1]===r&&t.color[2]===o||(t.color[0]=n,t.color[1]=r,t.color[2]=o,e.modified()),e.setDiffuseColor(t.color),e.setAmbientColor(t.color),e.setSpecularColor(t.color)},e.computeCompositeColor=ss("ComputeCompositeColor"),e.getColor=()=>{let e=0;t.ambient+t.diffuse+t.specular>0&&(e=1/(t.ambient+t.diffuse+t.specular));for(let 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r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof 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t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var 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Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. 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ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const g=t.numberOfColorsInRange+3,m=t.numberOfColorsInRange+2,h=r?[Math.min(Math.ceil(g/n**0),n),Math.min(Math.ceil(g/n**1),n),Math.min(Math.ceil(g/n**2),n)]:[m,2,1],v=h[0]*h[1]*h[2],y=new Float64Array(v);y.fill(NaN);const T=t.numberOfColorsInRange,b=T+2,x=[0,0,0],C=a[0],S=a[1]-a[0];for(let e=0;e<b;++e){const t=C+S*(e-1)/(T-1);y[(c=h,(l=x)[0]+c[0]*(l[1]+c[1]*l[2]))]=i?10**t:t,ec(x,h)}const A=Ds.newInstance({numberOfComponents:1,values:y}),I=t.lookupTable.mapScalars(A,t.colorMode,0);t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setDimensions(h),t.colorTextureMap.getPointData().setScalars(I),t.lookupTable.setAlpha(s)}var l,c;const u=t.lookupTable.getVectorMode()===Zl.MAGNITUDE&&n.getNumberOfComponents()>1?-1:t.lookupTable.getVectorComponent();t.colorCoordinates=function(e,t,n,r,o,a,i){const s=new Array(arguments.length);for(let e=0;e<arguments.length;++e){const t=arguments[e];s[e]=t.getMTime?.()??t}const l=s.join("/"),c=nc.get(e);if(c&&c.stringHash===l)return c.textureCoordinates;const u=(n[1]-n[0])/(r-1),[d,p]=[n[0]-u,n[1]+u],f=d-.5*u,g=1/(p-d+u),m=d,h=(r+1)/(p-d),v=e.getData(),y=e.getNumberOfTuples(),T=e.getNumberOfComponents(),b=t<0||t>=T,x=o[2]<=1?2:3,C=Ds.newInstance({numberOfComponents:x,values:new Float32Array(y*x)}),S=C.getData(),A=[0,0,0];tc(A,r+2,o);let I=0,w=0;const P=[.5,.5,.5];for(let e=0;e<y;++e){let e;if(b){let t=0;for(let e=0;e<T;++e){const n=v[I+e];t+=n*n}e=Math.sqrt(t)}else e=v[I+t];if(I+=T,a&&(e=Dl.applyLogScale(e,n,n)),_a(e))P[0]=A[0],P[1]=A[1],P[2]=A[2];else if(i){let t=(e-m)*h;t<1?t=0:t>r&&(t=r+1),tc(P,t,o)}else{P[1]=.49;const t=(e-f)*g;P[0]=t>1e3?1e3:t<-1e3?-1e3:t}for(let e=0;e<x;++e)S[w++]=P[e]}return nc.set(e,{stringHash:l,textureCoordinates:C}),C}(n,u,a,t.numberOfColorsInRange,t.colorTextureMap.getDimensions(),i,r)},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const o=e.getLookupTable();return!o||(o.build(),o.areScalarsOpaque(r,t.colorMode,-1))},e.canUseTextureMapForColoring=(e,n)=>!((!n||t.colorMode===ql.DIRECT_SCALARS)&&(!t.interpolateScalarsBeforeMapping||t.lookupTable&&t.lookupTable.getIndexedLookup()||!e||t.colorMode===ql.DEFAULT&&e.getDataType()===Ql.UNSIGNED_CHAR||t.colorMode===ql.DIRECT_SCALARS)),e.clearColorArrays=()=>{t.colorMapColors=null,t.colorCoordinates=null,t.colorTextureMap=null},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}},e.acquireInvertibleLookupTable=Jl("AcquireInvertibleLookupTable"),e.valueToColor=Jl("ValueToColor"),e.colorToValue=Jl("ColorToValue"),e.useInvertibleColorFor=Jl("UseInvertibleColorFor"),e.clearInvertibleColor=Jl("ClearInvertibleColor"),e.processSelectorPixelBuffers=(e,n)=>{if(!e||!t.selectionWebGLIdsToVTKIds||!t.populateSelectionSettings)return;const r=e.getRawPixelBuffer(Wl.ID_LOW24),o=e.getRawPixelBuffer(Wl.ID_HIGH24),a=e.getCurrentPass(),i=e.getFieldAssociation();let s=null;i===jl.FIELD_ASSOCIATION_POINTS?s=t.selectionWebGLIdsToVTKIds.points:i===jl.FIELD_ASSOCIATION_CELLS&&(s=t.selectionWebGLIdsToVTKIds.cells),s&&n.forEach((t=>{if(a===Wl.ID_LOW24){let n=0;o&&(n+=o[t],n*=256),n+=r[t+2],n*=256,n+=r[t+1],n*=256,n+=r[t];const a=s[n],i=e.getPixelBuffer(Wl.ID_LOW24);i[t]=255&a,i[t+1]=(65280&a)>>8,i[t+2]=(16711680&a)>>16}else if(a===Wl.ID_HIGH24&&o){let n=0;n+=o[t],n*=256,n+=r[t],n*=256,n+=r[t+1],n*=256,n+=r[t+2];const a=s[n];e.getPixelBuffer(Wl.ID_HIGH24)[t]=(4278190080&a)>>24}}))}}const oc={colorMapColors:null,areScalarsMappedFromCells:!1,static:!1,lookupTable:null,scalarVisibility:!0,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,fieldDataTupleId:-1,populateSelectionSettings:!0,selectionWebGLIdsToVTKIds:null,interpolateScalarsBeforeMapping:!1,colorCoordinates:null,colorTextureMap:null,numberOfColorsInRange:0,forceCompileOnly:0,useInvertibleColors:!1,invertibleScalars:null,customShaderAttributes:[]};function ac(e,t){let n=arguments.length>2&&void 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0===t.numberOfCells||n?(t.cellSizes?t.numberOfCells=t.cellSizes.length:t.numberOfCells=lc(e.getData()),t.numberOfCells):t.numberOfCells,e.getCellSizes=n=>void 0===t.cellSizes||n?(t.cellSizes=sc(e.getData()),t.cellSizes):t.cellSizes,e.resize=r=>{const o=e.getNumberOfTuples();n.resize(r);const a=e.getNumberOfTuples();a<o&&(0===a?(t.numberOfCells=0,t.cellSizes=[]):(t.numberOfCells=void 0,t.cellSizes=void 0))},e.setData=e=>{n.setData(e,1),t.numberOfCells=void 0,t.cellSizes=void 0},e.getCell=e=>{let n=e;const r=t.values[n++];return t.values.subarray(n,n+r)},e.insertNextCell=n=>{const r=e.getNumberOfCells();return e.insertNextTuples([n.length,...n]),++t.numberOfCells,null!=t.cellSizes&&t.cellSizes.push(n.length),r}}function dc(e){return{empty:!0,numberOfComponents:1,dataType:ys.UNSIGNED_INT,...e}}function pc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Ds.extend(e,t,dc(n)),uc(e,t)}var 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h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[1]<0&&o[0]<0)for(T=Yo(e,c),b=eu.distanceToLine(e,c,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=c):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[0]<0){const t=eu.distanceToLine(e,l,c,n);a.dist2=t.distance}else if(o[1]<0){const t=eu.distanceToLine(e,c,u,n);a.dist2=t.distance}else if(o[2]<0){const t=eu.distanceToLine(e,l,u,n);a.dist2=t.distance}a.evaluation=0}return a},e.evaluateLocation=(e,n,r)=>{const o=[],a=[],i=[];t.points.getPoint(0,o),t.points.getPoint(1,a),t.points.getPoint(2,i);const s=1-e[0]-e[1];for(let t=0;t<3;t++)n[t]=o[t]*s+a[t]*e[0]+i[t]*e[1];r[0]=s,r[1]=e[0],r[2]=e[1]},e.getParametricDistance=e=>{let t,n=0;const r=[];r[0]=e[0],r[1]=e[1],r[2]=1-e[0]-e[1];for(let e=0;e<3;e++)t=r[e]<0?-r[e]:r[e]>1?r[e]-1:0,t>n&&(n=t);return n}}const cu={};function uu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cu,n),Sc.extend(e,t,n),lu(e,t)}var du={newInstance:jt.newInstance(uu,"vtkTriangle"),extend:uu,...su};const pu=["verts","lines","polys","strips"],{vtkWarningMacro:fu}=jt,gu={[Ec]:eu,[Dc]:eu,[Vc]:du};function mu(e,t){t.classHierarchy.push("vtkPolyData"),pu.forEach((n=>{e[`getNumberOf${function(e){return e.replace(/(?:^\w|[A-Z]|\b\w)/g,(e=>e.toUpperCase())).replace(/\s+/g,"")}(n)}`]=()=>t[n].getNumberOfCells(),t[n]?t[n]=We(t[n]):t[n]=fc.newInstance()})),e.getNumberOfCells=()=>pu.reduce(((e,n)=>e+t[n].getNumberOfCells()),0);const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),pu.forEach((n=>{t[n]=fc.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))},e.buildCells=()=>{const n=e.getNumberOfVerts(),r=e.getNumberOfLines(),o=e.getNumberOfPolys(),a=e.getNumberOfStrips(),i=n+r+o+a,s=new Uint8Array(i);let l=s;const c=new Uint32Array(i);let u=c;if(n){let e=0;t.verts.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>1?Mc:Rc,e+=t+1})),u=u.subarray(n),l=l.subarray(n)}if(r){let e=0;t.lines.getCellSizes().forEach(((t,n)=>{u[n]=e,l[n]=t>2?Dc:Ec,1===t&&fu("Building VTK_LINE ",n," with only one point, but VTK_LINE needs at least two points. Check the input."),e+=t+1})),u=u.subarray(r),l=l.subarray(r)}if(o){let e=0;t.polys.getCellSizes().forEach(((t,n)=>{switch(u[n]=e,t){case 3:l[n]=Vc;break;case 4:l[n]=Nc;break;default:l[n]=Bc}t<3&&fu("Building VTK_TRIANGLE ",n," with less than three points, but VTK_TRIANGLE needs at least three points. Check the input."),e+=t+1})),u+=u.subarray(o),l+=l.subarray(o)}if(a){let e=0;l.fill(Lc,0,a),t.strips.getCellSizes().forEach(((t,n)=>{u[n]=e,e+=t+1}))}t.cells=jc.newInstance(),t.cells.setCellTypes(i,s,c)},e.buildLinks=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;void 0===t.cells&&e.buildCells(),t.links=Pc.newInstance(),n>0?t.links.allocate(n):t.links.allocate(e.getPoints().getNumberOfPoints()),t.links.buildLinks(e)},e.getCellType=e=>t.cells.getCellType(e),e.getCellPoints=n=>{const r=e.getCellType(n);let o=null;switch(r){case Rc:case Mc:o=t.verts;break;case Ec:case Dc:o=t.lines;break;case Vc:case Nc:case Bc:o=t.polys;break;case Lc:o=t.strips;break;default:return o=null,{type:0,cellPointIds:null}}const a=t.cells.getCellLocation(n);return{cellType:r,cellPointIds:o.getCell(a)}},e.getPointCells=e=>t.links.getCells(e),e.getCellEdgeNeighbors=(e,n,r)=>{const o=t.links.getLink(n),a=t.links.getLink(r);return o.cells.filter((t=>t!==e&&-1!==a.cells.indexOf(t)))},e.getCell=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=e.getCellPoints(t),o=n||gu[r.cellType].newInstance();return o.initialize(e.getPoints(),r.cellPointIds),o}}const hu={};function vu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hu,n),ou.extend(e,t,n),jt.get(e,t,["cells","links"]),jt.setGet(e,t,["verts","lines","polys","strips"]),mu(e,t)}var yu={newInstance:jt.newInstance(vu,"vtkPolyData"),extend:vu};function Tu(e,t){t.classHierarchy.push("vtkTexture"),e.imageLoaded=()=>{t.image.removeEventListener("load",e.imageLoaded),t.imageLoaded=!0,e.modified()},e.setJsImageData=n=>{t.jsImageData!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.canvas=null),t.jsImageData=n,t.imageLoaded=!0,e.modified())},e.setCanvas=n=>{t.canvas!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.image=null,t.jsImageData=null),t.canvas=n,e.modified())},e.setImage=n=>{t.image!==n&&(null!==n&&(e.setInputData(null),e.setInputConnection(null),t.canvas=null,t.jsImageData=null),t.image=n,t.imageLoaded=!1,n.complete?e.imageLoaded():n.addEventListener("load",e.imageLoaded),e.modified())},e.getDimensionality=()=>{let n=0,r=0,o=1;if(e.getInputData()){const t=e.getInputData();n=t.getDimensions()[0],r=t.getDimensions()[1],o=t.getDimensions()[2]}return t.jsImageData&&(n=t.jsImageData.width,r=t.jsImageData.height),t.canvas&&(n=t.canvas.width,r=t.canvas.height),t.image&&(n=t.image.width,r=t.image.height),(n>1)+(r>1)+(o>1)},e.getInputAsJsImageData=()=>{if(!t.imageLoaded||e.getInputData())return null;if(t.jsImageData)return t.jsImageData();if(t.canvas)return t.canvas.getContext("2d").getImageData(0,0,t.canvas.width,t.canvas.height);if(t.image){const e=document.createElement("canvas");e.width=t.image.width,e.height=t.image.height;const n=e.getContext("2d");return n.translate(0,e.height),n.scale(1,-1),n.drawImage(t.image,0,0,t.image.width,t.image.height),n.getImageData(0,0,e.width,e.height)}return null}}const bu={image:null,canvas:null,jsImageData:null,imageLoaded:!1,repeat:!1,interpolate:!1,edgeClamp:!1,mipLevel:0,resizable:!1};function xu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,bu,n),jt.obj(e,t),jt.algo(e,t,6,0),jt.get(e,t,["canvas","image","jsImageData","imageLoaded","resizable"]),jt.setGet(e,t,["repeat","edgeClamp","interpolate","mipLevel"]),Tu(e,t)}const Cu={generateMipmaps:(e,t,n,r)=>{const o=[1,2,1],a=e.length/(t*n);let i=t,s=n,l=e;const c=[l];for(let e=0;e<r;e++){const e=[...l];i/=2,s/=2,l=new Uint8ClampedArray(i*s*a);const t=a*i;let n=0;for(let r=0;r<l.length;r+=a){r%t==0&&(n+=2*a*i);for(let o=0;o<a;o++){let i=e[n+o];i+=e[n+a+o],i+=e[n-2*t+o],i+=e[n-2*t+a+o],i/=4,l[r+o]=i}n+=2*a}let r=[...l];for(let e=0;e<l.length;e+=a)for(let n=0;n<a;n++){let i=-(o.length-1)/2,s=4,c=0;for(let l=0;l<o.length;l++){let u=e+n+i*a;const d=u%t-(e+n)%t;d>a&&(u+=t),d<-a&&(u-=t),r[u]?c+=r[u]*o[l]:s-=o[l],i+=1}l[e+n]=c/s}r=[...l];for(let e=0;e<l.length;e+=a)for(let n=0;n<a;n++){let a=-(o.length-1)/2,i=4,s=0;for(let l=0;l<o.length;l++){const c=e+n+a*t;r[c]?s+=r[c]*o[l]:i-=o[l],a+=1}l[e+n]=s/i}c.push(l)}return c}};var Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible 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i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * normalMC;"]).result),e.Vertex=n}n.replaceShaderNormal(e,r,o)},e.replaceShaderColor=(e,r,o)=>{if(t.hardwareSupport&&t.renderable.getColorArray()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity"];a&&(i=i.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let s=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];a&&(s=s.concat(["  vec3 specularColor;","  float specularPower;"])),s=s.concat(["  opacity = opacityUniform;"]),a&&(s=s.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),t.drawingEdges||(i=i.concat(["varying vec4 vertexColorVSOutput;"]),n=gd.substitute(n,"//VTK::Color::Dec",["attribute vec4 gColor;","varying vec4 vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const 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input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const 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o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader VAO."))),n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),r.getIsIdentity())o.isUniformUsed("MCVCMatrix")&&o.setUniformMatrix("MCVCMatrix",i.wcvc),o.isUniformUsed("normalMatrix")&&o.setUniformMatrix3x3("normalMatrix",i.normalMatrix);else{const e=t.openGLActor.getKeyMatrices();if(o.isUniformUsed("MCVCMatrix")){const t=new Float64Array(16);T(t,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",t)}if(o.isUniformUsed("normalMatrix")){const t=new Float64Array(9);ve(t,i.normalMatrix,e.normalMatrix),o.setUniformMatrix3x3("normalMatrix",t)}}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=3;u+=4;let d=null,p=0;a.setColorBOStride(4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow),o&&(p=o.getNumberOfComponents(),a.setColorOffset(4),d=o.getData(),a.setColorBOStride(8)),a.setColorComponents(p),a.setStride(28);const f=new Float32Array(7*l*12),g=new Uint8Array(12*l*(d?8:4));let m=null,h=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(m=i.getArray(t.renderable.getScaleArray()).getData()),null!=t.renderable.getOrientationArray()&&i.hasArray(t.renderable.getOrientationArray())?h=i.getArray(t.renderable.getOrientationArray()).getData():Mm(["Error setting orientationArray.\n","You have to specify the stick orientation"]);const v=[0,1,3,0,3,2,2,3,5,2,5,4];let y=0,T=0,b=0,x=0;for(let e=0;e<l;++e){let n=t.renderable.getLength(),r=t.renderable.getRadius();m&&(n=m[2*e],r=m[2*e+1]);for(let t=0;t<v.length;++t)y=3*e,f[b++]=c[y++],f[b++]=c[y++],f[b++]=c[y++],y=3*e,f[b++]=h[y++]*n,f[b++]=h[y++]*n,f[b++]=h[y++]*n,f[b++]=r,g[x++]=v[t]%2*255,g[x++]=v[t]>=4?255:0,g[x++]=v[t]>=2?255:0,g[x++]=255,T=e*p,d&&(g[x++]=d[T],g[x++]=d[T+1],g[x++]=d[T+2],g[x++]=d[T+3])}a.setElementCount(b/7),a.upload(f,$u.ARRAY_BUFFER),a.getColorBO().upload(g,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Dm={};const Vm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dm,n),sp.extend(e,t,n),Em(e,t)}),"vtkOpenGLStickMapper");rn("vtkStickMapper",Vm);const Lm=[];Lm["-".charCodeAt(0)]=62,Lm["_".charCodeAt(0)]=63;const Bm="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";for(let e=0;e<Bm.length;e++)Lm[Bm.charCodeAt(e)]=e;function Nm(e){return void 0!==Lm[e.charCodeAt(0)]}function _m(e,t,n,r){const{start:o,count:a}=t,i=a%4,s=Math.floor(a/4);let l=o,c=null,u=n;for(let t=0;t<s;t++){for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<18;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<12;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<6;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)],r[u++]=c>>16&255,r[u++]=c>>8&255,r[u++]=255&c}switch(i){case 3:for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<10;!Nm(e[l]);)l++;for(c|=Lm[e.charCodeAt(l++)]<<4;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)]>>2,r[u++]=c>>8&255,r[u++]=255&c;break;case 2:for(;!Nm(e[l]);)l++;for(c=Lm[e.charCodeAt(l++)]<<2;!Nm(e[l]);)l++;c|=Lm[e.charCodeAt(l++)]>>4,r[u++]=255&c;break;case 1:throw new Error("BASE64: remain 1 should not happen")}return u}function Fm(e,t,n){const r=(e<<16)+(t<<8)+n;return Bm[r>>18]+Bm[r>>12&63]+Bm[r>>6&63]+Bm[63&r]}function km(e){const t=new Uint8Array(e),n=e.byteLength%3,r=e.byteLength-n,o=Array(r/3);for(let e=0;e<o.length;e++){const n=3*e;o[e]=Fm(t[n],t[n+1],t[n+2])}if(n>0){const 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r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let a=e.normalizedDisplayToProjection(t,n,r);return a=e.projectionToView(a[0],a[1],a[2],o),e.viewToWorld(a[0],a[1],a[2])},e.worldToNormalizedDisplay=(t,n,r,o)=>{let a=e.worldToView(t,n,r);return a=e.viewToProjection(a[0],a[1],a[2],o),e.projectionToNormalizedDisplay(a[0],a[1],a[2])},e.viewToWorld=(e,n,r)=>{if(null===t.activeCamera)return tv("ViewToWorld: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();h(o,o),m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.projectionToView=(e,n,r,o)=>{if(null===t.activeCamera)return tv("ProjectionToView: no active camera, cannot compute projection to view, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);h(a,a),m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.worldToView=(e,n,r)=>{if(null===t.activeCamera)return tv("WorldToView: no active camera, cannot compute view to world, returning 0,0,0"),[0,0,0];const o=t.activeCamera.getViewMatrix();m(o,o);const a=new Float64Array([e,n,r]);return Vn(a,a,o),a},e.viewToProjection=(e,n,r,o)=>{if(null===t.activeCamera)return tv("ViewToProjection: no active camera, cannot compute view to projection, returning 0,0,0"),[0,0,0];const a=t.activeCamera.getProjectionMatrix(o,-1,1);m(a,a);const i=new Float64Array([e,n,r]);return Vn(i,i,a),i},e.computeVisiblePropBounds=()=>{t.allBounds[0]=ki.INIT_BOUNDS[0],t.allBounds[1]=ki.INIT_BOUNDS[1],t.allBounds[2]=ki.INIT_BOUNDS[2],t.allBounds[3]=ki.INIT_BOUNDS[3],t.allBounds[4]=ki.INIT_BOUNDS[4],t.allBounds[5]=ki.INIT_BOUNDS[5];let r=!0;e.invokeEvent(n);for(let e=0;e<t.props.length;++e){const n=t.props[e];if(n.getVisibility()&&n.getUseBounds()){const e=n.getBounds();e&&Ra(e)&&(r=!1,e[0]<t.allBounds[0]&&(t.allBounds[0]=e[0]),e[1]>t.allBounds[1]&&(t.allBounds[1]=e[1]),e[2]<t.allBounds[2]&&(t.allBounds[2]=e[2]),e[3]>t.allBounds[3]&&(t.allBounds[3]=e[3]),e[4]<t.allBounds[4]&&(t.allBounds[4]=e[4]),e[5]>t.allBounds[5]&&(t.allBounds[5]=e[5]))}}return r&&(Oa(t.allBounds),ev("Can't compute bounds, no 3D props are visible")),t.allBounds},e.resetCamera=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const r=n||e.computeVisiblePropBounds(),a=[0,0,0];if(!Ra(r))return ev("Cannot reset camera!"),!1;let i=null;if(!e.getActiveCamera())return tv("Trying to reset non-existent camera"),!1;i=t.activeCamera.getViewPlaneNormal(),t.activeCamera.setViewAngle(30),a[0]=(r[0]+r[1])/2,a[1]=(r[2]+r[3])/2,a[2]=(r[4]+r[5])/2;let s=r[1]-r[0],l=r[3]-r[2],c=r[5]-r[4];s*=s,l*=l,c*=c;let u=s+l+c;u=0===u?1:u,u=.5*Math.sqrt(u);const d=Oo(t.activeCamera.getViewAngle()),p=u,f=u/Math.sin(.5*d),g=t.activeCamera.getViewUp();return Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return a[1]-a[0]<i&&(i=i-a[1]+a[0],a[1]+=i/2,a[0]-=i/2),a[0]<0&&(a[0]=0),a[0]=.99*a[0]-(a[1]-a[0])*t.clippingRangeExpansion,a[1]=1.01*a[1]+(a[1]-a[0])*t.clippingRangeExpansion,a[0]=a[0]>=a[1]?.01*a[1]:a[0],t.nearClippingPlaneTolerance||(t.nearClippingPlaneTolerance=.01),a[0]<t.nearClippingPlaneTolerance*a[1]&&(a[0]=t.nearClippingPlaneTolerance*a[1]),t.activeCamera.setClippingRange(a[0],a[1]),e.invokeEvent(r),!1},e.setRenderWindow=e=>{e!==t._renderWindow&&(t._vtkWindow=e,t._renderWindow=e)},e.visibleActorCount=()=>t.props.filter((e=>e.getVisibility())).length,e.visibleVolumeCount=e.visibleActorCount,e.getMTime=()=>{let e=t.mtime;const n=t.activeCamera?t.activeCamera.getMTime():0;n>e&&(e=n);const r=t._createdLight?t._createdLight.getMTime():0;return r>e&&(e=r),e},e.getTransparent=()=>!!t.preserveColorBuffer,e.isActiveCameraCreated=()=>!!t.activeCamera}const av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId detected"),o.set(n.pointerId,{pointerId:n.pointerId,position:s(n)}),n.pointerType){case"pen":case"touch":e.handleTouchStart(n);break;default:e.handleMouseDown(n)}},e.handlePointerUp=n=>{if(o.has(n.pointerId))switch(t.preventDefaultOnPointerUp&&Pv(n),o.delete(n.pointerId),t.container.releasePointerCapture(n.pointerId),n.pointerType){case"pen":case"touch":e.handleTouchEnd(n);break;default:e.handleMouseUp(n)}},e.handlePointerCancel=t=>{if(o.has(t.pointerId))switch(o.delete(t.pointerId),t.pointerType){case"pen":case"touch":e.handleTouchEnd(t);break;default:e.handleMouseUp(t)}},e.handlePointerMove=t=>{switch(o.has(t.pointerId)&&(o.get(t.pointerId).position=s(t)),t.pointerType){case"pen":case"touch":e.handleTouchMove(t);break;default:e.handleMouseMove(t)}},e.handleMouseDown=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonPressEvent(n);break;case 1:e.middleButtonPressEvent(n);break;case 2:e.rightButtonPressEvent(n);break;default:xv(`Unknown mouse button pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an animation`)}},e.switchToXRAnimation=()=>{t.animationRequest&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null),t.xrAnimation=!0},e.returnFromXRAnimation=()=>{t.xrAnimation=!1,0!==r.size&&(t.recentAnimationFrameRate=10,t.animationRequest=requestAnimationFrame(e.handleAnimation))},e.updateXRGamepads=(n,r,o)=>{n.inputSources.forEach((n=>{const a=null==n.gripSpace?null:r.getPose(n.gripSpace,o),i=null==n.gripSpace?null:r.getPose(n.targetRaySpace,o),s=n.gamepad,l=n.handedness;if(s){s.index in t.lastGamepadValues||(t.lastGamepadValues[s.index]={left:{buttons:{}},right:{buttons:{}},none:{buttons:{}}});for(let r=0;r<s.buttons.length;++r)r in t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const r={...Cv(n),...l(n),position:s(n),deviceType:u(n)};0===t.wheelTimeoutID&&(a=Math.abs(r.spinY)>=.3?Math.abs(r.spinY):1),r.spinY/=a,0===t.wheelTimeoutID?(e.startMouseWheelEvent(r),e.mouseWheelEvent(r)):(e.mouseWheelEvent(r),clearTimeout(t.wheelTimeoutID)),t.mouseScrollDebounceByPass?(e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0):t.wheelTimeoutID=setTimeout((()=>{e.extendAnimation(600),e.endMouseWheelEvent(),t.wheelTimeoutID=0}),200)},e.handleMouseUp=t=>{const n={...l(t),position:s(t),deviceType:u(t)};switch(t.button){case 0:e.leftButtonReleaseEvent(n);break;case 1:e.middleButtonReleaseEvent(n);break;case 2:e.rightButtonReleaseEvent(n);break;default:xv(`Unknown mouse button released: ${t.button}`)}},e.handleTouchStart=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Ov(o);if(2===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}e.recognizeGesture("TouchStart",t)}else if(1===r.length){const t={...l(Av),position:s(n),deviceType:u(n)};e.leftButtonPressEvent(t)}},e.handleTouchMove=n=>{const r=[...o.values()];if(t.recognizeGestures&&r.length>1){const t=Ov(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.mouseMoveEvent(t)}},e.handleTouchEnd=n=>{const r=[...o.values()];if(t.recognizeGestures)if(0===r.length){const t={...l(Av),position:s(n),deviceType:u(n)};e.leftButtonReleaseEvent(t)}else if(1===r.length){const t=Ov(o);e.recognizeGesture("TouchEnd",t);const a={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonPressEvent(a)}else{const t=Ov(o);e.recognizeGesture("TouchMove",t)}else if(1===r.length){const t={...l(Av),position:r[0].position,deviceType:u(n)};e.leftButtonReleaseEvent(t)}},e.setView=n=>{t._view!==n&&(t._view=n,t._view.getRenderable().setInteractor(e),e.modified())},e.getFirstRenderer=()=>t._view?.getRenderable()?.getRenderersByReference()?.[0],e.findPokedRenderer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;if(!t._view)return null;const r=t._view?.getRenderable()?.getRenderers();if(!r||0===r.length)return null;r.sort(((e,t)=>e.getLayer()-t.getLayer()));let o=null,a=null,i=null,s=r.length;for(;s--;){const l=r[s];if(t._view.isInViewport(e,n,l)&&l.getInteractive()){i=l;break}null===o&&l.getInteractive()&&(o=l),null===a&&t._view.isInViewport(e,n,l)&&(a=l)}return null===i&&(i=o),null===i&&(i=a),null==i&&(i=r[0]),i},e.render=()=>{e.isAnimating()||t.inRender||f()},wv.forEach((n=>{const r=n.charAt(0).toLowerCase()+n.slice(1);e[`${r}Event`]=r=>{if(!t.enabled)return;if(!e.getCurrentRenderer())return void Sv("\n          Can not forward events without a current renderer on the interactor.\n        ");const o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;t.deleted||(t._currentParent=r,t.preDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})),t.delegates.forEach((t=>{t.traverse(n,e)})),t.postDelegateOperations.forEach((t=>{e.setCurrentOperation(t),n.traverse(e)})))}}const ty={delegates:[],currentOperation:null,preDelegateOperations:[],postDelegateOperations:[],currentParent:null};function ny(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ty,n),jt.obj(e,t),jt.get(e,t,["currentOperation"]),jt.setGet(e,t,["delegates","_currentParent","preDelegateOperations","postDelegateOperations"]),jt.moveToProtected(e,t,["currentParent"]),ey(e,t)}var ry={newInstance:jt.newInstance(ny,"vtkRenderPass"),extend:ny};const{Representation:oy}=ds,{vtkErrorMacro:ay}=jt;function iy(e){const t=gd.substitute(e.Fragment,"//VTK::RenderPassFragmentShader::Impl","\n      float weight = gl_FragData[0].a * pow(max(1.1 - gl_FragCoord.z, 0.0), 2.0);\n      gl_FragData[0] = vec4(gl_FragData[0].rgb*weight, gl_FragData[0].a);\n      gl_FragData[1].r = weight;\n    ",!1);e.Fragment=t.result}function sy(e,t){t.classHierarchy.push("vtkOpenGLOrderIndependentTranslucentPass"),e.createVertexBuffer=()=>{const e=new Float32Array([-1,-1,-1,1,-1,-1,-1,1,-1,1,1,-1]),n=new Float32Array([0,0,1,0,0,1,1,1]),r=new Uint16Array([4,0,1,3,2]),o=Ds.newInstance({numberOfComponents:3,values:e});o.setName("points");const a=Ds.newInstance({numberOfComponents:2,values:n});a.setName("tcoords");const i=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(i,"polys",oy.SURFACE,{points:o,tcoords:a,cellOffset:0}),t.VBOBuildTime.modified()},e.createFramebuffer=e=>{const n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","attribute vec2 tcoordTC;","varying vec2 tcoord;","void main() { tcoord = tcoordTC; gl_Position = vertexDC; }"].join("\n"),"//VTK::System::Dec\n\nin vec2 tcoord;\n\nuniform sampler2D translucentRTexture;\nuniform sampler2D translucentRGBATexture;\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  vec4 t1Color = texture(translucentRGBATexture, tcoord);\n  float t2Color = texture(translucentRTexture, tcoord).r;\n  gl_FragData[0] = vec4(t1Color.rgb/max(t2Color,0.01), 1.0 - t1Color.a);\n}\n","")},e.createVBO=n=>{const r=n.getContext();t.tris.setOpenGLRenderWindow(n),e.createVertexBuffer();const o=t.copyShader;t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(o,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),r.FLOAT,3,r.FALSE)||ay("Error setting vertexDC in copy shader 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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a;e.onModified((function(){t.renderable&&(t.size[0]===o[0]&&t.size[1]===o[1]||(o[0]=t.size[0],o[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]))),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.addMissingNodes(t.renderable.getChildRenderWindowsByReference()),e.removeUnusedNodes(),e.initialize(),t.children.forEach((t=>{t.setOpenGLRenderWindow?.(e)}))}},e.initialize=()=>{if(!t.initialized){if(t.rootOpenGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.rootOpenGLRenderWindow)t.context2D=e.get2DContext();else{t.context=e.get3DContext(),e.resizeFromChildRenderWindows(),t.context&&(Ny++,_y.forEach((e=>e(Ny)))),t.textureUnitManager=wy.newInstance(),t.textureUnitManager.setContext(t.context),t.shaderCache.setContext(t.context);const 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rendererUBO.SCPCMatrix*vertexBC;"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderTCoord=(e,t,n)=>{t.getShaderDescription("vertex").addOutput("vec2<f32>","tcoordVS")},t.shaderReplacements.set("replaceShaderTCoord",e.replaceShaderTCoord),e.addTextureView=e=>{t.textureViews.includes(e)||t.textureViews.push(e)},e.prepareToDraw=n=>{t.renderEncoder=n,e.updateInput(),e.updateBuffers(),e.updateBindings(),e.updatePipeline()},e.updateInput=()=>{},e.updateBuffers=()=>{},e.updateBindings=()=>{t.bindGroup.setBindables(e.getBindables())},e.computePipelineHash=()=>{},e.registerDrawCallback=n=>{n.registerDrawCallback(t.pipeline,e.draw)},e.prepareAndDraw=n=>{e.prepareToDraw(n),n.setPipeline(t.pipeline),e.draw(n)},e.draw=e=>{const n=e.getBoundPipeline();e.activateBindGroup(t.bindGroup),t.WebGPURenderer&&t.WebGPURenderer.bindUBO(e),n.bindVertexInput(e,t.vertexInput);const r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pb,n),jt.obj(e,t),t.options={},t.bindGroupLayoutEntry={visibility:GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT,sampler:{}},t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),jt.get(e,t,["bindGroupTime","handle","options"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label"]),db(e,t)}var gb={newInstance:jt.newInstance(fb),extend:fb};function mb(e,t){t.classHierarchy.push("vtkWebGPUTextureView"),e.create=(e,n)=>{t.texture=e,t.options=n,t.options.dimension=t.options.dimension||"2d",t.options.label=t.label,t.textureHandle=e.getHandle(),t.handle=t.textureHandle.createView(t.options),t.bindGroupLayoutEntry.texture.viewDimension=t.options.dimension;const 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const r=fT(n);t.bufferEntries.push({name:e,type:n,sizeInBytes:r,offset:t.sizeInBytes,nativeType:gT(n)}),t.sizeInBytes+=r},e.send=e=>{if(!t._buffer){const n={nativeArray:t.Float32Array,usage:Fb.Storage,label:t.label};return t._buffer=e.getBufferManager().getBuffer(n),t.bindGroupTime.modified(),void t._sendTime.modified()}e.getHandle().queue.writeBuffer(t._buffer.getHandle(),0,t.arrayBuffer,0,t.sizeInBytes*t.numberOfInstances),t._sendTime.modified()},e.createView=e=>{e in t==0&&(t.arrayBuffer||(t.arrayBuffer=new ArrayBuffer(t.sizeInBytes*t.numberOfInstances)),t[e]=jt.newTypedArray(e,t.arrayBuffer))},e.setValue=(n,r,o)=>{const a=t._bufferEntryNames.get(n);if(void 0===a)return void kb(`entry named ${n} not found in UBO`);const i=t.bufferEntries[a];e.createView(i.nativeType);const s=t[i.nativeType];s[(i.offset+r*t.sizeInBytes)/s.BYTES_PER_ELEMENT]=o},e.setArray=(n,r,o)=>{const a=t._bufferEntryNames.get(n);if(void 0===a)return void kb(`entry named ${n} not found in UBO`);const i=t.bufferEntries[a];e.createView(i.nativeType);const s=t[i.nativeType],l=(i.offset+r*t.sizeInBytes)/s.BYTES_PER_ELEMENT;for(let e=0;e<o.length;e++)s[l+e]=o[e]},e.setAllInstancesFromArray=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType],s=r.length/t.numberOfInstances;for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<s;t++)i[n+t]=r[e*s+t]}},e.setAllInstancesFromArrayColorToFloat=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType],s=r.length/t.numberOfInstances;for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<s;t++)i[n+t]=r[e*s+t]/255}},e.setAllInstancesFromArray3x3To4x4=(n,r)=>{const o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15],y=n*s-r*i,T=n*l-o*i,b=n*c-a*i,x=r*l-o*s,C=r*c-a*s,S=o*c-a*l,A=u*m-d*g,I=u*h-p*g,w=u*v-f*g,P=d*h-p*m,O=d*v-f*m,R=p*v-f*h,M=y*R-T*O+b*P+x*w-C*I+S*A;return M?(M=1/M,e[0]=(s*R-l*O+c*P)*M,e[1]=(o*O-r*R-a*P)*M,e[2]=(m*S-h*C+v*x)*M,e[3]=(p*C-d*S-f*x)*M,e[4]=(l*w-i*R-c*I)*M,e[5]=(n*R-o*w+a*I)*M,e[6]=(h*b-g*S-v*T)*M,e[7]=(u*S-p*b+f*T)*M,e[8]=(i*O-s*w+c*A)*M,e[9]=(r*w-n*O-a*A)*M,e[10]=(g*C-m*b+v*y)*M,e[11]=(d*b-u*C-f*y)*M,e[12]=(s*I-i*P-l*A)*M,e[13]=(n*P-r*I+o*A)*M,e[14]=(m*T-g*x-h*y)*M,e[15]=(u*x-d*T+p*y)*M,e):null}function v(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15];return e[0]=s*(p*v-f*h)-d*(l*v-c*h)+m*(l*f-c*p),e[1]=-(r*(p*v-f*h)-d*(o*v-a*h)+m*(o*f-a*p)),e[2]=r*(l*v-c*h)-s*(o*v-a*h)+m*(o*c-a*l),e[3]=-(r*(l*f-c*p)-s*(o*f-a*p)+d*(o*c-a*l)),e[4]=-(i*(p*v-f*h)-u*(l*v-c*h)+g*(l*f-c*p)),e[5]=n*(p*v-f*h)-u*(o*v-a*h)+g*(o*f-a*p),e[6]=-(n*(l*v-c*h)-i*(o*v-a*h)+g*(o*c-a*l)),e[7]=n*(l*f-c*p)-i*(o*f-a*p)+u*(o*c-a*l),e[8]=i*(d*v-f*m)-u*(s*v-c*m)+g*(s*f-c*d),e[9]=-(n*(d*v-f*m)-u*(r*v-a*m)+g*(r*f-a*d)),e[10]=n*(s*v-c*m)-i*(r*v-a*m)+g*(r*c-a*s),e[11]=-(n*(s*f-c*d)-i*(r*f-a*d)+u*(r*c-a*s)),e[12]=-(i*(d*h-p*m)-u*(s*h-l*m)+g*(s*p-l*d)),e[13]=n*(d*h-p*m)-u*(r*h-o*m)+g*(r*p-o*d),e[14]=-(n*(s*h-l*m)-i*(r*h-o*m)+g*(r*l-o*s)),e[15]=n*(s*p-l*d)-i*(r*p-o*d)+u*(r*l-o*s),e}function y(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8],u=e[9],d=e[10],p=e[11],f=e[12],g=e[13],m=e[14],h=e[15];return(t*i-n*a)*(d*h-p*m)-(t*s-r*a)*(u*h-p*g)+(t*l-o*a)*(u*m-d*g)+(n*s-r*i)*(c*h-p*f)-(n*l-o*i)*(c*m-d*f)+(r*l-o*s)*(c*g-u*f)}function T(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return e[0]===t[0]&&e[1]===t[1]&&e[2]===t[2]&&e[3]===t[3]&&e[4]===t[4]&&e[5]===t[5]&&e[6]===t[6]&&e[7]===t[7]&&e[8]===t[8]&&e[9]===t[9]&&e[10]===t[10]&&e[11]===t[11]&&e[12]===t[12]&&e[13]===t[13]&&e[14]===t[14]&&e[15]===t[15]}function re(e,t){var n=e[0],r=e[1],o=e[2],i=e[3],s=e[4],l=e[5],c=e[6],u=e[7],d=e[8],p=e[9],f=e[10],g=e[11],m=e[12],h=e[13],v=e[14],y=e[15],T=t[0],b=t[1],x=t[2],C=t[3],S=t[4],A=t[5],I=t[6],w=t[7],P=t[8],O=t[9],R=t[10],M=t[11],E=t[12],D=t[13],V=t[14],L=t[15];return Math.abs(n-T)<=a*Math.max(1,Math.abs(n),Math.abs(T))&&Math.abs(r-b)<=a*Math.max(1,Math.abs(r),Math.abs(b))&&Math.abs(o-x)<=a*Math.max(1,Math.abs(o),Math.abs(x))&&Math.abs(i-C)<=a*Math.max(1,Math.abs(i),Math.abs(C))&&Math.abs(s-S)<=a*Math.max(1,Math.abs(s),Math.abs(S))&&Math.abs(l-A)<=a*Math.max(1,Math.abs(l),Math.abs(A))&&Math.abs(c-I)<=a*Math.max(1,Math.abs(c),Math.abs(I))&&Math.abs(u-w)<=a*Math.max(1,Math.abs(u),Math.abs(w))&&Math.abs(d-P)<=a*Math.max(1,Math.abs(d),Math.abs(P))&&Math.abs(p-O)<=a*Math.max(1,Math.abs(p),Math.abs(O))&&Math.abs(f-R)<=a*Math.max(1,Math.abs(f),Math.abs(R))&&Math.abs(g-M)<=a*Math.max(1,Math.abs(g),Math.abs(M))&&Math.abs(m-E)<=a*Math.max(1,Math.abs(m),Math.abs(E))&&Math.abs(h-D)<=a*Math.max(1,Math.abs(h),Math.abs(D))&&Math.abs(v-V)<=a*Math.max(1,Math.abs(v),Math.abs(V))&&Math.abs(y-L)<=a*Math.max(1,Math.abs(y),Math.abs(L))}var oe=T,ae=J;function ie(){var e=new i(9);return i!=Float32Array&&(e[1]=0,e[2]=0,e[3]=0,e[5]=0,e[6]=0,e[7]=0),e[0]=1,e[4]=1,e[8]=1,e}function se(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[4],e[4]=t[5],e[5]=t[6],e[6]=t[8],e[7]=t[9],e[8]=t[10],e}function le(e){var t=new i(9);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t}function ce(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function ue(e,t,n,r,o,a,s,l,c){var u=new i(9);return u[0]=e,u[1]=t,u[2]=n,u[3]=r,u[4]=o,u[5]=a,u[6]=s,u[7]=l,u[8]=c,u}function de(e,t,n,r,o,a,i,s,l,c){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e}function pe(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function fe(e,t){if(e===t){var n=t[1],r=t[2],o=t[5];e[1]=t[3],e[2]=t[6],e[3]=n,e[5]=t[7],e[6]=r,e[7]=o}else e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8];return e}function ge(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=u*i-s*c,p=-u*a+s*l,f=c*a-i*l,g=n*d+r*p+o*f;return g?(g=1/g,e[0]=d*g,e[1]=(-u*r+o*c)*g,e[2]=(s*r-o*i)*g,e[3]=p*g,e[4]=(u*n-o*l)*g,e[5]=(-s*n+o*a)*g,e[6]=f*g,e[7]=(-c*n+r*l)*g,e[8]=(i*n-r*a)*g,e):null}function me(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8];return e[0]=i*u-s*c,e[1]=o*c-r*u,e[2]=r*s-o*i,e[3]=s*l-a*u,e[4]=n*u-o*l,e[5]=o*a-n*s,e[6]=a*c-i*l,e[7]=r*l-n*c,e[8]=n*i-r*a,e}function he(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8];return t*(c*a-i*l)+n*(-c*o+i*s)+r*(l*o-a*s)}function ve(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=n[0],f=n[1],g=n[2],m=n[3],h=n[4],v=n[5],y=n[6],T=n[7],b=n[8];return e[0]=p*r+f*i+g*c,e[1]=p*o+f*s+g*u,e[2]=p*a+f*l+g*d,e[3]=m*r+h*i+v*c,e[4]=m*o+h*s+v*u,e[5]=m*a+h*l+v*d,e[6]=y*r+T*i+b*c,e[7]=y*o+T*s+b*u,e[8]=y*a+T*l+b*d,e}function ye(e,t,n){var 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n=0;n<3;n++)t.color[n]=e*(t.ambient*t.ambientColor[n]+t.diffuse*t.diffuseColor[n]+t.specular*t.specularColor[n]);return[].concat(t.color)},e.setSpecularPower=n=>{const r=1/Math.max(1,n);t.roughness===r&&t.specularPower===n||(t.specularPower=n,t.roughness=r,e.modified())},e.addShaderVariable=ss("AddShaderVariable"),e.setInterpolationToFlat=()=>e.setInterpolation(is.FLAT),e.setInterpolationToGouraud=()=>e.setInterpolation(is.GOURAUD),e.setInterpolationToPhong=()=>e.setInterpolation(is.PHONG),e.getInterpolationAsString=()=>jt.enumToString(is,t.interpolation),e.setRepresentationToWireframe=()=>e.setRepresentation(as.WIREFRAME),e.setRepresentationToSurface=()=>e.setRepresentation(as.SURFACE),e.setRepresentationToPoints=()=>e.setRepresentation(as.POINTS),e.getRepresentationAsString=()=>jt.enumToString(as,t.representation)}const cs={color:[1,1,1],ambientColor:[1,1,1],diffuseColor:[1,1,1],specularColor:[1,1,1],edgeColor:[0,0,0],ambient:0,diffuse:1,metallic:0,roughness:.6,normalStrength:1,emission:1,baseIOR:1.45,specular:0,specularPower:1,opacity:1,interpolation:is.GOURAUD,representation:as.SURFACE,edgeVisibility:!1,backfaceCulling:!1,frontfaceCulling:!1,pointSize:1,lineWidth:1,lighting:!0,shading:!1,materialName:null};function us(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,cs,n),jt.obj(e,t),jt.setGet(e,t,["lighting","interpolation","ambient","diffuse","metallic","roughness","normalStrength","emission","baseIOR","specular","specularPower","opacity","edgeVisibility","lineWidth","pointSize","backfaceCulling","frontfaceCulling","representation","diffuseTexture","metallicTexture","roughnessTexture","normalTexture","ambientOcclusionTexture","emissionTexture"]),jt.setGetArray(e,t,["ambientColor","specularColor","diffuseColor","edgeColor"],3),ls(e,t)}var ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||n[0]!==t.mapperBounds[0]||n[1]!==t.mapperBounds[1]||n[2]!==t.mapperBounds[2]||n[3]!==t.mapperBounds[3]||n[4]!==t.mapperBounds[4]||n[5]!==t.mapperBounds[5]||e.getMTime()>t.boundsMTime.getMTime()){ps("Recomputing bounds..."),t.mapperBounds=n.concat();const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}return t.bounds},e.getMTime=()=>{let e=n.getMTime();if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}if(null!==t.backfaceProperty){const n=t.backfaceProperty.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection(),e.processSelectorPixelBuffers=(e,n)=>{t.mapper&&t.mapper.processSelectorPixelBuffers&&t.mapper.processSelectorPixelBuffers(e,n)}}const gs={mapper:null,property:null,backfaceProperty:null,forceOpaque:!1,forceTranslucent:!1,bounds:[1,-1,1,-1,1,-1]};function ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gs,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["backfaceProperty","forceOpaque","forceTranslucent","mapper"]),fs(e,t)}var hs={newInstance:jt.newInstance(ms,"vtkActor"),extend:ms};const vs={Int8Array:1,Uint8Array:1,Uint8ClampedArray:1,Int16Array:2,Uint16Array:2,Int32Array:4,Uint32Array:4,Float32Array:4,Float64Array:8},ys={VOID:"",CHAR:"Int8Array",SIGNED_CHAR:"Int8Array",UNSIGNED_CHAR:"Uint8Array",UNSIGNED_CHAR_CLAMPED:"Uint8ClampedArray",SHORT:"Int16Array",UNSIGNED_SHORT:"Uint16Array",INT:"Int32Array",UNSIGNED_INT:"Uint32Array",FLOAT:"Float32Array",DOUBLE:"Float64Array"};var Ts={DefaultDataType:ys.FLOAT,DataTypeByteSize:vs,VtkDataTypes:ys};const{vtkErrorMacro:bs}=Kt,{DefaultDataType:xs}=Ts,Cs=1e-6;function Ss(e,t,n){const r=e.length;let o,a,i=Number.MAX_VALUE,s=-Number.MAX_VALUE;for(a=t;a<r;a+=n)if(!Number.isNaN(e[a])){i=e[a],s=i;break}for(;a<r;a+=n)o=e[a],o<i?i=o:o>s&&(s=o);return{min:i,max:s}}function As(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;if(t<0&&n>1){const t=e.length/n,r=new Float64Array(t);for(let o=0,a=0;o<t;++o){for(let t=a+n;a<t;++a)r[o]+=e[a]*e[a];r[o]**=.5}return 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t.size>n*r&&(t.size=n*r,e.dataChange()),!0}t.classHierarchy.push("vtkDataArray"),e.dataChange=()=>{t.ranges=null,e.modified()},e.resize=r=>{n(r);const o=r*e.getNumberOfComponents();return t.size!==o&&(t.size=o,e.dataChange(),!0)},e.initialize=()=>{e.resize(0)},e.getElementComponentSize=()=>t.values.BYTES_PER_ELEMENT,e.getComponent=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return t.values[e*t.numberOfComponents+n]},e.setComponent=(n,r,o)=>{o!==t.values[n*t.numberOfComponents+r]&&(t.values[n*t.numberOfComponents+r]=o,e.dataChange())},e.getValue=n=>{const r=n/t.numberOfComponents,o=n%t.numberOfComponents;return e.getComponent(r,o)},e.setValue=(n,r)=>{const o=n/t.numberOfComponents,a=n%t.numberOfComponents;e.setComponent(o,a,r)},e.getData=()=>t.size===t.values.length?t.values:t.values.subarray(0,t.size),e.getRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,r=n;r<0&&(r=1===t.numberOfComponents?0:t.numberOfComponents);let o=null;return t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents)),o=t.ranges[r],o?(t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple):(o=As(e.getData(),n,t.numberOfComponents),t.ranges[r]=o,t.rangeTuple[0]=o.min,t.rangeTuple[1]=o.max,t.rangeTuple)},e.setRange=(e,n)=>{t.ranges||(t.ranges=Is(t.ranges,t.numberOfComponents));const r={min:e.min,max:e.max};return t.ranges[n]=r,t.rangeTuple[0]=r.min,t.rangeTuple[1]=r.max,t.rangeTuple},e.setTuple=(e,n)=>{const r=e*t.numberOfComponents;for(let e=0;e<t.numberOfComponents;e++)t.values[r+e]=n[e]},e.setTuples=(e,n)=>{let r=e*t.numberOfComponents;const o=Math.min(n.length,t.size-r);for(let e=0;e<o;)t.values[r++]=n[e++]},e.insertTuple=(r,o)=>(t.size<=r*t.numberOfComponents&&(t.size=(r+1)*t.numberOfComponents,n(r+1)),e.setTuple(r,o),r),e.insertTuples=(r,o)=>{const a=r+o.length/t.numberOfComponents;return t.size<a*t.numberOfComponents&&(t.size=a*t.numberOfComponents,n(a)),e.setTuples(r,o),a},e.insertNextTuple=n=>{const 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0!==arguments[0]?arguments[0]:1;return e*t.numberOfComponents},e.getNumberOfComponents=()=>t.numberOfComponents,e.getNumberOfValues=()=>t.size,e.getNumberOfTuples=()=>t.size/t.numberOfComponents,e.getDataType=()=>t.dataType,e.newClone=()=>Es({empty:!0,name:t.name,dataType:t.dataType,numberOfComponents:t.numberOfComponents}),e.getName=()=>(t.name||(e.modified(),t.name=`vtkDataArray${e.getMTime()}`),t.name),e.setData=(n,r)=>{t.values=n,t.size=n.length,t.dataType=ws(n),r&&(t.numberOfComponents=r),t.size%t.numberOfComponents!=0&&(t.numberOfComponents=1),e.dataChange()},e.getState=()=>{if(t.deleted)return null;const n={...t,vtkClass:e.getClassName()};n.values=Array.from(n.values),delete n.buffer,Object.keys(n).forEach((e=>{n[e]||delete n[e]}));const r={};return Object.keys(n).sort().forEach((e=>{r[e]=n[e]})),r.mtime&&delete r.mtime,r},e.deepCopy=n=>{const r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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r=t.clippingPlanes.indexOf(n);return-1!==r&&(t.clippingPlanes.splice(r,1),e.modified(),!0)},e.getClippingPlanes=()=>t.clippingPlanes,e.setClippingPlanes=t=>{if(t)if(Array.isArray(t)){const n=t.length;for(let r=0;r<n&&r<6;r++)e.addClippingPlane(t[r])}else e.addClippingPlane(t)},e.getClippingPlaneInDataCoords=(e,n,r)=>{const o=t.clippingPlanes,a=e;if(o){const e=o.length;if(n>=0&&n<e){const e=o[n],t=e.getNormal(),i=e.getOrigin(),s=t[0],l=t[1],c=t[2],u=-(s*i[0]+l*i[1]+c*i[2]);return r[0]=s*a[0]+l*a[4]+c*a[8]+u*a[12],r[1]=s*a[1]+l*a[5]+c*a[9]+u*a[13],r[2]=s*a[2]+l*a[6]+c*a[10]+u*a[14],void(r[3]=s*a[3]+l*a[7]+c*a[11]+u*a[15])}}jt.vtkErrorMacro(`Clipping plane index ${n} is out of range.`)}}const Ls={clippingPlanes:[]};var Bs=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ls,n),jt.obj(e,t),jt.algo(e,t,1,0),t.clippingPlanes||(t.clippingPlanes=[]),Vs(e,t)};function Ns(e,t){e.getBounds=()=>(jt.vtkErrorMacro("vtkAbstractMapper3D.getBounds - NOT 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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e=a>-1?a:r;t.interpolateTuple(e,s,r,s,o,i),Gs("Unexpected case in interpolateData")}else t.insertTuples(s.getTuples());else ks("Unhandled case in interpolateData");else if(r<0||o<0||r>s.getNumberOfTuples())e.addArray(s),n.getAttributes(s).forEach((t=>{e.setAttribute(s,t)}));else{const l=s.getNumberOfComponents();let c=s.getNumberOfValues();const u=a>-1?a:r;c<=u*l&&(c=(u+1)*l),t=Ds.newInstance({name:s.getName(),dataType:s.getDataType(),numberOfComponents:l,values:jt.newTypedArray(s.getDataType(),c),size:0}),t.interpolateTuple(u,s,r,s,o,i),e.addArray(t),n.getAttributes(s).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.copyFieldOn=e=>{t.copyFieldFlags[e]=!0},e.copyFieldOff=e=>{t.copyFieldFlags[e]=!1},e.copyAllOn=()=>{t.doCopyAllOn&&!t.doCopyAllOff||(t.doCopyAllOn=!0,t.doCopyAllOff=!1,e.modified())},e.copyAllOff=()=>{!t.doCopyAllOn&&t.doCopyAllOff||(t.doCopyAllOn=!1,t.doCopyAllOff=!0,e.modified())},e.clearFieldFlags=()=>{t.copyFieldFlags={}},e.deepCopy=e=>{t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. The attribute must be a vtkDataArray.`),-1;if(n&&!e.checkNumberOfComponents(n,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1;let i=t[`active${a}`];if(i>=0&&i<t.arrays.length){if(t.arrays[i]===n)return i;e.removeArrayByIndex(i)}return n?(i=e.addArray(n),t[`active${a}`]=i):t[`active${a}`]=-1,e.modified(),t[`active${a}`]},e.getAttributes=t=>n.filter((n=>e[`get${n}`]()===t)),e.setActiveAttributeByName=(t,n)=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e.setActiveAttributeByIndex=(n,o)=>{const a=r(o);if(n>=0&&n<t.arrays.length){if("PEDIGREEIDS"!==a.toUpperCase()){const t=e.getArrayByIndex(n);if(!t.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. Only vtkDataArray subclasses can be set as active attributes.`),-1;if(!e.checkNumberOfComponents(t,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1}return t[`active${a}`]=n,e.modified(),n}return-1===n&&(t[`active${a}`]=n,e.modified()),-1},e.getActiveAttribute=t=>{const n=r(t);return e[`get${n}`]()},e.removeAllArrays=()=>{n.forEach((e=>{t[`active${e}`]=-1})),o.removeAllArrays()},e.removeArrayByIndex=e=>(-1!==e&&n.forEach((n=>{e===t[`active${n}`]?t[`active${n}`]=-1:e<t[`active${n}`]&&(t[`active${n}`]-=1)})),o.removeArrayByIndex(e)),n.forEach((n=>{const r=`active${n}`;e[`get${n}`]=()=>e.getArrayByIndex(t[r]),e[`set${n}`]=t=>e.setAttribute(t,n),e[`setActive${n}`]=t=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e[`copy${n}Off`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!1},e[`copy${n}On`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!0}})),e.initializeAttributeCopyFlags=()=>{t.copyAttributeFlags=[],Object.keys(qs).filter((e=>"ALLCOPY"!==e)).forEach((e=>{t.copyAttributeFlags[qs[e]]=Object.keys($s).filter((e=>"NUM_ATTRIBUTES"!==e)).reduce(((e,t)=>(e[$s[t]]=!0,e)),[])})),t.copyAttributeFlags[qs.COPYTUPLE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.INTERPOLATE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.COPYTUPLE][$s.PEDIGREEIDS]=!1},e.initialize=jt.chain(e.initialize,e.initializeAttributeCopyFlags),t.dataArrays&&Object.keys(t.dataArrays).length&&Object.keys(t.dataArrays).forEach((n=>{t.dataArrays[n].ref||"vtkDataArray"!==t.dataArrays[n].type||e.addArray(Ds.newInstance(t.dataArrays[n]))}));const a=e.shallowCopy;e.shallowCopy=(e,n)=>{a(e,n),t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.shallowCopy(e,n),{data:t}}))},e.initializeAttributeCopyFlags()}const Zs={activeScalars:-1,activeVectors:-1,activeTensors:-1,activeNormals:-1,activeTCoords:-1,activeGlobalIds:-1,activePedigreeIds:-1};function Qs(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zs,n),Hs.extend(e,t,n),jt.setGet(e,t,["activeScalars","activeNormals","activeTCoords","activeVectors","activeTensors","activeGlobalIds","activePedigreeIds"]),t.arrays||(t.arrays={}),Ys(e,t)}var Js={newInstance:jt.newInstance(Qs,"vtkDataSetAttributes"),extend:Qs,...Ks};const el=["pointData","cellData","fieldData"];function tl(e,t){t.classHierarchy.push("vtkDataSet"),el.forEach((e=>{t[e]?t[e]=We(t[e]):t[e]=Js.newInstance()}));const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),el.forEach((n=>{t[n]=Js.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))}}const nl={};function rl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nl,n),jt.obj(e,t),jt.setGet(e,t,el),tl(e,t)}var ol={newInstance:jt.newInstance(rl,"vtkDataSet"),extend:rl,FieldDataTypes:{UNIFORM:0,DATA_OBJECT_FIELD:0,COORDINATE:1,POINT_DATA:1,POINT:2,POINT_FIELD_DATA:2,CELL:3,CELL_FIELD_DATA:3,VERTEX:4,VERTEX_FIELD_DATA:4,EDGE:5,EDGE_FIELD_DATA:5,ROW:6,ROW_DATA:6},FieldAssociations:{FIELD_ASSOCIATION_POINTS:0,FIELD_ASSOCIATION_CELLS:1,FIELD_ASSOCIATION_NONE:2,FIELD_ASSOCIATION_POINTS_THEN_CELLS:3,FIELD_ASSOCIATION_VERTICES:4,FIELD_ASSOCIATION_EDGES:5,FIELD_ASSOCIATION_ROWS:6,NUMBER_OF_ASSOCIATIONS:7}};const al={UNCHANGED:0,SINGLE_POINT:1,X_LINE:2,Y_LINE:3,Z_LINE:4,XY_PLANE:5,YZ_PLANE:6,XZ_PLANE:7,XYZ_GRID:8,EMPTY:9};var il={StructuredType:al};const{StructuredType:sl}=il;var ll={getDataDescriptionFromExtent:function(e){let t=0;for(let n=0;n<3;++n)e[2*n]<e[2*n+1]&&t++;return e[0]>e[1]||e[2]>e[3]||e[4]>e[5]?sl.EMPTY:3===t?sl.XYZ_GRID:2===t?e[0]===e[1]?sl.YZ_PLANE:e[2]===e[3]?sl.XZ_PLANE:sl.XY_PLANE:1===t?e[0]<e[1]?sl.X_LINE:e[2]<e[3]?sl.Y_LINE:sl.Z_LINE:sl.SINGLE_POINT},...il};const{vtkErrorMacro:cl}=jt;function ul(e,t){t.classHierarchy.push("vtkImageData"),e.setExtent=function(){if(t.deleted)return cl("instance deleted - cannot call any method"),!1;for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];const a=1===r.length?r[0]:r;if(6!==a.length)return!1;const i=t.extent.some(((e,t)=>e!==a[t]));return i&&(t.extent=a.slice(),t.dataDescription=ll.getDataDescriptionFromExtent(t.extent),e.modified()),i},e.setDimensions=function(){let n,r,o;if(t.deleted)cl("instance deleted - cannot call any method");else{if(1===arguments.length){const e=arguments.length<=0?void 0:arguments[0];n=e[0],r=e[1],o=e[2]}else{if(3!==arguments.length)return void cl("Bad dimension specification");n=arguments.length<=0?void 0:arguments[0],r=arguments.length<=1?void 0:arguments[1],o=arguments.length<=2?void 0:arguments[2]}e.setExtent(0,n-1,0,r-1,0,o-1)}},e.getDimensions=()=>[t.extent[1]-t.extent[0]+1,t.extent[3]-t.extent[2]+1,t.extent[5]-t.extent[4]+1],e.getNumberOfCells=()=>{const t=e.getDimensions();let n=1;for(let e=0;e<3;e++){if(0===t[e])return 0;t[e]>1&&(n*=t[e]-1)}return n},e.getNumberOfPoints=()=>{const t=e.getDimensions();return t[0]*t[1]*t[2]},e.getPoint=n=>{const r=e.getDimensions();if(0===r[0]||0===r[1]||0===r[2])return cl("Requesting a point from an empty image."),null;const o=new Float64Array(3);switch(t.dataDescription){case al.EMPTY:return null;case al.SINGLE_POINT:break;case al.X_LINE:o[0]=n;break;case al.Y_LINE:o[1]=n;break;case al.Z_LINE:o[2]=n;break;case al.XY_PLANE:o[0]=n%r[0],o[1]=n/r[0];break;case al.YZ_PLANE:o[1]=n%r[1],o[2]=n/r[1];break;case al.XZ_PLANE:o[0]=n%r[0],o[2]=n/r[0];break;case al.XYZ_GRID:o[0]=n%r[0],o[1]=n/r[0]%r[1],o[2]=n/(r[0]*r[1]);break;default:cl("Invalid dataDescription")}const a=[0,0,0];return e.indexToWorld(o,a),a},e.getBounds=()=>e.extentToBounds(e.getSpatialExtent()),e.extentToBounds=e=>ki.transformBounds(e,t.indexToWorld),e.getSpatialExtent=()=>ki.inflate([...t.extent],.5),e.computeTransforms=()=>{w(t.indexToWorld,t.origin),t.indexToWorld[0]=t.direction[0],t.indexToWorld[1]=t.direction[1],t.indexToWorld[2]=t.direction[2],t.indexToWorld[4]=t.direction[3],t.indexToWorld[5]=t.direction[4],t.indexToWorld[6]=t.direction[5],t.indexToWorld[8]=t.direction[6],t.indexToWorld[9]=t.direction[7],t.indexToWorld[10]=t.direction[8],x(t.indexToWorld,t.indexToWorld,t.spacing),h(t.worldToIndex,t.indexToWorld)},e.indexToWorld=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.indexToWorld),n},e.indexToWorldVec3=e.indexToWorld,e.worldToIndex=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.worldToIndex),n},e.worldToIndexVec3=e.worldToIndex,e.indexToWorldBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.indexToWorld,n)},e.worldToIndexBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.worldToIndex,n)},e.onModified(e.computeTransforms),e.computeTransforms(),e.getCenter=()=>ki.getCenter(e.getBounds()),e.computeHistogram=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=[0,0,0,0,0,0];e.worldToIndexBounds(t,r);const o=[0,0,0],a=[0,0,0];ki.computeCornerPoints(r,o,a),ua(o,o),ua(a,a);const i=e.getDimensions();Ea(o,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],o),Ea(a,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],a);const s=i[0],l=i[0]*i[1],c=e.getPointData().getScalars().getData();let u=-1/0,d=1/0,p=0,f=0,g=0;for(let e=o[2];e<=a[2];e++)for(let t=o[1];t<=a[1];t++){let i=o[0]+t*s+e*l;for(let s=o[0];s<=a[0];s++){if(!n||n([s,t,e],r)){const e=c[i];e>u&&(u=e),e<d&&(d=e),p+=e*e,f+=e,g+=1}++i}}const m=g>0?f/g:0,h=g?Math.abs(p/g-m*m):0,v=Math.sqrt(h);return{minimum:d,maximum:u,average:m,variance:h,sigma:v,count:g}},e.computeIncrements=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;const n=[];let r=t;for(let t=0;t<3;++t)n[t]=r,r*=e[2*t+1]-e[2*t]+1;return n},e.computeOffsetIndex=t=>{let[n,r,o]=t;const a=e.getExtent(),i=e.getPointData().getScalars().getNumberOfComponents(),s=e.computeIncrements(a,i);return Math.floor((Math.round(n)-a[0])*s[0]+(Math.round(r)-a[2])*s[1]+(Math.round(o)-a[4])*s[2])},e.getOffsetIndexFromWorld=t=>{const n=e.getExtent(),r=e.worldToIndex(t);for(let e=0;e<3;++e)if(r[e]<n[2*e]||r[e]>n[2*e+1])return cl(`GetScalarPointer: Pixel ${r} is not in memory. Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. Current Scalar numberOfComponents: ${r}`),NaN;const o=e.getOffsetIndexFromWorld(t);return Number.isNaN(o)?o:e.getPointData().getScalars().getComponent(o,n)}}const dl={direction:null,indexToWorld:null,worldToIndex:null,spacing:[1,1,1],origin:[0,0,0],extent:[0,-1,0,-1,0,-1],dataDescription:al.EMPTY};function pl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dl,n),ol.extend(e,t,n),t.direction?Array.isArray(t.direction)&&(t.direction=new Float64Array(t.direction.slice(0,9))):t.direction=pe(new Float64Array(9)),t.indexToWorld=new Float64Array(16),t.worldToIndex=new Float64Array(16),jt.get(e,t,["indexToWorld","worldToIndex"]),jt.setGetArray(e,t,["origin","spacing"],3),jt.setGetArray(e,t,["direction"],9),jt.getArray(e,t,["extent"],6),ul(e,t)}var fl={newInstance:jt.newInstance(pl,"vtkImageData"),extend:pl};const gl={LUMINANCE:1,LUMINANCE_ALPHA:2,RGB:3,RGBA:4};var ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const 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u=(n[1]-n[0])/(r-1),[d,p]=[n[0]-u,n[1]+u],f=d-.5*u,g=1/(p-d+u),m=d,h=(r+1)/(p-d),v=e.getData(),y=e.getNumberOfTuples(),T=e.getNumberOfComponents(),b=t<0||t>=T,x=o[2]<=1?2:3,C=Ds.newInstance({numberOfComponents:x,values:new Float32Array(y*x)}),S=C.getData(),A=[0,0,0];tc(A,r+2,o);let I=0,w=0;const P=[.5,.5,.5];for(let e=0;e<y;++e){let e;if(b){let t=0;for(let e=0;e<T;++e){const n=v[I+e];t+=n*n}e=Math.sqrt(t)}else e=v[I+t];if(I+=T,a&&(e=Dl.applyLogScale(e,n,n)),_a(e))P[0]=A[0],P[1]=A[1],P[2]=A[2];else if(i){let t=(e-m)*h;t<1?t=0:t>r&&(t=r+1),tc(P,t,o)}else{P[1]=.49;const t=(e-f)*g;P[0]=t>1e3?1e3:t<-1e3?-1e3:t}for(let e=0;e<x;++e)S[w++]=P[e]}return nc.set(e,{stringHash:l,textureCoordinates:C}),C}(n,u,a,t.numberOfColorsInRange,t.colorTextureMap.getDimensions(),i,r)},e.getIsOpaque=()=>{const n=e.getInputData(),r=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!t.scalarVisibility||null==r)return!0;const 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fc={newInstance:jt.newInstance(pc,"vtkCellArray"),extend:pc,...cc};const{vtkErrorMacro:gc}=jt,mc=[1,-1,1,-1,1,-1];function hc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return gc(`getBounds called on an array with components of\n        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n},e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.deepCopy=e=>{e.initialize(t.points,t.pointsIds)},e.getCellDimension=()=>{},e.intersectWithLine=(e,t,n,r,o,a,i)=>{},e.evaluatePosition=(e,t,n,r,o,a)=>{jt.vtkErrorMacro("vtkCell.evaluatePosition is not implemented.")}}const xc={bounds:[-1,-1,-1,-1,-1,-1],pointsIds:[]};function Cc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xc,n),jt.obj(e,t),t.points||(t.points=Tc.newInstance()),jt.get(e,t,["points","pointsIds"]),bc(e,t)}var Sc={newInstance:jt.newInstance(Cc,"vtkCell"),extend:Cc};function Ac(e,t){t.classHierarchy.push("vtkCellLinks"),e.buildLinks=n=>{const r=n.getPoints().getNumberOfPoints(),o=n.getNumberOfCells(),a=new Uint32Array(r);if(n.isA("vtkPolyData")){for(let t=0;t<o;++t){const{cellPointIds:r}=n.getCellPoints(t);r.forEach((t=>{e.incrementLinkCount(t)}))}e.allocateLinks(r),t.maxId=r-1;for(let 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let n=t;for(t>=e.array.length&&(n+=e.array.length);n>e.array.length;)e.array.push({ncells:0,cells:null});e.array.length=n}(t,t.maxId+1)},e.reset=()=>{t.maxId=-1},e.deepCopy=e=>{t.array=[...e.array],t.extend=e.extend,t.maxId=e.maxId},e.incrementLinkCount=e=>{++t.array[e].ncells},e.allocateLinks=e=>{for(let n=0;n<e;++n)t.array[n].cells=new Array(t.array[n].ncells)},e.insertCellReference=(e,n,r)=>{t.array[e].cells[n]=r}}const Ic={array:null,maxId:0,extend:0};function wc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ic,n),jt.obj(e,t),Ac(e,t)}var Pc={newInstance:jt.newInstance(wc,"vtkCellLinks"),extend:wc};const Oc=0,Rc=1,Mc=2,Ec=3,Dc=4,Vc=5,Lc=6,Bc=7,Nc=9,_c=21,Fc=41,kc=42,Gc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],Uc={getClassNameFromTypeId:function(e){return e<Gc.length?Gc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Gc.findIndex(e)},isLinear:function(e){return e<_c||e===Fc||e===kc},hasSubCells:function(e){return e===Lc||e===Dc||e===Mc}};function zc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Oc},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let r=0;r<n;++r)if(t===e.getCellType(r))return!0;return!1},e.insertNextType=t=>e.insertNextCell(t,-1),e.getCellType=e=>t.typeArray[e],e.reset=()=>{t.maxId=-1},e.deepCopy=n=>{e.allocate(n.getSize(),n.getExtend()),t.typeArray.set(n.getTypeArray()),t.locationArray.set(n.getLocationArray()),t.maxId=n.getMaxId()}}const Wc={size:0,maxId:-1,extend:1e3};function Hc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wc,n),jt.obj(e,t),jt.get(e,t,["size","maxId","extend"]),jt.getArray(e,t,["typeArray","locationArray"]),zc(e,t)}var jc={newInstance:jt.newInstance(Hc,"vtkCellTypes"),extend:Hc,...Uc},Kc={IntersectionState:{NO_INTERSECTION:0,YES_INTERSECTION:1,ON_LINE:2}};const{IntersectionState:$c}=Kc;function qc(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;const o={t:Number.MIN_VALUE,distance:0},a=[];let i;a[0]=n[0]-t[0],a[1]=n[1]-t[1],a[2]=n[2]-t[2];const s=a[0]*(e[0]-t[0])+a[1]*(e[1]-t[1])+a[2]*(e[2]-t[2]),l=jo(a,a);let c=1e-5*s;return 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u=[],d=[],p=Xc(e,n,l,c,u,d);var f;if(i.t=u[0],i.betweenPoints=(f=i.t)>=0&&f<=1,a[0]=d[0],p===$c.YES_INTERSECTION){for(let t=0;t<3;t++)o[t]=l[t]+a[0]*(c[t]-l[t]),s[t]=e[t]+i.t*(n[t]-e[t]);if(Yo(o,s)<=r*r)return i.intersect=1,i}else{let t;if(i.t<0)return t=qc(e,l,c,o),t.distance<=r*r?(i.t=0,i.intersect=1,i.betweenPoints=!0,i):i;if(i.t>1)return t=qc(n,l,c,o),t.distance<=r*r?(i.t=1,i.intersect=1,i.betweenPoints=!0,i):i;if(a[0]<0)return a[0]=0,t=qc(l,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i;if(a[0]>1)return a[0]=1,t=qc(c,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i}return i},e.evaluateLocation=(e,n,r)=>{const o=[],a=[];t.points.getPoint(0,o),t.points.getPoint(1,a);for(let t=0;t<3;t++)n[t]=o[t]+e[0]*(a[t]-o[t]);r[0]=1-e[0],r[1]=e[0]},e.evaluateOrientation=(e,n,r)=>!!t.orientations&&(function(e,t,n,r){var o,i,s,l,c,u=t[0],d=t[1],p=t[2],f=t[3],g=n[0],m=n[1],h=n[2],v=n[3];(i=u*g+d*m+p*h+f*v)<0&&(i=-i,g=-g,m=-m,h=-h,v=-v),1-i>a?(o=Math.acos(i),s=Math.sin(o),l=Math.sin((1-r)*o)/s,c=Math.sin(r*o)/s):(l=1-r,c=r),e[0]=l*u+c*g,e[1]=l*d+c*m,e[2]=l*p+c*h,e[3]=l*f+c*v}(n,t.orientations[0],t.orientations[1],e[0]),r[0]=1-e[0],r[1]=e[0],!0)}const Qc={orientations:null};function Jc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Qc,n),Sc.extend(e,t,n),jt.setGet(e,t,["orientations"]),Zc(e,t)}var eu={newInstance:jt.newInstance(Jc,"vtkLine"),extend:Jc,...Yc,...Kc};function tu(e,t){t.classHierarchy.push("vtkPointSet"),t.points?t.points=We(t.points):t.points=Tc.newInstance(),e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.getBounds=()=>t.points.getBounds(),e.computeBounds=()=>{e.getBounds()};const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),t.points=Tc.newInstance(),t.points.shallowCopy(e.getPoints())}}const nu={};function ru(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nu,n),ol.extend(e,t,n),jt.setGet(e,t,["points"]),tu(e,t)}var ou={newInstance:jt.newInstance(ru,"vtkPointSet"),extend:ru};function au(e,t,n,r){const o=n[0]-t[0],a=n[1]-t[1],i=n[2]-t[2],s=e[0]-t[0],l=e[1]-t[1],c=e[2]-t[2];r[0]=a*c-i*l,r[1]=i*s-o*c,r[2]=o*l-a*s}function iu(e,t,n,r){au(e,t,n,r);const o=Math.sqrt(r[0]*r[0]+r[1]*r[1]+r[2]*r[2]);0!==o&&(r[0]/=o,r[1]/=o,r[2]/=o)}const su={computeNormalDirection:au,computeNormal:iu,intersectWithTriangle:function(e,t,n,r,o,a){let i=arguments.length>6&&void 0!==arguments[6]?arguments[6]:1e-6,s=!1;const l=[],c=[],u=[],d=[],p=[];iu(e,t,n,d),iu(r,o,a,p);const f=-jo(d,e),g=-jo(p,r),m=[jo(p,e)+g,jo(p,t)+g,jo(p,n)+g];if(m[0]*m[1]>i&&m[0]*m[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible buffer.",!1),e.bind=()=>!!o&&(t.context.bindBuffer(n(r),o),!0),e.release=()=>!!o&&(t.context.bindBuffer(n(r),null),!0),e.releaseGraphicsResources=()=>{null!==o&&(t.context.bindBuffer(n(r),null),t.context.deleteBuffer(o),o=null,t.allocatedGPUMemoryInBytes=0)},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getError=()=>i}const Zu={objectType:Xu.ARRAY_BUFFER,context:null,allocatedGPUMemoryInBytes:0};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),jt.obj(e,t),jt.get(e,t,["_openGLRenderWindow","allocatedGPUMemoryInBytes"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu,...qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkOpenGLCellArrayBufferObject"),e.setType($u.ARRAY_BUFFER),e.createVBO=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * 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vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=mg(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.oglObject?.getHandle()&&m?.hash===g)t.colorTexture=m.oglObject;else{t.colorTexture=Ud.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(n*p*3);if(c===og.NEAREST?(t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST)):(t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR)),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader 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Image;n.crossOrigin&&(o.crossOrigin=n.crossOrigin),o.onload=()=>e(o),o.onerror=r,o.src=t}))}};zm("http",(e=>kh));const{vtkDebugMacro:Gh}=jt;function Uh(e,t){t.classHierarchy.push("vtkCamera");const n=new Float64Array(3),r=new Float64Array([0,0,-1]),o=new Float64Array([0,1,0]),a=g(new Float64Array(16)),i=g(new Float64Array(16)),s=new Float64Array(3),l=new Float64Array(3),c=new Float64Array(3),u=g(new Float64Array(16)),p=g(new Float64Array(16)),f=new Float64Array(3),v=new Float64Array(3);function y(){t.viewPlaneNormal[0]=-t.directionOfProjection[0],t.viewPlaneNormal[1]=-t.directionOfProjection[1],t.viewPlaneNormal[2]=-t.directionOfProjection[2]}e.orthogonalizeViewUp=()=>{const n=e.getViewMatrix();t.viewUp[0]=n[4],t.viewUp[1]=n[5],t.viewUp[2]=n[6],e.modified()},e.setPosition=(n,r,o)=>{n===t.position[0]&&r===t.position[1]&&o===t.position[2]||(t.position[0]=n,t.position[1]=r,t.position[2]=o,e.computeDistance(),e.modified())},e.setFocalPoint=(n,r,o)=>{n===t.focalPoint[0]&&r===t.focalPoint[1]&&o===t.focalPoint[2]||(t.focalPoint[0]=n,t.focalPoint[1]=r,t.focalPoint[2]=o,e.computeDistance(),e.modified())},e.setDistance=n=>{if(t.distance===n)return;t.distance=n,t.distance<1e-20&&(t.distance=1e-20,Gh("Distance is set to minimum."));const r=t.directionOfProjection;t.focalPoint[0]=t.position[0]+r[0]*t.distance,t.focalPoint[1]=t.position[1]+r[1]*t.distance,t.focalPoint[2]=t.position[2]+r[2]*t.distance,e.modified()},e.computeDistance=()=>{const e=t.focalPoint[0]-t.position[0],n=t.focalPoint[1]-t.position[1],r=t.focalPoint[2]-t.position[2];if(t.distance=Math.sqrt(e*e+n*n+r*r),t.distance<1e-20){t.distance=1e-20,Gh("Distance is set to minimum.");const e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let 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Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId 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pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an 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t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const 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o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const Mv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Ev(e,t){let n=arguments.length>2&&void 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let 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n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = 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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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v[0]=e,v[1]=t,v[2]=n,v[3]=r,v[4]=o,v[5]=a,v[6]=s,v[7]=l,v[8]=c,v[9]=u,v[10]=d,v[11]=p,v[12]=f,v[13]=g,v[14]=m,v[15]=h,v}function f(e,t,n,r,o,a,i,s,l,c,u,d,p,f,g,m,h){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e[9]=u,e[10]=d,e[11]=p,e[12]=f,e[13]=g,e[14]=m,e[15]=h,e}function g(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function m(e,t){if(e===t){var n=t[1],r=t[2],o=t[3],a=t[6],i=t[7],s=t[11];e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=n,e[6]=t[9],e[7]=t[13],e[8]=r,e[9]=a,e[11]=t[14],e[12]=o,e[13]=i,e[14]=s}else e[0]=t[0],e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=t[1],e[5]=t[5],e[6]=t[9],e[7]=t[13],e[8]=t[2],e[9]=t[6],e[10]=t[10],e[11]=t[14],e[12]=t[3],e[13]=t[7],e[14]=t[11],e[15]=t[15];return e}function h(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15],y=n*s-r*i,T=n*l-o*i,b=n*c-a*i,x=r*l-o*s,C=r*c-a*s,S=o*c-a*l,A=u*m-d*g,I=u*h-p*g,w=u*v-f*g,P=d*h-p*m,O=d*v-f*m,R=p*v-f*h,M=y*R-T*O+b*P+x*w-C*I+S*A;return M?(M=1/M,e[0]=(s*R-l*O+c*P)*M,e[1]=(o*O-r*R-a*P)*M,e[2]=(m*S-h*C+v*x)*M,e[3]=(p*C-d*S-f*x)*M,e[4]=(l*w-i*R-c*I)*M,e[5]=(n*R-o*w+a*I)*M,e[6]=(h*b-g*S-v*T)*M,e[7]=(u*S-p*b+f*T)*M,e[8]=(i*O-s*w+c*A)*M,e[9]=(r*w-n*O-a*A)*M,e[10]=(g*C-m*b+v*y)*M,e[11]=(d*b-u*C-f*y)*M,e[12]=(s*I-i*P-l*A)*M,e[13]=(n*P-r*I+o*A)*M,e[14]=(m*T-g*x-h*y)*M,e[15]=(u*x-d*T+p*y)*M,e):null}function v(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=t[4],s=t[5],l=t[6],c=t[7],u=t[8],d=t[9],p=t[10],f=t[11],g=t[12],m=t[13],h=t[14],v=t[15];return e[0]=s*(p*v-f*h)-d*(l*v-c*h)+m*(l*f-c*p),e[1]=-(r*(p*v-f*h)-d*(o*v-a*h)+m*(o*f-a*p)),e[2]=r*(l*v-c*h)-s*(o*v-a*h)+m*(o*c-a*l),e[3]=-(r*(l*f-c*p)-s*(o*f-a*p)+d*(o*c-a*l)),e[4]=-(i*(p*v-f*h)-u*(l*v-c*h)+g*(l*f-c*p)),e[5]=n*(p*v-f*h)-u*(o*v-a*h)+g*(o*f-a*p),e[6]=-(n*(l*v-c*h)-i*(o*v-a*h)+g*(o*c-a*l)),e[7]=n*(l*f-c*p)-i*(o*f-a*p)+u*(o*c-a*l),e[8]=i*(d*v-f*m)-u*(s*v-c*m)+g*(s*f-c*d),e[9]=-(n*(d*v-f*m)-u*(r*v-a*m)+g*(r*f-a*d)),e[10]=n*(s*v-c*m)-i*(r*v-a*m)+g*(r*c-a*s),e[11]=-(n*(s*f-c*d)-i*(r*f-a*d)+u*(r*c-a*s)),e[12]=-(i*(d*h-p*m)-u*(s*h-l*m)+g*(s*p-l*d)),e[13]=n*(d*h-p*m)-u*(r*h-o*m)+g*(r*p-o*d),e[14]=-(n*(s*h-l*m)-i*(r*h-o*m)+g*(r*l-o*s)),e[15]=n*(s*p-l*d)-i*(r*p-o*d)+u*(r*l-o*s),e}function y(e){var t=e[0],n=e[1],r=e[2],o=e[3],a=e[4],i=e[5],s=e[6],l=e[7],c=e[8],u=e[9],d=e[10],p=e[11],f=e[12],g=e[13],m=e[14],h=e[15];return(t*i-n*a)*(d*h-p*m)-(t*s-r*a)*(u*h-p*g)+(t*l-o*a)*(u*m-d*g)+(n*s-r*i)*(c*h-p*f)-(n*l-o*i)*(c*m-d*f)+(r*l-o*s)*(c*g-u*f)}function T(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],m=t[12],h=t[13],v=t[14],y=t[15],T=n[0],b=n[1],x=n[2],C=n[3];return e[0]=T*r+b*s+x*d+C*m,e[1]=T*o+b*l+x*p+C*h,e[2]=T*a+b*c+x*f+C*v,e[3]=T*i+b*u+x*g+C*y,T=n[4],b=n[5],x=n[6],C=n[7],e[4]=T*r+b*s+x*d+C*m,e[5]=T*o+b*l+x*p+C*h,e[6]=T*a+b*c+x*f+C*v,e[7]=T*i+b*u+x*g+C*y,T=n[8],b=n[9],x=n[10],C=n[11],e[8]=T*r+b*s+x*d+C*m,e[9]=T*o+b*l+x*p+C*h,e[10]=T*a+b*c+x*f+C*v,e[11]=T*i+b*u+x*g+C*y,T=n[12],b=n[13],x=n[14],C=n[15],e[12]=T*r+b*s+x*d+C*m,e[13]=T*o+b*l+x*p+C*h,e[14]=T*a+b*c+x*f+C*v,e[15]=T*i+b*u+x*g+C*y,e}function b(e,t,n){var r,o,a,i,s,l,c,u,d,p,f,g,m=n[0],h=n[1],v=n[2];return t===e?(e[12]=t[0]*m+t[4]*h+t[8]*v+t[12],e[13]=t[1]*m+t[5]*h+t[9]*v+t[13],e[14]=t[2]*m+t[6]*h+t[10]*v+t[14],e[15]=t[3]*m+t[7]*h+t[11]*v+t[15]):(r=t[0],o=t[1],a=t[2],i=t[3],s=t[4],l=t[5],c=t[6],u=t[7],d=t[8],p=t[9],f=t[10],g=t[11],e[0]=r,e[1]=o,e[2]=a,e[3]=i,e[4]=s,e[5]=l,e[6]=c,e[7]=u,e[8]=d,e[9]=p,e[10]=f,e[11]=g,e[12]=r*m+s*h+d*v+t[12],e[13]=o*m+l*h+p*v+t[13],e[14]=a*m+c*h+f*v+t[14],e[15]=i*m+u*h+g*v+t[15]),e}function x(e,t,n){var r=n[0],o=n[1],a=n[2];return e[0]=t[0]*r,e[1]=t[1]*r,e[2]=t[2]*r,e[3]=t[3]*r,e[4]=t[4]*o,e[5]=t[5]*o,e[6]=t[6]*o,e[7]=t[7]*o,e[8]=t[8]*a,e[9]=t[9]*a,e[10]=t[10]*a,e[11]=t[11]*a,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function C(e,t,n,r){var o,i,s,l,c,u,d,p,f,g,m,h,v,y,T,b,x,C,S,A,I,w,P,O,R=r[0],M=r[1],E=r[2],D=Math.hypot(R,M,E);return D<a?null:(R*=D=1/D,M*=D,E*=D,o=Math.sin(n),s=1-(i=Math.cos(n)),l=t[0],c=t[1],u=t[2],d=t[3],p=t[4],f=t[5],g=t[6],m=t[7],h=t[8],v=t[9],y=t[10],T=t[11],b=R*R*s+i,x=M*R*s+E*o,C=E*R*s-M*o,S=R*M*s-E*o,A=M*M*s+i,I=E*M*s+R*o,w=R*E*s+M*o,P=M*E*s-R*o,O=E*E*s+i,e[0]=l*b+p*x+h*C,e[1]=c*b+f*x+v*C,e[2]=u*b+g*x+y*C,e[3]=d*b+m*x+T*C,e[4]=l*S+p*A+h*I,e[5]=c*S+f*A+v*I,e[6]=u*S+g*A+y*I,e[7]=d*S+m*A+T*I,e[8]=l*w+p*P+h*O,e[9]=c*w+f*P+v*O,e[10]=u*w+g*P+y*O,e[11]=d*w+m*P+T*O,t!==e&&(e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e)}function S(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[4],i=t[5],s=t[6],l=t[7],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[4]=a*o+c*r,e[5]=i*o+u*r,e[6]=s*o+d*r,e[7]=l*o+p*r,e[8]=c*o-a*r,e[9]=u*o-i*r,e[10]=d*o-s*r,e[11]=p*o-l*r,e}function A(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[8],u=t[9],d=t[10],p=t[11];return t!==e&&(e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o-c*r,e[1]=i*o-u*r,e[2]=s*o-d*r,e[3]=l*o-p*r,e[8]=a*r+c*o,e[9]=i*r+u*o,e[10]=s*r+d*o,e[11]=l*r+p*o,e}function I(e,t,n){var r=Math.sin(n),o=Math.cos(n),a=t[0],i=t[1],s=t[2],l=t[3],c=t[4],u=t[5],d=t[6],p=t[7];return t!==e&&(e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15]),e[0]=a*o+c*r,e[1]=i*o+u*r,e[2]=s*o+d*r,e[3]=l*o+p*r,e[4]=c*o-a*r,e[5]=u*o-i*r,e[6]=d*o-s*r,e[7]=p*o-l*r,e}function w(e,t){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=t[0],e[13]=t[1],e[14]=t[2],e[15]=1,e}function P(e,t){return e[0]=t[0],e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=t[1],e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=t[2],e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function O(e,t,n){var r,o,i,s=n[0],l=n[1],c=n[2],u=Math.hypot(s,l,c);return u<a?null:(s*=u=1/u,l*=u,c*=u,r=Math.sin(t),i=1-(o=Math.cos(t)),e[0]=s*s*i+o,e[1]=l*s*i+c*r,e[2]=c*s*i-l*r,e[3]=0,e[4]=s*l*i-c*r,e[5]=l*l*i+o,e[6]=c*l*i+s*r,e[7]=0,e[8]=s*c*i+l*r,e[9]=l*c*i-s*r,e[10]=c*c*i+o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e)}function R(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=r,e[6]=n,e[7]=0,e[8]=0,e[9]=-n,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function M(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=0,e[2]=-n,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=n,e[9]=0,e[10]=r,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function E(e,t){var n=Math.sin(t),r=Math.cos(t);return e[0]=r,e[1]=n,e[2]=0,e[3]=0,e[4]=-n,e[5]=r,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function D(e,t,n){var r=t[0],o=t[1],a=t[2],i=t[3],s=r+r,l=o+o,c=a+a,u=r*s,d=r*l,p=r*c,f=o*l,g=o*c,m=a*c,h=i*s,v=i*l,y=i*c;return e[0]=1-(f+m),e[1]=d+y,e[2]=p-v,e[3]=0,e[4]=d-y,e[5]=1-(u+m),e[6]=g+h,e[7]=0,e[8]=p+v,e[9]=g-h,e[10]=1-(u+f),e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function V(e,t){var n=new i(3),r=-t[0],o=-t[1],a=-t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=r*r+o*o+a*a+s*s;return p>0?(n[0]=2*(l*s+d*r+c*a-u*o)/p,n[1]=2*(c*s+d*o+u*r-l*a)/p,n[2]=2*(u*s+d*a+l*o-c*r)/p):(n[0]=2*(l*s+d*r+c*a-u*o),n[1]=2*(c*s+d*o+u*r-l*a),n[2]=2*(u*s+d*a+l*o-c*r)),D(e,t,n),e}function L(e,t){return e[0]=t[12],e[1]=t[13],e[2]=t[14],e}function B(e,t){var n=t[0],r=t[1],o=t[2],a=t[4],i=t[5],s=t[6],l=t[8],c=t[9],u=t[10];return e[0]=Math.hypot(n,r,o),e[1]=Math.hypot(a,i,s),e[2]=Math.hypot(l,c,u),e}function N(e,t){var n=new i(3);B(n,t);var r=1/n[0],o=1/n[1],a=1/n[2],s=t[0]*r,l=t[1]*o,c=t[2]*a,u=t[4]*r,d=t[5]*o,p=t[6]*a,f=t[8]*r,g=t[9]*o,m=t[10]*a,h=s+d+m,v=0;return h>0?(v=2*Math.sqrt(h+1),e[3]=.25*v,e[0]=(p-g)/v,e[1]=(f-c)/v,e[2]=(l-u)/v):s>d&&s>m?(v=2*Math.sqrt(1+s-d-m),e[3]=(p-g)/v,e[0]=.25*v,e[1]=(l+u)/v,e[2]=(f+c)/v):d>m?(v=2*Math.sqrt(1+d-s-m),e[3]=(f-c)/v,e[0]=(l+u)/v,e[1]=.25*v,e[2]=(p+g)/v):(v=2*Math.sqrt(1+m-s-d),e[3]=(l-u)/v,e[0]=(f+c)/v,e[1]=(p+g)/v,e[2]=.25*v),e}function _(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=t[3],l=o+o,c=a+a,u=i+i,d=o*l,p=o*c,f=o*u,g=a*c,m=a*u,h=i*u,v=s*l,y=s*c,T=s*u,b=r[0],x=r[1],C=r[2];return e[0]=(1-(g+h))*b,e[1]=(p+T)*b,e[2]=(f-y)*b,e[3]=0,e[4]=(p-T)*x,e[5]=(1-(d+h))*x,e[6]=(m+v)*x,e[7]=0,e[8]=(f+y)*C,e[9]=(m-v)*C,e[10]=(1-(d+g))*C,e[11]=0,e[12]=n[0],e[13]=n[1],e[14]=n[2],e[15]=1,e}function F(e,t,n,r,o){var a=t[0],i=t[1],s=t[2],l=t[3],c=a+a,u=i+i,d=s+s,p=a*c,f=a*u,g=a*d,m=i*u,h=i*d,v=s*d,y=l*c,T=l*u,b=l*d,x=r[0],C=r[1],S=r[2],A=o[0],I=o[1],w=o[2],P=(1-(m+v))*x,O=(f+b)*x,R=(g-T)*x,M=(f-b)*C,E=(1-(p+v))*C,D=(h+y)*C,V=(g+T)*S,L=(h-y)*S,B=(1-(p+m))*S;return e[0]=P,e[1]=O,e[2]=R,e[3]=0,e[4]=M,e[5]=E,e[6]=D,e[7]=0,e[8]=V,e[9]=L,e[10]=B,e[11]=0,e[12]=n[0]+A-(P*A+M*I+V*w),e[13]=n[1]+I-(O*A+E*I+L*w),e[14]=n[2]+w-(R*A+D*I+B*w),e[15]=1,e}function k(e,t){var n=t[0],r=t[1],o=t[2],a=t[3],i=n+n,s=r+r,l=o+o,c=n*i,u=r*i,d=r*s,p=o*i,f=o*s,g=o*l,m=a*i,h=a*s,v=a*l;return e[0]=1-d-g,e[1]=u+v,e[2]=p-h,e[3]=0,e[4]=u-v,e[5]=1-c-g,e[6]=f+m,e[7]=0,e[8]=p+h,e[9]=f-m,e[10]=1-c-d,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function G(e,t,n,r,o,a,i){var s=1/(n-t),l=1/(o-r),c=1/(a-i);return e[0]=2*a*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=2*a*l,e[6]=0,e[7]=0,e[8]=(n+t)*s,e[9]=(o+r)*l,e[10]=(i+a)*c,e[11]=-1,e[12]=0,e[13]=0,e[14]=i*a*2*c,e[15]=0,e}function U(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=(o+r)*a,e[14]=2*o*r*a):(e[10]=-1,e[14]=-2*r),e}Math.hypot||(Math.hypot=function(){for(var e=0,t=arguments.length;t--;)e+=arguments[t]*arguments[t];return Math.sqrt(e)});var z=U;function W(e,t,n,r,o){var a,i=1/Math.tan(t/2);return e[0]=i/n,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=i,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[11]=-1,e[12]=0,e[13]=0,e[15]=0,null!=o&&o!==1/0?(a=1/(r-o),e[10]=o*a,e[14]=o*r*a):(e[10]=-1,e[14]=-r),e}function H(e,t,n,r){var o=Math.tan(t.upDegrees*Math.PI/180),a=Math.tan(t.downDegrees*Math.PI/180),i=Math.tan(t.leftDegrees*Math.PI/180),s=Math.tan(t.rightDegrees*Math.PI/180),l=2/(i+s),c=2/(o+a);return e[0]=l,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=-(i-s)*l*.5,e[9]=(o-a)*c*.5,e[10]=r/(n-r),e[11]=-1,e[12]=0,e[13]=0,e[14]=r*n/(n-r),e[15]=0,e}function j(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=2*c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=(i+a)*c,e[15]=1,e}var K=j;function $(e,t,n,r,o,a,i){var s=1/(t-n),l=1/(r-o),c=1/(a-i);return e[0]=-2*s,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=-2*l,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=c,e[11]=0,e[12]=(t+n)*s,e[13]=(o+r)*l,e[14]=a*c,e[15]=1,e}function q(e,t,n,r){var o,i,s,l,c,u,d,p,f,m,h=t[0],v=t[1],y=t[2],T=r[0],b=r[1],x=r[2],C=n[0],S=n[1],A=n[2];return Math.abs(h-C)<a&&Math.abs(v-S)<a&&Math.abs(y-A)<a?g(e):(d=h-C,p=v-S,f=y-A,o=b*(f*=m=1/Math.hypot(d,p,f))-x*(p*=m),i=x*(d*=m)-T*f,s=T*p-b*d,(m=Math.hypot(o,i,s))?(o*=m=1/m,i*=m,s*=m):(o=0,i=0,s=0),l=p*s-f*i,c=f*o-d*s,u=d*i-p*o,(m=Math.hypot(l,c,u))?(l*=m=1/m,c*=m,u*=m):(l=0,c=0,u=0),e[0]=o,e[1]=l,e[2]=d,e[3]=0,e[4]=i,e[5]=c,e[6]=p,e[7]=0,e[8]=s,e[9]=u,e[10]=f,e[11]=0,e[12]=-(o*h+i*v+s*y),e[13]=-(l*h+c*v+u*y),e[14]=-(d*h+p*v+f*y),e[15]=1,e)}function X(e,t,n,r){var o=t[0],a=t[1],i=t[2],s=r[0],l=r[1],c=r[2],u=o-n[0],d=a-n[1],p=i-n[2],f=u*u+d*d+p*p;f>0&&(u*=f=1/Math.sqrt(f),d*=f,p*=f);var g=l*p-c*d,m=c*u-s*p,h=s*d-l*u;return(f=g*g+m*m+h*h)>0&&(g*=f=1/Math.sqrt(f),m*=f,h*=f),e[0]=g,e[1]=m,e[2]=h,e[3]=0,e[4]=d*h-p*m,e[5]=p*g-u*h,e[6]=u*m-d*g,e[7]=0,e[8]=u,e[9]=d,e[10]=p,e[11]=0,e[12]=o,e[13]=a,e[14]=i,e[15]=1,e}function Y(e){return"mat4("+e[0]+", "+e[1]+", "+e[2]+", "+e[3]+", "+e[4]+", "+e[5]+", "+e[6]+", "+e[7]+", "+e[8]+", "+e[9]+", "+e[10]+", "+e[11]+", "+e[12]+", "+e[13]+", "+e[14]+", "+e[15]+")"}function Z(e){return Math.hypot(e[0],e[1],e[2],e[3],e[4],e[5],e[6],e[7],e[8],e[9],e[10],e[11],e[12],e[13],e[14],e[15])}function Q(e,t,n){return e[0]=t[0]+n[0],e[1]=t[1]+n[1],e[2]=t[2]+n[2],e[3]=t[3]+n[3],e[4]=t[4]+n[4],e[5]=t[5]+n[5],e[6]=t[6]+n[6],e[7]=t[7]+n[7],e[8]=t[8]+n[8],e[9]=t[9]+n[9],e[10]=t[10]+n[10],e[11]=t[11]+n[11],e[12]=t[12]+n[12],e[13]=t[13]+n[13],e[14]=t[14]+n[14],e[15]=t[15]+n[15],e}function J(e,t,n){return e[0]=t[0]-n[0],e[1]=t[1]-n[1],e[2]=t[2]-n[2],e[3]=t[3]-n[3],e[4]=t[4]-n[4],e[5]=t[5]-n[5],e[6]=t[6]-n[6],e[7]=t[7]-n[7],e[8]=t[8]-n[8],e[9]=t[9]-n[9],e[10]=t[10]-n[10],e[11]=t[11]-n[11],e[12]=t[12]-n[12],e[13]=t[13]-n[13],e[14]=t[14]-n[14],e[15]=t[15]-n[15],e}function ee(e,t,n){return e[0]=t[0]*n,e[1]=t[1]*n,e[2]=t[2]*n,e[3]=t[3]*n,e[4]=t[4]*n,e[5]=t[5]*n,e[6]=t[6]*n,e[7]=t[7]*n,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12]*n,e[13]=t[13]*n,e[14]=t[14]*n,e[15]=t[15]*n,e}function te(e,t,n,r){return e[0]=t[0]+n[0]*r,e[1]=t[1]+n[1]*r,e[2]=t[2]+n[2]*r,e[3]=t[3]+n[3]*r,e[4]=t[4]+n[4]*r,e[5]=t[5]+n[5]*r,e[6]=t[6]+n[6]*r,e[7]=t[7]+n[7]*r,e[8]=t[8]+n[8]*r,e[9]=t[9]+n[9]*r,e[10]=t[10]+n[10]*r,e[11]=t[11]+n[11]*r,e[12]=t[12]+n[12]*r,e[13]=t[13]+n[13]*r,e[14]=t[14]+n[14]*r,e[15]=t[15]+n[15]*r,e}function ne(e,t){return e[0]===t[0]&&e[1]===t[1]&&e[2]===t[2]&&e[3]===t[3]&&e[4]===t[4]&&e[5]===t[5]&&e[6]===t[6]&&e[7]===t[7]&&e[8]===t[8]&&e[9]===t[9]&&e[10]===t[10]&&e[11]===t[11]&&e[12]===t[12]&&e[13]===t[13]&&e[14]===t[14]&&e[15]===t[15]}function re(e,t){var n=e[0],r=e[1],o=e[2],i=e[3],s=e[4],l=e[5],c=e[6],u=e[7],d=e[8],p=e[9],f=e[10],g=e[11],m=e[12],h=e[13],v=e[14],y=e[15],T=t[0],b=t[1],x=t[2],C=t[3],S=t[4],A=t[5],I=t[6],w=t[7],P=t[8],O=t[9],R=t[10],M=t[11],E=t[12],D=t[13],V=t[14],L=t[15];return 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i!=Float32Array&&(e[1]=0,e[2]=0,e[3]=0,e[5]=0,e[6]=0,e[7]=0),e[0]=1,e[4]=1,e[8]=1,e}function se(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[4],e[4]=t[5],e[5]=t[6],e[6]=t[8],e[7]=t[9],e[8]=t[10],e}function le(e){var t=new i(9);return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t}function ce(e,t){return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e}function ue(e,t,n,r,o,a,s,l,c){var u=new i(9);return u[0]=e,u[1]=t,u[2]=n,u[3]=r,u[4]=o,u[5]=a,u[6]=s,u[7]=l,u[8]=c,u}function de(e,t,n,r,o,a,i,s,l,c){return e[0]=t,e[1]=n,e[2]=r,e[3]=o,e[4]=a,e[5]=i,e[6]=s,e[7]=l,e[8]=c,e}function pe(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=1,e[5]=0,e[6]=0,e[7]=0,e[8]=1,e}function fe(e,t){if(e===t){var n=t[1],r=t[2],o=t[5];e[1]=t[3],e[2]=t[6],e[3]=n,e[5]=t[7],e[6]=r,e[7]=o}else e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8];return e}function ge(e,t){var 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ds={newInstance:jt.newInstance(us,"vtkProperty"),extend:us,...os};const{vtkDebugMacro:ps}=jt;function fs(e,t){t.classHierarchy.push("vtkActor");const n={...e};e.getActors=()=>[e],e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.texture||!t.texture.isTranslucent()),n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>null!==t.mapper&&(null===t.property&&e.setProperty(e.makeProperty()),!e.getIsOpaque()),e.makeProperty=ds.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return 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t.size>n*r&&(t.size=n*r,e.dataChange()),!0}t.classHierarchy.push("vtkDataArray"),e.dataChange=()=>{t.ranges=null,e.modified()},e.resize=r=>{n(r);const o=r*e.getNumberOfComponents();return t.size!==o&&(t.size=o,e.dataChange(),!0)},e.initialize=()=>{e.resize(0)},e.getElementComponentSize=()=>t.values.BYTES_PER_ELEMENT,e.getComponent=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return t.values[e*t.numberOfComponents+n]},e.setComponent=(n,r,o)=>{o!==t.values[n*t.numberOfComponents+r]&&(t.values[n*t.numberOfComponents+r]=o,e.dataChange())},e.getValue=n=>{const r=n/t.numberOfComponents,o=n%t.numberOfComponents;return e.getComponent(r,o)},e.setValue=(n,r)=>{const o=n/t.numberOfComponents,a=n%t.numberOfComponents;e.setComponent(o,a,r)},e.getData=()=>t.size===t.values.length?t.values:t.values.subarray(0,t.size),e.getRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,r=n;r<0&&(r=1===t.numberOfComponents?0:t.numberOfComponents);let 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0!==arguments[0]?arguments[0]:1;return e*t.numberOfComponents},e.getNumberOfComponents=()=>t.numberOfComponents,e.getNumberOfValues=()=>t.size,e.getNumberOfTuples=()=>t.size/t.numberOfComponents,e.getDataType=()=>t.dataType,e.newClone=()=>Es({empty:!0,name:t.name,dataType:t.dataType,numberOfComponents:t.numberOfComponents}),e.getName=()=>(t.name||(e.modified(),t.name=`vtkDataArray${e.getMTime()}`),t.name),e.setData=(n,r)=>{t.values=n,t.size=n.length,t.dataType=ws(n),r&&(t.numberOfComponents=r),t.size%t.numberOfComponents!=0&&(t.numberOfComponents=1),e.dataChange()},e.getState=()=>{if(t.deleted)return null;const n={...t,vtkClass:e.getClassName()};n.values=Array.from(n.values),delete n.buffer,Object.keys(n).forEach((e=>{n[e]||delete n[e]}));const r={};return Object.keys(n).sort().forEach((e=>{r[e]=n[e]})),r.mtime&&delete r.mtime,r},e.deepCopy=n=>{const r=e.getDataType(),o=t.values;e.shallowCopy(n),o?.length>=n.getNumberOfValues()&&r===n.getDataType()?(o.set(n.getData()),t.values=o,e.dataChange()):e.setData(n.getData().slice())},e.interpolateTuple=(n,r,o,a,i,s)=>{const l=t.numberOfComponents||1;l===r.getNumberOfComponents()&&l===a.getNumberOfComponents()||bs("numberOfComponents must match");const c=r.getTuple(o),u=a.getTuple(i),d=[];switch(d.length=l,l){case 4:d[3]=c[3]+(u[3]-c[3])*s;case 3:d[2]=c[2]+(u[2]-c[2])*s;case 2:d[1]=c[1]+(u[1]-c[1])*s;case 1:d[0]=c[0]+(u[0]-c[0])*s;break;default:for(let e=0;e<l;e++)d[e]=c[e]+(u[e]-c[e])*s}return e.insertTuple(n,d)}}const Rs={name:"",numberOfComponents:1,dataType:xs,rangeTuple:[0,0]};function Ms(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,Rs,n),!t.empty&&!t.values&&!t.size)throw new TypeError("Cannot create vtkDataArray object without: size > 0, 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n=e.getState;t.arrays&&(t.arrays=t.arrays.map((e=>({data:We(e.data)})))),e.initialize=()=>{e.initializeFields(),e.copyAllOn(),e.clearFieldFlags()},e.initializeFields=()=>{t.arrays=[],t.copyFieldFlags={},e.modified()},e.copyStructure=n=>{e.initializeFields(),t.copyFieldFlags=n.getCopyFieldFlags().map((e=>e)),t.arrays=n.arrays().map((e=>({array:e})))},e.getNumberOfArrays=()=>t.arrays.length,e.getNumberOfActiveArrays=()=>t.arrays.length,e.addArray=n=>{const r=n.getName(),{array:o,index:a}=e.getArrayWithIndex(r);return null!=o?(t.arrays[a]={data:n},a):(t.arrays=[].concat(t.arrays,{data:n}),t.arrays.length-1)},e.removeAllArrays=()=>{t.arrays=[]},e.removeArray=n=>{const r=t.arrays.findIndex((e=>e.data.getName()===n));return e.removeArrayByIndex(r)},e.removeArrayByIndex=e=>-1!==e&&e<t.arrays.length&&(t.arrays.splice(e,1),!0),e.getArrays=()=>t.arrays.map((e=>e.data)),e.getArray=t=>"number"==typeof t?e.getArrayByIndex(t):e.getArrayByName(t),e.getArrayByName=e=>t.arrays.reduce(((t,n,r)=>n.data.getName()===e?n.data:t),null),e.getArrayWithIndex=e=>{const n=t.arrays.findIndex((t=>t.data.getName()===e));return{array:-1!==n?t.arrays[n].data:null,index:n}},e.getArrayByIndex=e=>e>=0&&e<t.arrays.length?t.arrays[e].data:null,e.hasArray=t=>e.getArrayWithIndex(t).index>=0,e.getArrayName=e=>{const n=t.arrays[e];return n?n.data.getName():""},e.getCopyFieldFlags=()=>t.copyFieldFlags,e.getFlag=e=>t.copyFieldFlags[e],e.passData=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1,o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;n.getArrays().forEach((a=>{const i=e.getFlag(a.getName());if(!1!==i&&(!t.doCopyAllOff||!0===i)&&a){let t=e.getArrayByName(a.getName());if(t)if(a.getNumberOfComponents()===t.getNumberOfComponents())if(r>-1&&r<a.getNumberOfTuples()){const e=o>-1?o:r;t.insertTuple(e,a.getTuple(r))}else t.insertTuples(0,a.getTuples());else ks("Unhandled case 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u=a>-1?a:r;c<=u*l&&(c=(u+1)*l),t=Ds.newInstance({name:s.getName(),dataType:s.getDataType(),numberOfComponents:l,values:jt.newTypedArray(s.getDataType(),c),size:0}),t.interpolateTuple(u,s,r,s,o,i),e.addArray(t),n.getAttributes(s).forEach((n=>{e.setAttribute(t,n)}))}}}))},e.copyFieldOn=e=>{t.copyFieldFlags[e]=!0},e.copyFieldOff=e=>{t.copyFieldFlags[e]=!1},e.copyAllOn=()=>{t.doCopyAllOn&&!t.doCopyAllOff||(t.doCopyAllOn=!0,t.doCopyAllOff=!1,e.modified())},e.copyAllOff=()=>{!t.doCopyAllOn&&t.doCopyAllOff||(t.doCopyAllOn=!1,t.doCopyAllOff=!0,e.modified())},e.clearFieldFlags=()=>{t.copyFieldFlags={}},e.deepCopy=e=>{t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.deepCopy(e),{data:t}}))},e.copyFlags=e=>e.getCopyFieldFlags().map((e=>e)),e.reset=()=>t.arrays.forEach((e=>e.data.reset())),e.getMTime=()=>t.arrays.reduce(((e,t)=>t.data.getMTime()>e?t.data.getMTime():e),t.mtime),e.getNumberOfComponents=()=>t.arrays.reduce(((e,t)=>e+t.data.getNumberOfComponents()),0),e.getNumberOfTuples=()=>t.arrays.length>0?t.arrays[0].getNumberOfTuples():0,e.getState=()=>{const e=n();return e&&(e.arrays=t.arrays.map((e=>({data:e.data.getState()})))),e}}const zs={arrays:[],copyFieldFlags:[],doCopyAllOn:!0,doCopyAllOff:!1};function Ws(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zs,n),jt.obj(e,t),Us(e,t)}var Hs={newInstance:jt.newInstance(Ws,"vtkFieldData"),extend:Ws};const js={DEFAULT:0,SINGLE:1,DOUBLE:2};var Ks={AttributeCopyOperations:{COPYTUPLE:0,INTERPOLATE:1,PASSDATA:2,ALLCOPY:3},AttributeLimitTypes:{MAX:0,EXACT:1,NOLIMIT:2},AttributeTypes:{SCALARS:0,VECTORS:1,NORMALS:2,TCOORDS:3,TENSORS:4,GLOBALIDS:5,PEDIGREEIDS:6,EDGEFLAG:7,NUM_ATTRIBUTES:8},CellGhostTypes:{DUPLICATECELL:1,HIGHCONNECTIVITYCELL:2,LOWCONNECTIVITYCELL:4,REFINEDCELL:8,EXTERIORCELL:16,HIDDENCELL:32},DesiredOutputPrecision:js,PointGhostTypes:{DUPLICATEPOINT:1,HIDDENPOINT:2},ghostArrayName:"vtkGhostType"};const{AttributeTypes:$s,AttributeCopyOperations:qs}=Ks,{vtkWarningMacro:Xs}=jt;function Ys(e,t){const n=["Scalars","Vectors","Normals","TCoords","Tensors","GlobalIds","PedigreeIds"];function r(e){let t=n.find((t=>$s[t.toUpperCase()]===e||"number"!=typeof e&&t.toLowerCase()===e.toLowerCase()));return void 0===t&&(t=null),t}t.classHierarchy.push("vtkDataSetAttributes");const o={...e};e.checkNumberOfComponents=e=>!0,e.setAttribute=(n,o)=>{const a=r(o);if(n&&"PEDIGREEIDS"===a.toUpperCase()&&!n.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. The attribute must be a vtkDataArray.`),-1;if(n&&!e.checkNumberOfComponents(n,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1;let i=t[`active${a}`];if(i>=0&&i<t.arrays.length){if(t.arrays[i]===n)return i;e.removeArrayByIndex(i)}return n?(i=e.addArray(n),t[`active${a}`]=i):t[`active${a}`]=-1,e.modified(),t[`active${a}`]},e.getAttributes=t=>n.filter((n=>e[`get${n}`]()===t)),e.setActiveAttributeByName=(t,n)=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e.setActiveAttributeByIndex=(n,o)=>{const a=r(o);if(n>=0&&n<t.arrays.length){if("PEDIGREEIDS"!==a.toUpperCase()){const t=e.getArrayByIndex(n);if(!t.isA("vtkDataArray"))return Xs(`Cannot set attribute ${a}. Only vtkDataArray subclasses can be set as active attributes.`),-1;if(!e.checkNumberOfComponents(t,a))return Xs(`Cannot set attribute ${a}. Incorrect number of components.`),-1}return t[`active${a}`]=n,e.modified(),n}return-1===n&&(t[`active${a}`]=n,e.modified()),-1},e.getActiveAttribute=t=>{const n=r(t);return e[`get${n}`]()},e.removeAllArrays=()=>{n.forEach((e=>{t[`active${e}`]=-1})),o.removeAllArrays()},e.removeArrayByIndex=e=>(-1!==e&&n.forEach((n=>{e===t[`active${n}`]?t[`active${n}`]=-1:e<t[`active${n}`]&&(t[`active${n}`]-=1)})),o.removeArrayByIndex(e)),n.forEach((n=>{const r=`active${n}`;e[`get${n}`]=()=>e.getArrayByIndex(t[r]),e[`set${n}`]=t=>e.setAttribute(t,n),e[`setActive${n}`]=t=>e.setActiveAttributeByIndex(e.getArrayWithIndex(t).index,n),e[`copy${n}Off`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!1},e[`copy${n}On`]=()=>{const e=n.toUpperCase();t.copyAttributeFlags[qs.PASSDATA][$s[e]]=!0}})),e.initializeAttributeCopyFlags=()=>{t.copyAttributeFlags=[],Object.keys(qs).filter((e=>"ALLCOPY"!==e)).forEach((e=>{t.copyAttributeFlags[qs[e]]=Object.keys($s).filter((e=>"NUM_ATTRIBUTES"!==e)).reduce(((e,t)=>(e[$s[t]]=!0,e)),[])})),t.copyAttributeFlags[qs.COPYTUPLE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.INTERPOLATE][$s.GLOBALIDS]=!1,t.copyAttributeFlags[qs.COPYTUPLE][$s.PEDIGREEIDS]=!1},e.initialize=jt.chain(e.initialize,e.initializeAttributeCopyFlags),t.dataArrays&&Object.keys(t.dataArrays).length&&Object.keys(t.dataArrays).forEach((n=>{t.dataArrays[n].ref||"vtkDataArray"!==t.dataArrays[n].type||e.addArray(Ds.newInstance(t.dataArrays[n]))}));const a=e.shallowCopy;e.shallowCopy=(e,n)=>{a(e,n),t.arrays=e.getArrays().map((e=>{const t=e.newClone();return t.shallowCopy(e,n),{data:t}}))},e.initializeAttributeCopyFlags()}const Zs={activeScalars:-1,activeVectors:-1,activeTensors:-1,activeNormals:-1,activeTCoords:-1,activeGlobalIds:-1,activePedigreeIds:-1};function Qs(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zs,n),Hs.extend(e,t,n),jt.setGet(e,t,["activeScalars","activeNormals","activeTCoords","activeVectors","activeTensors","activeGlobalIds","activePedigreeIds"]),t.arrays||(t.arrays={}),Ys(e,t)}var Js={newInstance:jt.newInstance(Qs,"vtkDataSetAttributes"),extend:Qs,...Ks};const el=["pointData","cellData","fieldData"];function tl(e,t){t.classHierarchy.push("vtkDataSet"),el.forEach((e=>{t[e]?t[e]=We(t[e]):t[e]=Js.newInstance()}));const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),el.forEach((n=>{t[n]=Js.newInstance(),t[n].shallowCopy(e.getReferenceByName(n))}))}}const nl={};function rl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nl,n),jt.obj(e,t),jt.setGet(e,t,el),tl(e,t)}var ol={newInstance:jt.newInstance(rl,"vtkDataSet"),extend:rl,FieldDataTypes:{UNIFORM:0,DATA_OBJECT_FIELD:0,COORDINATE:1,POINT_DATA:1,POINT:2,POINT_FIELD_DATA:2,CELL:3,CELL_FIELD_DATA:3,VERTEX:4,VERTEX_FIELD_DATA:4,EDGE:5,EDGE_FIELD_DATA:5,ROW:6,ROW_DATA:6},FieldAssociations:{FIELD_ASSOCIATION_POINTS:0,FIELD_ASSOCIATION_CELLS:1,FIELD_ASSOCIATION_NONE:2,FIELD_ASSOCIATION_POINTS_THEN_CELLS:3,FIELD_ASSOCIATION_VERTICES:4,FIELD_ASSOCIATION_EDGES:5,FIELD_ASSOCIATION_ROWS:6,NUMBER_OF_ASSOCIATIONS:7}};const al={UNCHANGED:0,SINGLE_POINT:1,X_LINE:2,Y_LINE:3,Z_LINE:4,XY_PLANE:5,YZ_PLANE:6,XZ_PLANE:7,XYZ_GRID:8,EMPTY:9};var il={StructuredType:al};const{StructuredType:sl}=il;var ll={getDataDescriptionFromExtent:function(e){let t=0;for(let n=0;n<3;++n)e[2*n]<e[2*n+1]&&t++;return e[0]>e[1]||e[2]>e[3]||e[4]>e[5]?sl.EMPTY:3===t?sl.XYZ_GRID:2===t?e[0]===e[1]?sl.YZ_PLANE:e[2]===e[3]?sl.XZ_PLANE:sl.XY_PLANE:1===t?e[0]<e[1]?sl.X_LINE:e[2]<e[3]?sl.Y_LINE:sl.Z_LINE:sl.SINGLE_POINT},...il};const{vtkErrorMacro:cl}=jt;function ul(e,t){t.classHierarchy.push("vtkImageData"),e.setExtent=function(){if(t.deleted)return cl("instance deleted - cannot call any method"),!1;for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];const a=1===r.length?r[0]:r;if(6!==a.length)return!1;const i=t.extent.some(((e,t)=>e!==a[t]));return i&&(t.extent=a.slice(),t.dataDescription=ll.getDataDescriptionFromExtent(t.extent),e.modified()),i},e.setDimensions=function(){let n,r,o;if(t.deleted)cl("instance deleted - cannot call any method");else{if(1===arguments.length){const e=arguments.length<=0?void 0:arguments[0];n=e[0],r=e[1],o=e[2]}else{if(3!==arguments.length)return void cl("Bad dimension specification");n=arguments.length<=0?void 0:arguments[0],r=arguments.length<=1?void 0:arguments[1],o=arguments.length<=2?void 0:arguments[2]}e.setExtent(0,n-1,0,r-1,0,o-1)}},e.getDimensions=()=>[t.extent[1]-t.extent[0]+1,t.extent[3]-t.extent[2]+1,t.extent[5]-t.extent[4]+1],e.getNumberOfCells=()=>{const t=e.getDimensions();let n=1;for(let e=0;e<3;e++){if(0===t[e])return 0;t[e]>1&&(n*=t[e]-1)}return n},e.getNumberOfPoints=()=>{const t=e.getDimensions();return t[0]*t[1]*t[2]},e.getPoint=n=>{const r=e.getDimensions();if(0===r[0]||0===r[1]||0===r[2])return cl("Requesting a point from an empty image."),null;const o=new Float64Array(3);switch(t.dataDescription){case al.EMPTY:return null;case al.SINGLE_POINT:break;case al.X_LINE:o[0]=n;break;case al.Y_LINE:o[1]=n;break;case al.Z_LINE:o[2]=n;break;case al.XY_PLANE:o[0]=n%r[0],o[1]=n/r[0];break;case al.YZ_PLANE:o[1]=n%r[1],o[2]=n/r[1];break;case al.XZ_PLANE:o[0]=n%r[0],o[2]=n/r[0];break;case al.XYZ_GRID:o[0]=n%r[0],o[1]=n/r[0]%r[1],o[2]=n/(r[0]*r[1]);break;default:cl("Invalid dataDescription")}const a=[0,0,0];return e.indexToWorld(o,a),a},e.getBounds=()=>e.extentToBounds(e.getSpatialExtent()),e.extentToBounds=e=>ki.transformBounds(e,t.indexToWorld),e.getSpatialExtent=()=>ki.inflate([...t.extent],.5),e.computeTransforms=()=>{w(t.indexToWorld,t.origin),t.indexToWorld[0]=t.direction[0],t.indexToWorld[1]=t.direction[1],t.indexToWorld[2]=t.direction[2],t.indexToWorld[4]=t.direction[3],t.indexToWorld[5]=t.direction[4],t.indexToWorld[6]=t.direction[5],t.indexToWorld[8]=t.direction[6],t.indexToWorld[9]=t.direction[7],t.indexToWorld[10]=t.direction[8],x(t.indexToWorld,t.indexToWorld,t.spacing),h(t.worldToIndex,t.indexToWorld)},e.indexToWorld=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.indexToWorld),n},e.indexToWorldVec3=e.indexToWorld,e.worldToIndex=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return Vn(n,e,t.worldToIndex),n},e.worldToIndexVec3=e.worldToIndex,e.indexToWorldBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.indexToWorld,n)},e.worldToIndexBounds=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];return ki.transformBounds(e,t.worldToIndex,n)},e.onModified(e.computeTransforms),e.computeTransforms(),e.getCenter=()=>ki.getCenter(e.getBounds()),e.computeHistogram=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;const r=[0,0,0,0,0,0];e.worldToIndexBounds(t,r);const o=[0,0,0],a=[0,0,0];ki.computeCornerPoints(r,o,a),ua(o,o),ua(a,a);const i=e.getDimensions();Ea(o,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],o),Ea(a,[0,0,0],[i[0]-1,i[1]-1,i[2]-1],a);const s=i[0],l=i[0]*i[1],c=e.getPointData().getScalars().getData();let u=-1/0,d=1/0,p=0,f=0,g=0;for(let e=o[2];e<=a[2];e++)for(let t=o[1];t<=a[1];t++){let i=o[0]+t*s+e*l;for(let s=o[0];s<=a[0];s++){if(!n||n([s,t,e],r)){const e=c[i];e>u&&(u=e),e<d&&(d=e),p+=e*e,f+=e,g+=1}++i}}const m=g>0?f/g:0,h=g?Math.abs(p/g-m*m):0,v=Math.sqrt(h);return{minimum:d,maximum:u,average:m,variance:h,sigma:v,count:g}},e.computeIncrements=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;const n=[];let r=t;for(let t=0;t<3;++t)n[t]=r,r*=e[2*t+1]-e[2*t]+1;return n},e.computeOffsetIndex=t=>{let[n,r,o]=t;const a=e.getExtent(),i=e.getPointData().getScalars().getNumberOfComponents(),s=e.computeIncrements(a,i);return Math.floor((Math.round(n)-a[0])*s[0]+(Math.round(r)-a[2])*s[1]+(Math.round(o)-a[4])*s[2])},e.getOffsetIndexFromWorld=t=>{const n=e.getExtent(),r=e.worldToIndex(t);for(let e=0;e<3;++e)if(r[e]<n[2*e]||r[e]>n[2*e+1])return cl(`GetScalarPointer: Pixel ${r} is not in memory. Current extent = ${n}`),NaN;return e.computeOffsetIndex(r)},e.getScalarValueFromWorld=function(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=e.getPointData().getScalars().getNumberOfComponents();if(n<0||n>=r)return cl(`GetScalarPointer: Scalar Component ${n} is not within bounds. Current Scalar numberOfComponents: ${r}`),NaN;const o=e.getOffsetIndexFromWorld(t);return Number.isNaN(o)?o:e.getPointData().getScalars().getComponent(o,n)}}const dl={direction:null,indexToWorld:null,worldToIndex:null,spacing:[1,1,1],origin:[0,0,0],extent:[0,-1,0,-1,0,-1],dataDescription:al.EMPTY};function pl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dl,n),ol.extend(e,t,n),t.direction?Array.isArray(t.direction)&&(t.direction=new Float64Array(t.direction.slice(0,9))):t.direction=pe(new Float64Array(9)),t.indexToWorld=new Float64Array(16),t.worldToIndex=new Float64Array(16),jt.get(e,t,["indexToWorld","worldToIndex"]),jt.setGetArray(e,t,["origin","spacing"],3),jt.setGetArray(e,t,["direction"],9),jt.getArray(e,t,["extent"],6),ul(e,t)}var fl={newInstance:jt.newInstance(pl,"vtkImageData"),extend:pl};const gl={LUMINANCE:1,LUMINANCE_ALPHA:2,RGB:3,RGBA:4};var ml={VectorMode:{MAGNITUDE:0,COMPONENT:1,RGBCOLORS:2},ScalarMappingTarget:gl},hl={ColorMode:{DEFAULT:0,MAP_SCALARS:1,DIRECT_SCALARS:2},GetArray:{BY_ID:0,BY_NAME:1},ScalarMode:{DEFAULT:0,USE_POINT_DATA:1,USE_CELL_DATA:2,USE_POINT_FIELD_DATA:3,USE_CELL_FIELD_DATA:4,USE_FIELD_DATA:5}};const{ScalarMappingTarget:vl,VectorMode:yl}=ml,{VtkDataTypes:Tl}=Ds,{ColorMode:bl}=hl,{vtkErrorMacro:xl}=jt;function Cl(e){return e}function Sl(e){return Math.floor(255*e+.5)}function Al(e,t){t.classHierarchy.push("vtkScalarsToColors"),e.setVectorModeToMagnitude=()=>e.setVectorMode(yl.MAGNITUDE),e.setVectorModeToComponent=()=>e.setVectorMode(yl.COMPONENT),e.setVectorModeToRGBColors=()=>e.setVectorMode(yl.RGBCOLORS),e.build=()=>{},e.isOpaque=()=>!0,e.setAnnotations=(n,r)=>{if(!(n&&!r||!n&&r))if(n&&r&&n.length!==r.length)xl("Values and annotations do not have the same number of tuples so ignoring");else{if(t.annotationArray=[],r&&n){const e=r.length;for(let o=0;o<e;o++)t.annotationArray.push({value:n[o],annotation:String(r[o])})}e.updateAnnotatedValueMap(),e.modified()}},e.setAnnotation=(n,r)=>{let o=e.checkForAnnotatedValue(n),a=!1;return o>=0?t.annotationArray[o].annotation!==r&&(t.annotationArray[o].annotation=r,a=!0):(t.annotationArray.push({value:n,annotation:r}),o=t.annotationArray.length-1,a=!0),a&&(e.updateAnnotatedValueMap(),e.modified()),o},e.getNumberOfAnnotatedValues=()=>t.annotationArray.length,e.getAnnotatedValue=e=>e<0||e>=t.annotationArray.length?null:t.annotationArray[e].value,e.getAnnotation=e=>void 0===t.annotationArray[e]?null:t.annotationArray[e].annotation,e.getAnnotatedValueIndex=n=>t.annotationArray.length?e.checkForAnnotatedValue(n):-1,e.removeAnnotation=n=>{const r=e.checkForAnnotatedValue(n),o=r>=0;return o&&(t.annotationArray.splice(r,1),e.updateAnnotatedValueMap(),e.modified()),o},e.resetAnnotations=()=>{t.annotationArray=[],t.annotatedValueMap=[],e.modified()},e.getAnnotationColor=(n,r)=>{if(t.indexedLookup){const t=e.getAnnotatedValueIndex(n);e.getIndexedColor(t,r)}else e.getColor(parseFloat(n),r),r[3]=1},e.checkForAnnotatedValue=t=>e.getAnnotatedValueIndexInternal(t),e.getAnnotatedValueIndexInternal=e=>{if(void 0!==t.annotatedValueMap[e]){const n=t.annotationArray.length;return t.annotatedValueMap[e]%n}return-1},e.getIndexedColor=(e,t)=>{t[0]=0,t[1]=0,t[2]=0,t[3]=0},e.updateAnnotatedValueMap=()=>{t.annotatedValueMap=[];const e=t.annotationArray.length;for(let n=0;n<e;n++)t.annotatedValueMap[t.annotationArray[n].value]=n},e.mapScalars=(t,n,r)=>{const o=t.getNumberOfComponents();let a=null;if(n===bl.DEFAULT&&(t.getDataType()===Tl.UNSIGNED_CHAR||t.getDataType()===Tl.UNSIGNED_CHAR_CLAMPED)||n===bl.DIRECT_SCALARS&&t)a=e.convertToRGBA(t,o,t.getNumberOfTuples());else{const n={type:"vtkDataArray",name:"temp",numberOfComponents:4,dataType:Tl.UNSIGNED_CHAR},i=jt.newTypedArray(n.dataType,4*t.getNumberOfTuples());n.values=i,n.size=i.length,a=Ds.newInstance(n);let s=r;s<0&&o>1?e.mapVectorsThroughTable(t,a,vl.RGBA,-1,-1):(s<0&&(s=0),s>=o&&(s=o-1),e.mapScalarsThroughTable(t,a,vl.RGBA,s))}return a},e.mapVectorsToMagnitude=(e,t,n)=>{const r=e.getNumberOfTuples(),o=e.getNumberOfComponents(),a=t.getData(),i=e.getData();for(let e=0;e<r;e++){let t=0;for(let r=0;r<n;r++)t+=i[e*o+r]*i[e*o+r];a[e]=Math.sqrt(t)}},e.mapVectorsThroughTable=(t,n,r,o,a)=>{let i=e.getVectorMode(),s=a,l=o;const c=t.getNumberOfComponents();i===yl.COMPONENT?(-1===l&&(l=e.getVectorComponent()),l<0&&(l=0),l>=c&&(l=c-1)):(-1===s&&(s=e.getVectorSize()),s<=0?(l=0,s=c):(l<0&&(l=0),l>=c&&(l=c-1),l+s>c&&(s=c-l)),i!==yl.MAGNITUDE||1!==c&&1!==s||(i=yl.COMPONENT));let u=0;switch(l>0&&(u=l),i){case yl.COMPONENT:e.mapScalarsThroughTable(t,n,r,u);break;case yl.RGBCOLORS:break;case yl.MAGNITUDE:default:{const o=Ds.newInstance({numberOfComponents:1,values:new Float32Array(t.getNumberOfTuples())});e.mapVectorsToMagnitude(t,o,s),e.mapScalarsThroughTable(o,n,r,0);break}}},e.luminanceToRGBA=(e,t,n,r)=>{const o=r(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=1){const t=r(a[e]);i[4*l]=t,i[4*l+1]=t,i[4*l+2]=t,i[4*l+3]=o,l++}},e.luminanceAlphaToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=2){const t=r(o[e]);a[s]=t,a[s+1]=t,a[s+2]=t,a[s+3]=r(o[e+1])*n,s+=4}},e.rGBToRGBA=(e,t,n,r)=>{const o=Sl(n),a=t.getData(),i=e.getData(),s=a.length;let l=0;for(let e=0;e<s;e+=3)i[4*l]=r(a[e]),i[4*l+1]=r(a[e+1]),i[4*l+2]=r(a[e+2]),i[4*l+3]=o,l++},e.rGBAToRGBA=(e,t,n,r)=>{const o=t.getData(),a=e.getData(),i=o.length;let s=0;for(let e=0;e<i;e+=4)a[4*s]=r(o[e]),a[4*s+1]=r(o[e+1]),a[4*s+2]=r(o[e+2]),a[4*s+3]=r(o[e+3])*n,s++},e.convertToRGBA=(n,r,o)=>{let{alpha:a}=t;if(4===r&&a>=1&&n.getDataType()===Tl.UNSIGNED_CHAR)return n;const i=Ds.newInstance({numberOfComponents:4,empty:!0,size:4*o,dataType:Tl.UNSIGNED_CHAR});if(o<=0)return i;a=a>0?a:0,a=a<1?a:1;let s=Cl;switch(n.getDataType()!==Tl.FLOAT&&n.getDataType()!==Tl.DOUBLE||(s=Sl),r){case 1:e.luminanceToRGBA(i,n,a,s);break;case 2:e.luminanceAlphaToRGBA(i,n,s);break;case 3:e.rGBToRGBA(i,n,a,s);break;case 4:e.rGBAToRGBA(i,n,a,s);break;default:return xl("Cannot convert colors"),null}return i},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>16777216,e.setRange=(t,n)=>e.setMappingRange(t,n),e.getRange=()=>e.getMappingRange(),e.areScalarsOpaque=(n,r,o)=>{if(!n)return e.isOpaque();const a=n.getNumberOfComponents();return(r!==bl.DEFAULT||n.getDataType()!==Tl.UNSIGNED_CHAR)&&r!==bl.DIRECT_SCALARS||(3===a||1===a?t.alpha>=1:255===n.getRange(a-1)[0])}}const Il={alpha:1,vectorComponent:0,vectorSize:-1,vectorMode:yl.COMPONENT,mappingRange:null,annotationArray:null,annotatedValueMap:null,indexedLookup:!1};function wl(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Il,n),jt.obj(e,t),t.mappingRange=[0,255],t.annotationArray=[],t.annotatedValueMap=[],jt.setGet(e,t,["vectorSize","vectorComponent","vectorMode","alpha","indexedLookup"]),jt.setArray(e,t,["mappingRange"],2),jt.getArray(e,t,["mappingRange"]),Al(e,t)}var Pl={newInstance:jt.newInstance(wl,"vtkScalarsToColors"),extend:wl,...ml};const{vtkErrorMacro:Ol}=jt;function Rl(e,t){t.classHierarchy.push("vtkLookupTable"),e.isOpaque=()=>{if(t.opaqueFlagBuildTime.getMTime()<e.getMTime()){let e=!0;t.nanColor[3]<1&&(e=0),t.useBelowRangeColor&&t.belowRangeColor[3]<1&&(e=0),t.useAboveRangeColor&&t.aboveRangeColor[3]<1&&(e=0);for(let n=3;n<t.table.length&&e;n+=4)t.table[n]<255&&(e=!1);t.opaqueFlag=e,t.opaqueFlagBuildTime.modified()}return t.opaqueFlag},e.usingLogScale=()=>!1,e.getNumberOfAvailableColors=()=>t.table.length/4-3,e.linearIndexLookup=(e,t)=>{let n=0;const r=Number(e);return r<t.range[0]?n=t.maxIndex+0+1.5:r>t.range[1]?n=t.maxIndex+1+1.5:(n=(r+t.shift)*t.scale,n=n<t.maxIndex?n:t.maxIndex),Math.floor(n)},e.linearLookup=(t,n,r)=>{let o=0;o=_a(t)?Math.floor(r.maxIndex+1.5+2):e.linearIndexLookup(t,r);const a=4*o;return n.slice(a,a+4)},e.indexedLookupFunction=(n,r,o)=>{let a=e.getAnnotatedValueIndexInternal(n);-1===a&&(a=t.numberOfColors+2);const i=4*a;return[r[i],r[i+1],r[i+2],r[i+3]]},e.lookupShiftAndScale=(e,t)=>{t.shift=-e[0],t.scale=Number.MAX_VALUE,e[1]>e[0]&&(t.scale=(t.maxIndex+1)/(e[1]-e[0]))},e.mapScalarsThroughTable=(n,r,o,a)=>{let i=e.linearLookup;t.indexedLookup&&(i=e.indexedLookupFunction);const s=e.getMappingRange(),l={maxIndex:e.getNumberOfColors()-1,range:s,shift:0,scale:0};e.lookupShiftAndScale(s,l);const c=e.getAlpha(),u=n.getNumberOfTuples(),d=n.getNumberOfComponents(),p=r.getData(),f=n.getData();if(c>=1){if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=n[3]}}else if(o===gl.RGBA)for(let e=0;e<u;e++){const n=i(f[e*d+a],t.table,l);p[4*e]=n[0],p[4*e+1]=n[1],p[4*e+2]=n[2],p[4*e+3]=Math.floor(n[3]*c+.5)}},e.forceBuild=()=>{let n=0,r=0,o=0,a=0;const i=t.numberOfColors-1;i&&(n=(t.hueRange[1]-t.hueRange[0])/i,r=(t.saturationRange[1]-t.saturationRange[0])/i,o=(t.valueRange[1]-t.valueRange[0])/i,a=(t.alphaRange[1]-t.alphaRange[0])/i),t.table.length=4*i+16;const s=[],l=[];for(let e=0;e<=i;e++)s[0]=t.hueRange[0]+e*n,s[1]=t.saturationRange[0]+e*r,s[2]=t.valueRange[0]+e*o,xa(s,l),l[3]=t.alphaRange[0]+e*a,t.table[4*e]=255*l[0]+.5,t.table[4*e+1]=255*l[1]+.5,t.table[4*e+2]=255*l[2]+.5,t.table[4*e+3]=255*l[3]+.5;e.buildSpecialColors(),t.buildTime.modified()},e.setTable=n=>{if(Array.isArray(n)){const r=n[0].length;t.numberOfColors=n.length;const o=4-r;let a=0;for(let e=0;e<t.numberOfColors;e++)t.table[4*e]=255,t.table[4*e+1]=255,t.table[4*e+2]=255,t.table[4*e+3]=255;for(let e=0;e<n.length;e++){const i=n[e];for(let e=0;e<r;e++)t.table[a++]=i[e];a+=o}return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0}if(4!==n.getNumberOfComponents())return Ol("Expected 4 components for RGBA colors"),!1;if(n.getDataType()!==ys.UNSIGNED_CHAR)return Ol("Expected unsigned char values for RGBA colors"),!1;t.numberOfColors=n.getNumberOfTuples();const r=n.getData();t.table.length=r.length;for(let e=0;e<r.length;e++)t.table[e]=r[e];return e.buildSpecialColors(),t.insertTime.modified(),e.modified(),!0},e.buildSpecialColors=()=>{const{numberOfColors:e}=t,n=t.table;let r=4*(e+0);t.useBelowRangeColor||0===e?(n[r]=255*t.belowRangeColor[0]+.5,n[r+1]=255*t.belowRangeColor[1]+.5,n[r+2]=255*t.belowRangeColor[2]+.5,n[r+3]=255*t.belowRangeColor[3]+.5):(n[r]=n[0],n[r+1]=n[1],n[r+2]=n[2],n[r+3]=n[3]),r=4*(e+1),t.useAboveRangeColor||0===e?(n[r]=255*t.aboveRangeColor[0]+.5,n[r+1]=255*t.aboveRangeColor[1]+.5,n[r+2]=255*t.aboveRangeColor[2]+.5,n[r+3]=255*t.aboveRangeColor[3]+.5):(n[r]=n[4*(e-1)+0],n[r+1]=n[4*(e-1)+1],n[r+2]=n[4*(e-1)+2],n[r+3]=n[4*(e-1)+3]),r=4*(e+2),n[r]=255*t.nanColor[0]+.5,n[r+1]=255*t.nanColor[1]+.5,n[r+2]=255*t.nanColor[2]+.5,n[r+3]=255*t.nanColor[3]+.5},e.build=()=>{(t.table.length<1||e.getMTime()>t.buildTime.getMTime()&&t.insertTime.getMTime()<=t.buildTime.getMTime())&&e.forceBuild()},t.table.length>0&&(e.buildSpecialColors(),t.insertTime.modified())}const Ml={numberOfColors:256,hueRange:[0,.66667],saturationRange:[1,1],valueRange:[1,1],alphaRange:[1,1],nanColor:[.5,0,0,1],belowRangeColor:[0,0,0,1],aboveRangeColor:[1,1,1,1],useAboveRangeColor:!1,useBelowRangeColor:!1,alpha:1};function El(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ml,n),Pl.extend(e,t,n),t.table||(t.table=[]),t.buildTime={},jt.obj(t.buildTime),t.opaqueFlagBuildTime={},jt.obj(t.opaqueFlagBuildTime,{mtime:0}),t.insertTime={},jt.obj(t.insertTime,{mtime:0}),jt.get(e,t,["buildTime"]),jt.setGet(e,t,["numberOfColors","useAboveRangeColor","useBelowRangeColor"]),jt.setArray(e,t,["alphaRange","hueRange","saturationRange","valueRange"],2),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["hueRange","saturationRange","valueRange","alphaRange","nanColor","belowRangeColor","aboveRangeColor"]),Rl(e,t)}var Dl={newInstance:jt.newInstance(El,"vtkLookupTable"),extend:El};const Vl={Off:0,PolygonOffset:1};let Ll=Vl.PolygonOffset,Bl=Vl.Off;const Nl=["VTK_RESOLVE_OFF","VTK_RESOLVE_POLYGON_OFFSET"];function _l(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const t=Bl===e;return Bl=e,t}var Fl={Resolve:Vl,getResolveCoincidentTopologyAsString:function(){return Nl[Bl]},getResolveCoincidentTopologyPolygonOffsetFaces:function(){return Ll},getResolveCoincidentTopology:function(){return Bl},setResolveCoincidentTopology:_l,setResolveCoincidentTopologyPolygonOffsetFaces:function(e){const t=Ll===e;return Ll=e,t},setResolveCoincidentTopologyToDefault:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToOff:function(){return _l(Vl.Off)},setResolveCoincidentTopologyToPolygonOffset:function(){return _l(Vl.PolygonOffset)}};function kl(e,t,n){n.forEach((n=>{e[`get${n.method}`]=()=>t[n.key],e[`set${n.method}`]=jt.objectSetterMap.object(e,t,{name:n.key,params:["factor","offset"]})}))}const Gl=["Polygon","Line","Point"],Ul={};kl(Ul,{Polygon:{factor:2,offset:0},Line:{factor:1,offset:-1},Point:{factor:0,offset:-2}},Gl.map((e=>({key:e,method:`ResolveCoincidentTopology${e}OffsetParameters`}))));var zl={implementCoincidentTopologyMethods:function(e,t){void 0===t.resolveCoincidentTopology&&(t.resolveCoincidentTopology=!1),jt.setGet(e,t,["resolveCoincidentTopology"]),t.topologyOffset={Polygon:{factor:0,offset:0},Line:{factor:0,offset:0},Point:{factor:0,offset:0}},Object.keys(Fl).forEach((t=>{e[t]=Fl[t]})),Object.keys(Ul).forEach((t=>{e[t]=Ul[t]})),kl(e,t.topologyOffset,Gl.map((e=>({key:e,method:`RelativeCoincidentTopology${e}OffsetParameters`})))),e.getCoincidentTopologyPolygonOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyPolygonOffsetParameters(),n=e.getRelativeCoincidentTopologyPolygonOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyLineOffsetParameters=()=>{const t=Ul.getResolveCoincidentTopologyLineOffsetParameters(),n=e.getRelativeCoincidentTopologyLineOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}},e.getCoincidentTopologyPointOffsetParameter=()=>{const t=Ul.getResolveCoincidentTopologyPointOffsetParameters(),n=e.getRelativeCoincidentTopologyPointOffsetParameters();return{factor:t.factor+n.factor,offset:t.offset+n.offset}}},staticOffsetAPI:Ul,otherStaticMethods:Fl,CATEGORIES:Gl,Resolve:Vl};const Wl={MIN_KNOWN_PASS:0,ACTOR_PASS:0,COMPOSITE_INDEX_PASS:1,ID_LOW24:2,ID_HIGH24:3,MAX_KNOWN_PASS:3};var Hl={PassTypes:Wl};const{FieldAssociations:jl}=ol,{staticOffsetAPI:Kl,otherStaticMethods:$l}=zl,{ColorMode:ql,ScalarMode:Xl,GetArray:Yl}=hl,{VectorMode:Zl}=ml,{VtkDataTypes:Ql}=Ds;function Jl(e){return()=>jt.vtkErrorMacro(`vtkMapper::${e} - NOT IMPLEMENTED`)}function ec(e,t){const n=e[1]%2==0?1:-1;if(e[0]+=n,e[0]>=t[0]||e[0]<0){const r=e[2]%2==0?1:-1;e[0]-=n,e[1]+=r,(e[1]>=t[1]||e[1]<0)&&(e[1]-=r,e[2]++)}}function tc(e,t,n){const r=Math.floor(t),o=r%(2*n[0]);let a,i;o<n[0]?(e[0]=o,a=1,i=e[0]===n[0]-1):(e[0]=2*n[0]-1-o,a=-1,i=0===e[0]);const s=Math.floor(r/n[0]),l=s%(2*n[1]);let c,u;l<n[1]?(e[1]=l,c=1,u=e[1]===n[1]-1):(e[1]=2*n[1]-1-l,c=-1,u=0===e[1]),e[2]=Math.floor(s/n[1]);const d=t-r;i?u?e[2]+=d:e[1]+=c*d:e[0]+=a*d,e[0]=(e[0]+.5)/n[0],e[1]=(e[1]+.5)/n[1],e[2]=(e[2]+.5)/n[2]}const nc=new WeakMap;function rc(e,t){t.classHierarchy.push("vtkMapper"),e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.setForceCompileOnly=e=>{t.forceCompileOnly=e},e.setSelectionWebGLIdsToVTKIds=e=>{t.selectionWebGLIdsToVTKIds=e},e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(ql,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(Xl,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFlag:!1};let i=null,s=!1;if(n===Xl.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===Xl.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===Xl.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===Xl.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===Xl.USE_FIELD_DATA){const t=e.getFieldData();i=r===Yl.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.mapScalars=(n,r)=>{const{scalars:o,cellFlag:a}=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName);if(t.areScalarsMappedFromCells=a,!o)return t.colorCoordinates=null,t.colorTextureMap=null,void(t.colorMapColors=null);const i=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString!==i){if(t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),e.canUseTextureMapForColoring(o,a))t.mapScalarsToTexture(o,a,r);else{t.colorCoordinates=null,t.colorTextureMap=null;const n=e.getLookupTable();n&&(n.build(),t.colorMapColors=n.mapScalars(o,t.colorMode,t.fieldDataTupleId))}t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`}},t.mapScalarsToTexture=(n,r,o)=>{const a=t.lookupTable.getRange(),i=t.lookupTable.usingLogScale();i&&Dl.getLogRange(a,a);const s=t.lookupTable.getAlpha();if(t.colorMapColors=null,null==t.colorTextureMap||e.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getMTime()>t.colorTextureMap.getMTime()||t.lookupTable.getAlpha()!==o){t.lookupTable.setAlpha(o),t.colorTextureMap=null,t.lookupTable.build();const e=t.lookupTable.getNumberOfAvailableColors(),n=2048,u=n**3-3,d=4094,p=2,f=r?u:d;t.numberOfColorsInRange=Math.min(Math.max(e,p),f);const g=t.numberOfColorsInRange+3,m=t.numberOfColorsInRange+2,h=r?[Math.min(Math.ceil(g/n**0),n),Math.min(Math.ceil(g/n**1),n),Math.min(Math.ceil(g/n**2),n)]:[m,2,1],v=h[0]*h[1]*h[2],y=new Float64Array(v);y.fill(NaN);const T=t.numberOfColorsInRange,b=T+2,x=[0,0,0],C=a[0],S=a[1]-a[0];for(let e=0;e<b;++e){const t=C+S*(e-1)/(T-1);y[(c=h,(l=x)[0]+c[0]*(l[1]+c[1]*l[2]))]=i?10**t:t,ec(x,h)}const A=Ds.newInstance({numberOfComponents:1,values:y}),I=t.lookupTable.mapScalars(A,t.colorMode,0);t.colorTextureMap=fl.newInstance(),t.colorTextureMap.setDimensions(h),t.colorTextureMap.getPointData().setScalars(I),t.lookupTable.setAlpha(s)}var l,c;const u=t.lookupTable.getVectorMode()===Zl.MAGNITUDE&&n.getNumberOfComponents()>1?-1:t.lookupTable.getVectorComponent();t.colorCoordinates=function(e,t,n,r,o,a,i){const s=new Array(arguments.length);for(let e=0;e<arguments.length;++e){const t=arguments[e];s[e]=t.getMTime?.()??t}const l=s.join("/"),c=nc.get(e);if(c&&c.stringHash===l)return c.textureCoordinates;const 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fc={newInstance:jt.newInstance(pc,"vtkCellArray"),extend:pc,...cc};const{vtkErrorMacro:gc}=jt,mc=[1,-1,1,-1,1,-1];function hc(e,t){t.classHierarchy.push("vtkPoints"),e.getNumberOfPoints=e.getNumberOfTuples,e.setNumberOfPoints=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3;e.getNumberOfPoints()!==n&&(t.size=n*r,t.values=jt.newTypedArray(t.dataType,t.size),e.setNumberOfComponents(r),e.modified())},e.setPoint=function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.setTuple(t,r)},e.getPoint=e.getTuple,e.findPoint=e.findTuple,e.insertNextPoint=(t,n,r)=>e.insertNextTuple([t,n,r]),e.getBounds=()=>{if(3===e.getNumberOfComponents()){const n=e.getRange(0);t.bounds[0]=n[0],t.bounds[1]=n[1];const r=e.getRange(1);t.bounds[2]=r[0],t.bounds[3]=r[1];const o=e.getRange(2);return t.bounds[4]=o[0],t.bounds[5]=o[1],t.bounds}if(2!==e.getNumberOfComponents())return gc(`getBounds called on an array with components of\n        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n},e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.deepCopy=e=>{e.initialize(t.points,t.pointsIds)},e.getCellDimension=()=>{},e.intersectWithLine=(e,t,n,r,o,a,i)=>{},e.evaluatePosition=(e,t,n,r,o,a)=>{jt.vtkErrorMacro("vtkCell.evaluatePosition is not implemented.")}}const xc={bounds:[-1,-1,-1,-1,-1,-1],pointsIds:[]};function Cc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xc,n),jt.obj(e,t),t.points||(t.points=Tc.newInstance()),jt.get(e,t,["points","pointsIds"]),bc(e,t)}var Sc={newInstance:jt.newInstance(Cc,"vtkCell"),extend:Cc};function Ac(e,t){t.classHierarchy.push("vtkCellLinks"),e.buildLinks=n=>{const r=n.getPoints().getNumberOfPoints(),o=n.getNumberOfCells(),a=new Uint32Array(r);if(n.isA("vtkPolyData")){for(let t=0;t<o;++t){const{cellPointIds:r}=n.getCellPoints(t);r.forEach((t=>{e.incrementLinkCount(t)}))}e.allocateLinks(r),t.maxId=r-1;for(let 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0!==arguments[1]?arguments[1]:1e3;t.array=Array(e).fill().map((()=>({ncells:0,cells:null}))),t.extend=n,t.maxId=-1},e.initialize=()=>{t.array=null},e.getLink=e=>t.array[e],e.getNcells=e=>t.array[e].ncells,e.getCells=e=>t.array[e].cells,e.insertNextPoint=e=>{t.array.push({ncells:e,cells:Array(e)}),++t.maxId},e.insertNextCellReference=(e,n)=>{t.array[e].cells[t.array[e].ncells++]=n},e.deletePoint=e=>{t.array[e].ncells=0,t.array[e].cells=null},e.removeCellReference=(e,n)=>{t.array[n].cells=t.array[n].cells.filter((t=>t!==e)),t.array[n].ncells=t.array[n].cells.length},e.addCellReference=(e,n)=>{t.array[n].cells[t.array[n].ncells++]=e},e.resizeCellList=(e,n)=>{t.array[e].cells.length=n},e.squeeze=()=>{!function(e,t){let n=t;for(t>=e.array.length&&(n+=e.array.length);n>e.array.length;)e.array.push({ncells:0,cells:null});e.array.length=n}(t,t.maxId+1)},e.reset=()=>{t.maxId=-1},e.deepCopy=e=>{t.array=[...e.array],t.extend=e.extend,t.maxId=e.maxId},e.incrementLinkCount=e=>{++t.array[e].ncells},e.allocateLinks=e=>{for(let n=0;n<e;++n)t.array[n].cells=new Array(t.array[n].ncells)},e.insertCellReference=(e,n,r)=>{t.array[e].cells[n]=r}}const Ic={array:null,maxId:0,extend:0};function wc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ic,n),jt.obj(e,t),Ac(e,t)}var Pc={newInstance:jt.newInstance(wc,"vtkCellLinks"),extend:wc};const Oc=0,Rc=1,Mc=2,Ec=3,Dc=4,Vc=5,Lc=6,Bc=7,Nc=9,_c=21,Fc=41,kc=42,Gc=["vtkEmptyCell","vtkVertex","vtkPolyVertex","vtkLine","vtkPolyLine","vtkTriangle","vtkTriangleStrip","vtkPolygon","vtkPixel","vtkQuad","vtkTetra","vtkVoxel","vtkHexahedron","vtkWedge","vtkPyramid","vtkPentagonalPrism","vtkHexagonalPrism","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkQuadraticEdge","vtkQuadraticTriangle","vtkQuadraticQuad","vtkQuadraticTetra","vtkQuadraticHexahedron","vtkQuadraticWedge","vtkQuadraticPyramid","vtkBiQuadraticQuad","vtkTriQuadraticHexahedron","vtkQuadraticLinearQuad","vtkQuadraticLinearWedge","vtkBiQuadraticQuadraticWedge","vtkBiQuadraticQuadraticHexahedron","vtkBiQuadraticTriangle","vtkCubicLine","vtkQuadraticPolygon","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkConvexPointSet","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","UnknownClass","vtkParametricCurve","vtkParametricSurface","vtkParametricTriSurface","vtkParametricQuadSurface","vtkParametricTetraRegion","vtkParametricHexRegion","UnknownClass","UnknownClass","UnknownClass","vtkHigherOrderEdge","vtkHigherOrderTriangle","vtkHigherOrderQuad","vtkHigherOrderPolygon","vtkHigherOrderTetrahedron","vtkHigherOrderWedge","vtkHigherOrderPyramid","vtkHigherOrderHexahedron"],Uc={getClassNameFromTypeId:function(e){return e<Gc.length?Gc[e]:"UnknownClass"},getTypeIdFromClassName:function(e){return Gc.findIndex(e)},isLinear:function(e){return e<_c||e===Fc||e===kc},hasSubCells:function(e){return e===Lc||e===Dc||e===Mc}};function zc(e,t){t.classHierarchy.push("vtkCellTypes"),e.allocate=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:512,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e3;t.size=e>0?e:1,t.extend=n>0?n:1,t.maxId=-1,t.typeArray=new Uint8Array(e),t.locationArray=new Uint32Array(e)},e.insertCell=(e,n,r)=>{t.typeArray[e]=n,t.locationArray[e]=r,e>t.maxId&&(t.maxId=e)},e.insertNextCell=(n,r)=>(e.insertCell(++t.maxId,n,r),t.maxId),e.setCellTypes=(e,n,r)=>{t.size=e,t.typeArray=n,t.locationArray=r,t.maxId=e-1},e.getCellLocation=e=>t.locationArray[e],e.deleteCell=e=>{t.typeArray[e]=Oc},e.getNumberOfTypes=()=>t.maxId+1,e.isType=t=>{const n=e.getNumberOfTypes();for(let r=0;r<n;++r)if(t===e.getCellType(r))return!0;return!1},e.insertNextType=t=>e.insertNextCell(t,-1),e.getCellType=e=>t.typeArray[e],e.reset=()=>{t.maxId=-1},e.deepCopy=n=>{e.allocate(n.getSize(),n.getExtend()),t.typeArray.set(n.getTypeArray()),t.locationArray.set(n.getLocationArray()),t.maxId=n.getMaxId()}}const Wc={size:0,maxId:-1,extend:1e3};function Hc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wc,n),jt.obj(e,t),jt.get(e,t,["size","maxId","extend"]),jt.getArray(e,t,["typeArray","locationArray"]),zc(e,t)}var jc={newInstance:jt.newInstance(Hc,"vtkCellTypes"),extend:Hc,...Uc},Kc={IntersectionState:{NO_INTERSECTION:0,YES_INTERSECTION:1,ON_LINE:2}};const{IntersectionState:$c}=Kc;function qc(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;const o={t:Number.MIN_VALUE,distance:0},a=[];let i;a[0]=n[0]-t[0],a[1]=n[1]-t[1],a[2]=n[2]-t[2];const s=a[0]*(e[0]-t[0])+a[1]*(e[1]-t[1])+a[2]*(e[2]-t[2]),l=jo(a,a);let c=1e-5*s;return 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u=[],d=[],p=Xc(e,n,l,c,u,d);var f;if(i.t=u[0],i.betweenPoints=(f=i.t)>=0&&f<=1,a[0]=d[0],p===$c.YES_INTERSECTION){for(let t=0;t<3;t++)o[t]=l[t]+a[0]*(c[t]-l[t]),s[t]=e[t]+i.t*(n[t]-e[t]);if(Yo(o,s)<=r*r)return i.intersect=1,i}else{let t;if(i.t<0)return t=qc(e,l,c,o),t.distance<=r*r?(i.t=0,i.intersect=1,i.betweenPoints=!0,i):i;if(i.t>1)return t=qc(n,l,c,o),t.distance<=r*r?(i.t=1,i.intersect=1,i.betweenPoints=!0,i):i;if(a[0]<0)return a[0]=0,t=qc(l,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i;if(a[0]>1)return a[0]=1,t=qc(c,e,n,o),i.t=t.t,t.distance<=r*r?(i.intersect=1,i):i}return i},e.evaluateLocation=(e,n,r)=>{const o=[],a=[];t.points.getPoint(0,o),t.points.getPoint(1,a);for(let t=0;t<3;t++)n[t]=o[t]+e[0]*(a[t]-o[t]);r[0]=1-e[0],r[1]=e[0]},e.evaluateOrientation=(e,n,r)=>!!t.orientations&&(function(e,t,n,r){var o,i,s,l,c,u=t[0],d=t[1],p=t[2],f=t[3],g=n[0],m=n[1],h=n[2],v=n[3];(i=u*g+d*m+p*h+f*v)<0&&(i=-i,g=-g,m=-m,h=-h,v=-v),1-i>a?(o=Math.acos(i),s=Math.sin(o),l=Math.sin((1-r)*o)/s,c=Math.sin(r*o)/s):(l=1-r,c=r),e[0]=l*u+c*g,e[1]=l*d+c*m,e[2]=l*p+c*h,e[3]=l*f+c*v}(n,t.orientations[0],t.orientations[1],e[0]),r[0]=1-e[0],r[1]=e[0],!0)}const Qc={orientations:null};function Jc(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Qc,n),Sc.extend(e,t,n),jt.setGet(e,t,["orientations"]),Zc(e,t)}var eu={newInstance:jt.newInstance(Jc,"vtkLine"),extend:Jc,...Yc,...Kc};function tu(e,t){t.classHierarchy.push("vtkPointSet"),t.points?t.points=We(t.points):t.points=Tc.newInstance(),e.getNumberOfPoints=()=>t.points.getNumberOfPoints(),e.getBounds=()=>t.points.getBounds(),e.computeBounds=()=>{e.getBounds()};const n=e.shallowCopy;e.shallowCopy=function(e){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];n(e,r),t.points=Tc.newInstance(),t.points.shallowCopy(e.getPoints())}}const nu={};function ru(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nu,n),ol.extend(e,t,n),jt.setGet(e,t,["points"]),tu(e,t)}var ou={newInstance:jt.newInstance(ru,"vtkPointSet"),extend:ru};function au(e,t,n,r){const o=n[0]-t[0],a=n[1]-t[1],i=n[2]-t[2],s=e[0]-t[0],l=e[1]-t[1],c=e[2]-t[2];r[0]=a*c-i*l,r[1]=i*s-o*c,r[2]=o*l-a*s}function iu(e,t,n,r){au(e,t,n,r);const o=Math.sqrt(r[0]*r[0]+r[1]*r[1]+r[2]*r[2]);0!==o&&(r[0]/=o,r[1]/=o,r[2]/=o)}const su={computeNormalDirection:au,computeNormal:iu,intersectWithTriangle:function(e,t,n,r,o,a){let i=arguments.length>6&&void 0!==arguments[6]?arguments[6]:1e-6,s=!1;const l=[],c=[],u=[],d=[],p=[];iu(e,t,n,d),iu(r,o,a,p);const f=-jo(d,e),g=-jo(p,r),m=[jo(p,e)+g,jo(p,t)+g,jo(p,n)+g];if(m[0]*m[1]>i&&m[0]*m[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const h=[jo(d,r)+f,jo(d,o)+f,jo(d,a)+f];if(h[0]*h[1]>i&&h[0]*h[2]>i)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Math.abs(d[0]-p[0])<1e-9&&Math.abs(d[1]-p[1])<1e-9&&Math.abs(d[2]-p[2])<1e-9&&Math.abs(f-g)<1e-9)return s=!0,{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};const v=[e,t,n],y=[r,o,a],T=jo(d,p),b=(f-g*T)/(T*T-1),x=(g-f*T)/(T*T-1),C=[b*d[0]+x*p[0],b*d[1]+x*p[1],b*d[2]+x*p[2]],S=Ko(d,p,[]);qo(S);let A=0,I=0;const w=[],P=[];let O,R,M=50,E=50;for(let t=0;t<3;t++){const n=t,o=(t+1)%3,a=ni.intersectWithLine(v[n],v[o],r,p);a.intersection&&a.t>0-i&&a.t<1+i&&(a.t<1+i&&a.t>1-i&&(M=A),w[A++]=jo(a.x,S)-jo(C,S));const s=ni.intersectWithLine(y[n],y[o],e,d);s.intersection&&s.t>0-i&&s.t<1+i&&(s.t<1+i&&s.t>1-i&&(E=I),P[I++]=jo(s.x,S)-jo(C,S))}if(A>2){A--;const e=w[2];w[2]=w[M],w[M]=e}if(I>2){I--;const e=P[2];P[2]=P[E],P[E]=e}if(2!==A||2!==I)return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(Number.isNaN(w[0])||Number.isNaN(w[1])||Number.isNaN(P[0])||Number.isNaN(P[1]))return{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u};if(w[0]>w[1]){const e=w[1];w[1]=w[0],w[0]=e}if(P[0]>P[1]){const e=P[1];P[1]=P[0],P[0]=e}return w[1]<P[0]||P[1]<w[0]?{intersect:!1,coplanar:s,pt1:l,pt2:c,surfaceId:u}:(w[0]<P[0]?w[1]<P[1]?(u[0]=2,u[1]=1,O=P[0],R=w[1]):(u[0]=2,u[1]=2,O=P[0],R=P[1]):w[1]<P[1]?(u[0]=1,u[1]=1,O=w[0],R=w[1]):(u[0]=1,u[1]=2,O=w[0],R=P[1]),Ho(C,S,O,l),Ho(C,S,R,c),{intersect:!0,coplanar:s,pt1:l,pt2:c,surfaceId:u})}};function lu(e,t){t.classHierarchy.push("vtkTriangle"),e.getCellDimension=()=>2,e.intersectWithLine=(n,r,o,a,i)=>{const s={subId:0,t:Number.MAX_VALUE,intersect:0,betweenPoints:!1};i[2]=0;const l=[],c=o*o,u=[],d=[],p=[];t.points.getPoint(0,u),t.points.getPoint(1,d),t.points.getPoint(2,p);const f=[],g=[];if(iu(u,d,p,f),0!==f[0]||0!==f[1]||0!==f[2]){const t=ni.intersectWithLine(n,r,u,f);if(s.betweenPoints=t.betweenPoints,s.t=t.t,a[0]=t.x[0],a[1]=t.x[1],a[2]=t.x[2],!t.intersection)return i[0]=0,i[1]=0,s.intersect=0,s;const o=e.evaluatePosition(a,l,i,g);if(o.evaluation>=0)return o.dist2<=c?(s.intersect=1,s):(s.intersect=o.evaluation,s)}const m=Yo(u,d),h=Yo(d,p),v=Yo(p,u);t.line||(t.line=eu.newInstance()),m>h&&m>v?(t.line.getPoints().setPoint(0,u),t.line.getPoints().setPoint(1,d)):h>v&&h>m?(t.line.getPoints().setPoint(0,d),t.line.getPoints().setPoint(1,p)):(t.line.getPoints().setPoint(0,p),t.line.getPoints().setPoint(1,u));const y=t.line.intersectWithLine(n,r,o,a,i);if(s.betweenPoints=y.betweenPoints,s.t=y.t,y.intersect){const e=[],t=[],n=[];for(let r=0;r<3;r++)e[r]=u[r]-p[r],t[r]=d[r]-p[r],n[r]=a[r]-p[r];return i[0]=jo(n,e)/v,i[1]=jo(n,t)/h,s.intersect=1,s}return i[0]=0,i[1]=0,s.intersect=0,s},e.evaluatePosition=(e,n,r,o)=>{const a={subId:0,dist2:0,evaluation:-1};let i,s;const l=[],c=[],u=[],d=[];let p;const f=[],g=[],m=[];let h=0,v=0;const y=[];let T,b,x,C=[];const S=[],A=[],I=[];a.subId=0,r[2]=0,t.points.getPoint(1,l),t.points.getPoint(2,c),t.points.getPoint(0,u),au(l,c,u,d),ni.generalizedProjectPoint(e,l,d,I);let w=0;for(i=0;i<3;i++)p=d[i]<0?-d[i]:d[i],p>w&&(w=p,v=i);for(s=0,i=0;i<3;i++)i!==v&&(y[s++]=i);for(i=0;i<2;i++)f[i]=I[y[i]]-u[y[i]],g[i]=l[y[i]]-u[y[i]],m[i]=c[y[i]]-u[y[i]];if(h=Jo(g,m),0===h)return r[0]=0,r[1]=0,a.evaluation=-1,a;if(r[0]=Jo(f,m)/h,r[1]=Jo(g,f)/h,o[0]=1-(r[0]+r[1]),o[1]=r[0],o[2]=r[1],o[0]>=0&&o[0]<=1&&o[1]>=0&&o[1]<=1&&o[2]>=0&&o[2]<=1)n&&(a.dist2=Yo(I,e),n[0]=I[0],n[1]=I[1],n[2]=I[2]),a.evaluation=1;else{let t;if(n)if(o[1]<0&&o[2]<0)for(T=Yo(e,u),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,u,c,t,A),T<b?(a.dist2=T,C=u):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else if(o[2]<0&&o[0]<0)for(T=Yo(e,l),b=eu.distanceToLine(e,l,u,t,S),x=eu.distanceToLine(e,l,c,t,A),T<b?(a.dist2=T,C=l):(a.dist2=b,C=S),x<a.dist2&&(a.dist2=x,C=A),i=0;i<3;i++)n[i]=C[i];else 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Su={newInstance:jt.newInstance(xu,"vtkTexture"),extend:xu,...Cu};const Au=[[-1,0,0],[1,0,0],[0,-1,0],[0,1,0],[0,0,-1],[0,0,1]],Iu=[[8,7,11,3],[9,1,10,5],[4,9,0,8],[2,11,6,10],[0,3,2,1],[4,5,6,7]],wu=[[0,1],[1,3],[2,3],[0,2],[4,5],[5,7],[6,7],[4,6],[0,4],[1,5],[3,7],[2,6]],Pu=[0,1,0,1,0,1,0,1,2,2,2,2],Ou=[[1,2],[1,2],[0,2],[0,2],[0,1],[0,1]],Ru=new Float64Array(3),Mu=new Float64Array(3),Eu=new Float64Array(3),Du=new Float64Array(3),Vu=new Float64Array(3),Lu=new Float64Array(3),Bu=new Float64Array(16);function Nu(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function _u(e){const t=[],n=[];for(let r=0;r<3;r++){const o=go().domain([e[2*r],e[2*r+1]]);t[r]=o.ticks(5);const a=o.tickFormat(5);n[r]=t[r].map(a)}return{ticks:t,tickStrings:n}}function Fu(e,t){t.classHierarchy.push("vtkCubeAxesActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.tmActor.addTexture(t.renderable.getTmTexture()),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),e.modified())},e.createPolyDataForOneLabel=(e,n,r,o,a,i,s)=>{const l=t.renderable.get_tmAtlas().get(e);if(!l)return;const c=t.renderable.getTextPolyData().getPoints().getData(),u=t.lastSize;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],Vn(Eu,Ru,r),Eu[0]+=.1,Vn(Mu,Eu,o),Pn(Vu,Mu,Ru),Eu[0]-=.1,Eu[1]+=.1,Vn(Mu,Eu,o),Pn(Lu,Mu,Ru);for(let e=0;e<3;e++)Vu[e]/=.05*u[0],Lu[e]/=.05*u[1];let d=s.ptIdx,p=s.cellIdx;Ru[0]=c[3*n],Ru[1]=c[3*n+1],Ru[2]=c[3*n+2],a[0]<-.5?On(Eu,Vu,a[0]*i-l.width):a[0]>.5?On(Eu,Vu,a[0]*i):On(Eu,Vu,a[0]*i-l.width/2),wn(Ru,Ru,Eu),On(Eu,Lu,a[1]*i-l.height/2),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[0],s.tcoords[2*d+1]=l.tcoords[1],d++,On(Eu,Vu,l.width),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[2],s.tcoords[2*d+1]=l.tcoords[3],d++,On(Eu,Lu,l.height),wn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[4],s.tcoords[2*d+1]=l.tcoords[5],d++,On(Eu,Vu,l.width),Pn(Ru,Ru,Eu),s.points[3*d]=Ru[0],s.points[3*d+1]=Ru[1],s.points[3*d+2]=Ru[2],s.tcoords[2*d]=l.tcoords[6],s.tcoords[2*d+1]=l.tcoords[7],d++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-3,s.polys[4*p+3]=d-2,p++,s.polys[4*p]=3,s.polys[4*p+1]=d-4,s.polys[4*p+2]=d-2,s.polys[4*p+3]=d-1,s.ptIdx+=4,s.cellIdx+=2},e.updateTexturePolyData=()=>{const n=t.camera.getCompositeProjectionMatrix(t.lastAspectRatio,-1,1);m(n,n);const r=t.renderable.getTextValues().length,o=4*r,a=2*r,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o);h(Bu,n);const c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l};let u=0,d=0,p=0;const f=t.renderable.getTextPolyData().getPoints().getData(),g=t.renderable.getTextValues();for(;u<f.length/3;){Ru[0]=f[3*u],Ru[1]=f[3*u+1],Ru[2]=f[3*u+2],Vn(Eu,Ru,n),Ru[0]=f[3*u+3],Ru[1]=f[3*u+4],Ru[2]=f[3*u+5],Vn(Du,Ru,n),Pn(Eu,Eu,Du);const r=[Eu[0],Eu[1]];Qo(r),e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getAxisTitlePixelOffset(),c),u+=2,d++;for(let o=0;o<t.renderable.getTickCounts()[p];o++)e.createPolyDataForOneLabel(g[d],u,n,Bu,r,t.renderable.getTickLabelPixelOffset(),c),u++,d++;p++}const v=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(v),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0),t.camera=r,e.updateTexturePolyData()}}const ku=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),jt.setGet(e,t,["renderable"]),jt.get(e,t,["lastSize","lastAspectRatio","axisTextStyle","tickTextStyle","tmActor","ticks"]),t.forceUpdate=!1,t.lastRedrawTime={},jt.obj(t.lastRedrawTime,{mtime:0}),t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.lastTickBounds=[],Fu(e,t)}),"vtkCubeAxesActorHelper");function Gu(e,t){t.classHierarchy.push("vtkCubeAxesActor"),e.setCamera=n=>{t.camera!==n&&(t.cameraModifiedSub&&(t.cameraModifiedSub.unsubscribe(),t.cameraModifiedSub=null),t.camera=n,n&&(t.cameraModifiedSub=n.onModified(e.update)),e.update(),e.modified())},e.computeFacesToDraw=()=>{const e=t.camera.getViewMatrix();m(e,e);let n=!1;const r=ki.getDiagonalLength(t.dataBounds),o=Math.sin(t.faceVisibilityAngle*Math.PI/180);for(let a=0;a<6;a++){let i=!1;const s=Math.floor(a/2),l=(s+1)%3,c=(s+2)%3;t.dataBounds[2*l]!==t.dataBounds[2*l+1]&&t.dataBounds[2*c]!==t.dataBounds[2*c+1]&&(Ru[s]=t.dataBounds[a]-.1*r*Au[a][s],Ru[l]=.5*(t.dataBounds[2*l]+t.dataBounds[2*l+1]),Ru[c]=.5*(t.dataBounds[2*c]+t.dataBounds[2*c+1]),Vn(Eu,Ru,e),Ru[s]=t.dataBounds[a],Vn(Du,Ru,e),Pn(Eu,Du,Eu),Mn(Eu,Eu),i=Eu[2]>o,t.camera.getParallelProjection()||(Mn(Du,Du),i=En(Du,Eu)>o)),i!==t.lastFacesToDraw[a]&&(t.lastFacesToDraw[a]=i,n=!0)}return n},e.updatePolyData=(e,n,r)=>{let o=0,a=0;o+=8;let i=0;for(let e=0;e<12;e++)n[e]>0&&i++;if(a+=i,t.gridLines)for(let t=0;t<6;t++)e[t]&&(o+=2*r[Ou[t][0]].length+2*r[Ou[t][1]].length,a+=r[Ou[t][0]].length+r[Ou[t][1]].length);const s=new Float64Array(3*o),l=new Uint32Array(3*a);let c=0,u=0;for(let e=0;e<2;e++)for(let n=0;n<2;n++)for(let r=0;r<2;r++)s[3*c]=t.dataBounds[r],s[3*c+1]=t.dataBounds[2+n],s[3*c+2]=t.dataBounds[4+e],c++;for(let e=0;e<12;e++)n[e]>0&&(l[3*u]=2,l[3*u+1]=wu[e][0],l[3*u+2]=wu[e][1],u++);if(t.gridLines)for(let n=0;n<6;n++)if(e[n]){const e=Math.floor(n/2);let o=r[Ou[n][0]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][0]]=o[r],s[3*c+Ou[n][1]]=t.dataBounds[2*Ou[n][1]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++;o=r[Ou[n][1]];for(let r=0;r<o.length;r++)s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]],c++,s[3*c+e]=t.dataBounds[n],s[3*c+Ou[n][1]]=o[r],s[3*c+Ou[n][0]]=t.dataBounds[2*Ou[n][0]+1],c++,l[3*u]=2,l[3*u+1]=c-2,l[3*u+2]=c-1,u++}t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getLines().setData(l,1),t.polyData.getLines().modified(),t.polyData.modified()},e.updateTextData=(e,n,r,o)=>{let a=0;for(let e=0;e<12;e++)1===n[e]&&(a+=2,a+=r[Pu[e]].length);const i=t.polyData.getPoints().getData(),s=new Float64Array(3*a);let l=0,c=0,u=0;for(let a=0;a<6;a++)if(e[a])for(let e=0;e<4;e++){const d=Iu[a][e];if(1===n[d]){const e=Pu[d],n=3*wu[d][0],p=3*wu[d][1];s[3*l]=.5*(i[n]+i[p]),s[3*l+1]=.5*(i[n+1]+i[p+1]),s[3*l+2]=.5*(i[n+2]+i[p+2]),l++,s[3*l+Math.floor(a/2)]=t.dataBounds[a],s[3*l+Ou[a][0]]=.5*(t.dataBounds[2*Ou[a][0]]+t.dataBounds[2*Ou[a][0]+1]),s[3*l+Ou[a][1]]=.5*(t.dataBounds[2*Ou[a][1]]+t.dataBounds[2*Ou[a][1]+1]),l++,t.textValues[c]=t.axisLabels[e],c++;const f=(e+1)%3,g=(e+2)%3,m=r[e],h=o[e];t.tickCounts[u]=m.length;for(let r=0;r<m.length;r++)s[3*l+e]=m[r],s[3*l+f]=i[n+f],s[3*l+g]=i[n+g],l++,t.textValues[c]=h[r],c++;u++}}t.textPolyData.getPoints().setData(s,3),t.textPolyData.modified()},e.update=()=>{if(!t.camera)return;const n=e.computeFacesToDraw(),r=t.lastFacesToDraw;let o=!1;for(let e=0;e<6;e++)t.dataBounds[e]!==t.lastTickBounds[e]&&(o=!0,t.lastTickBounds[e]=t.dataBounds[e]);if(n||o||t.forceUpdate){const n=new Array(12).fill(0);for(let e=0;e<6;e++)if(r[e])for(let t=0;t<4;t++)n[Iu[e][t]]++;const a=t.generateTicks(t.dataBounds);e.updatePolyData(r,n,a.ticks),e.updateTextData(r,n,a.ticks,a.tickStrings),(o||t.forceUpdate)&&e.updateTextureAtlas(a.tickStrings)}t.forceUpdate=!1},e.updateTextureAtlas=e=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t._tmAtlas.clear();let n=0,r=1;for(let o=0;o<3;o++){if(!t._tmAtlas.has(t.axisLabels[o])){Nu(t.tmContext,t.axisTextStyle);const e=t.tmContext.measureText(t.axisLabels[o]),a={height:e.actualBoundingBoxAscent+2,startingHeight:r,width:e.width+2,textStyle:t.axisTextStyle};t._tmAtlas.set(t.axisLabels[o],a),r+=a.height,n<a.width&&(n=a.width)}Nu(t.tmContext,t.tickTextStyle);for(let a=0;a<e[o].length;a++)if(!t._tmAtlas.has(e[o][a])){const i=t.tmContext.measureText(e[o][a]),s={height:i.actualBoundingBoxAscent+2,startingHeight:r,width:i.width+2,textStyle:t.tickTextStyle};t._tmAtlas.set(e[o][a],s),r+=s.height,n<s.width&&(n=s.width)}}n=_o(n),r=_o(r),t._tmAtlas.forEach((e=>{e.tcoords=[0,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight-e.height)/r,e.width/n,(r-e.startingHeight)/r,0,(r-e.startingHeight)/r]})),t.tmCanvas.width=n,t.tmCanvas.height=r,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,n,r),t._tmAtlas.forEach(((e,n)=>{Nu(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified()},e.onModified((()=>{t.forceUpdate=!0,e.update()})),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.get_tmAtlas=()=>t._tmAtlas,e.getBounds=()=>(e.update(),ki.setBounds(t.bounds,t.gridActor.getBounds()),ki.scaleAboutCenter(t.bounds,t.boundsScaleFactor,t.boundsScaleFactor,t.boundsScaleFactor),t.bounds);const n=jt.chain(e.setProperty,t.gridActor.setProperty);e.setProperty=e=>n(e)[0]}function Uu(e,t,n){return{boundsScaleFactor:1.3,camera:null,dataBounds:[...ki.INIT_BOUNDS],faceVisibilityAngle:8,gridLines:!0,axisLabels:null,axisTitlePixelOffset:35,tickLabelPixelOffset:12,generateTicks:_u,...n,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif",...n?.axisTextStyle},tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif",...n?.tickTextStyle}}}function zu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};hs.extend(e,t,Uu(0,0,n)),t.lastFacesToDraw=[!1,!1,!1,!1,!1,!1],t.axisLabels=["X-Axis","Y-Axis","Z-Axis"],t.tickCounts=[],t.textValues=[],t.lastTickBounds=[],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),t.gridMapper=ic.newInstance(),t.polyData=yu.newInstance(),t.gridMapper.setInputData(t.polyData),t.gridActor=hs.newInstance(),t.gridActor.setMapper(t.gridMapper),t.gridActor.setProperty(e.getProperty()),t.gridActor.setParentProp(e),t.textPolyData=yu.newInstance(),jt.setGet(e,t,["axisTitlePixelOffset","boundsScaleFactor","faceVisibilityAngle","gridLines","tickLabelPixelOffset","generateTicks"]),jt.setGetArray(e,t,["dataBounds"],6),jt.setGetArray(e,t,["axisLabels"],3),jt.get(e,t,["axisTextStyle","tickTextStyle","camera","tmTexture","textValues","textPolyData","tickCounts","gridActor"]),Gu(e,t)}var Wu={newInstance:jt.newInstance(zu,"vtkCubeAxesActor"),extend:zu,newCubeAxesActorHelper:ku,defaultGenerateTicks:_u};function Hu(e,t){t.classHierarchy.push("vtkOpenGLCubeAxesActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.CubeAxesActorHelper.getRenderable()||t.CubeAxesActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.CubeAxesActorHelper.getTmActor()),e.addMissingNode(t.renderable.getGridActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.CubeAxesActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const ju={};const Ku=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ju,n),Zt.extend(e,t,n),t.CubeAxesActorHelper=Wu.newCubeAxesActorHelper(),Hu(e,t)}),"vtkOpenGLCubeAxesActor");rn("vtkCubeAxesActor",Ku);const $u={ARRAY_BUFFER:0,ELEMENT_ARRAY_BUFFER:1,TEXTURE_BUFFER:2};var qu={ObjectType:$u};const{ObjectType:Xu}=qu;function Yu(e,t){function n(e){switch(e){case Xu.ELEMENT_ARRAY_BUFFER:return t.context.ELEMENT_ARRAY_BUFFER;case Xu.TEXTURE_BUFFER:if("TEXTURE_BUFFER"in t.context)return t.context.TEXTURE_BUFFER;case Xu.ARRAY_BUFFER:default:return t.context.ARRAY_BUFFER}}t.classHierarchy.push("vtkOpenGLBufferObject");let r=null,o=null,a=!0,i="";e.getType=()=>r,e.setType=e=>{r=e},e.getHandle=()=>o,e.isReady=()=>!1===a,e.generateBuffer=e=>{const a=n(e);return null===o&&(o=t.context.createBuffer(),r=e),n(r)===a},e.upload=(s,l)=>e.generateBuffer(l)?(t.context.bindBuffer(n(r),o),t.context.bufferData(n(r),s,t.context.STATIC_DRAW),t.allocatedGPUMemoryInBytes=s.length*s.BYTES_PER_ELEMENT,a=!1,!0):(i="Trying to upload array buffer to incompatible buffer.",!1),e.bind=()=>!!o&&(t.context.bindBuffer(n(r),o),!0),e.release=()=>!!o&&(t.context.bindBuffer(n(r),null),!0),e.releaseGraphicsResources=()=>{null!==o&&(t.context.bindBuffer(n(r),null),t.context.deleteBuffer(o),o=null,t.allocatedGPUMemoryInBytes=0)},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getError=()=>i}const Zu={objectType:Xu.ARRAY_BUFFER,context:null,allocatedGPUMemoryInBytes:0};function Qu(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zu,n),jt.obj(e,t),jt.get(e,t,["_openGLRenderWindow","allocatedGPUMemoryInBytes"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),Yu(e,t)}var Ju={newInstance:jt.newInstance(Qu),extend:Qu,...qu};const{vtkErrorMacro:ed}=jt;function td(e,t){t.classHierarchy.push("vtkOpenGLCellArrayBufferObject"),e.setType($u.ARRAY_BUFFER),e.createVBO=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:null;if(!n.getData()||!n.getData().length)return t.elementCount=0,0;t.blockSize=3,t.vertexOffset=0,t.normalOffset=0,t.tCoordOffset=0,t.tCoordComponents=0,t.colorComponents=0,t.colorOffset=0,t.customData=[];const s=a.points.getData();let l=null,c=null,u=null;const d=a.colors?a.colors.getNumberOfComponents():0,p=a.tcoords?a.tcoords.getNumberOfComponents():0;a.normals&&(t.normalOffset=4*t.blockSize,t.blockSize+=3,l=a.normals.getData()),a.customAttributes&&a.customAttributes.forEach((e=>{e&&(t.customData.push({data:e.getData(),offset:4*t.blockSize,components:e.getNumberOfComponents(),name:e.getName()}),t.blockSize+=e.getNumberOfComponents())})),a.tcoords&&(t.tCoordOffset=4*t.blockSize,t.tCoordComponents=p,t.blockSize+=p,c=a.tcoords.getData()),a.colors?(t.colorComponents=a.colors.getNumberOfComponents(),t.colorOffset=0,u=a.colors.getData(),t.colorBO||(t.colorBO=Ju.newInstance()),t.colorBO.setOpenGLRenderWindow(t._openGLRenderWindow)):t.colorBO=null,t.stride=4*t.blockSize;let f,g=0,m=0,h=0,v=0,y=0,T=0;const b={anythingToPoints(e,t,n,r){for(let o=0;o<e;++o)f(t[n+o],r)},linesToWireframe(e,t,n,r){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r)},polysToWireframe(e,t,n,r){if(e>2)for(let o=0;o<e;++o)f(t[n+o],r),f(t[n+(o+1)%e],r)},stripsToWireframe(e,t,n,r){if(e>2){for(let o=0;o<e-1;++o)f(t[n+o],r),f(t[n+o+1],r);for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+2],r)}},polysToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+0],r),f(t[n+o+1],r),f(t[n+o+2],r)},stripsToSurface(e,t,n,r){for(let o=0;o<e-2;o++)f(t[n+o],r),f(t[n+o+1+o%2],r),f(t[n+o+1+(o+1)%2],r)}},x={anythingToPoints:(e,t)=>e,linesToWireframe:(e,t)=>e>1?2*(e-1):0,polysToWireframe:(e,t)=>e>2?2*e:0,stripsToWireframe:(e,t)=>e>2?4*e-6:0,polysToSurface:(e,t)=>e>2?3*(e-2):0,stripsToSurface:(e,t,n)=>e>2?3*(e-2):0};let C=null,S=null;o===rs.POINTS||"verts"===r?(C=b.anythingToPoints,S=x.anythingToPoints):o===rs.WIREFRAME||"lines"===r?(C=b[`${r}ToWireframe`],S=x[`${r}ToWireframe`]):(C=b[`${r}ToSurface`],S=x[`${r}ToSurface`]);const A=n.getData(),I=A.length;let w=0;for(let e=0;e<I;)w+=S(A[e],A),e+=A[e]+1;let P=null;const O=new Float32Array(w*t.blockSize);u&&(P=new Uint8Array(4*w));let R=0,M=0,E=0,D=0;for(let e=0;e<3;++e){const t=a.points.getRange(e),n=t[1]-t[0];E+=n*n;const r=.5*(t[1]+t[0]);D+=r*r}const V=E>0&&(Math.abs(D)/E>1e6||Math.abs(Math.log10(E))>3||0===E&&D>1e6);if(V){const t=new Float64Array(3),n=new Float64Array(3);for(let e=0;e<3;++e){const r=a.points.getRange(e),o=r[1]-r[0];t[e]=.5*(r[1]+r[0]),n[e]=o>0?1/o:1}e.setCoordShiftAndScale(t,n)}else!0===t.coordShiftAndScaleEnabled&&e.setCoordShiftAndScale(null,null);if(i)if(i.points||i.cells){const e=new Int32Array(w+i.points.length);e.set(i.points),i.points=e;const t=new Int32Array(w+i.cells.length);t.set(i.cells),i.cells=t}else i.points=new Int32Array(w),i.cells=new Int32Array(w);let L=a.vertexOffset;f=function(e,n){if(i&&(i.points[L]=e,i.cells[L]=T+a.cellOffset),++L,g=3*e,t.coordShiftAndScaleEnabled?(O[R++]=(s[g++]-t.coordShift[0])*t.coordScale[0],O[R++]=(s[g++]-t.coordShift[1])*t.coordScale[1],O[R++]=(s[g++]-t.coordShift[2])*t.coordScale[2]):(O[R++]=s[g++],O[R++]=s[g++],O[R++]=s[g++]),null!==l&&(m=a.haveCellNormals?3*(T+a.cellOffset):3*e,O[R++]=l[m++],O[R++]=l[m++],O[R++]=l[m++]),t.customData.forEach((t=>{y=e*t.components;for(let e=0;e<t.components;++e)O[R++]=t.data[y++]})),null!==c){h=a.useTCoordsPerCell?n*p:e*p;for(let e=0;e<p;++e)O[R++]=c[h++]}null!==u&&(v=a.haveCellScalars?(T+a.cellOffset)*d:e*d,P[M++]=u[v++],P[M++]=u[v++],P[M++]=u[v++],P[M++]=4===d?u[v++]:255)};for(let e=0;e<I;e+=A[e]+1,T++)C(A[e],A,e+1,T+a.cellOffset);return t.elementCount=w,e.upload(O,$u.ARRAY_BUFFER),t.colorBO&&(t.colorBOStride=4,t.colorBO.upload(P,$u.ARRAY_BUFFER)),T},e.setCoordShiftAndScale=(e,n)=>{null===e||e.constructor===Float64Array&&3===e.length?null===n||n.constructor===Float64Array&&3===n.length?(null!==t.coordShift&&null!==e&&Nn(e,t.coordShift)||(t.coordShift=e),null!==t.coordScale&&null!==n&&Nn(n,t.coordScale)||(t.coordScale=n),t.coordShiftAndScaleEnabled=function(e,t){return null!==e&&null!==t&&!(Bn(e,[0,0,0])&&Bn(t,[1,1,1]))}(t.coordShift,t.coordScale),t.coordShiftAndScaleEnabled?t.inverseShiftAndScaleMatrix=function(e,t){const n=new Float64Array(3);Rn(n,t);const r=new Float64Array(16);return _(r,Ui(),e,n),r}(t.coordShift,t.coordScale):t.inverseShiftAndScaleMatrix=null):ed("Wrong type for coordScale, expected vec3 or null"):ed("Wrong type for coordShift, expected vec3 or null")}}const nd={elementCount:0,stride:0,colorBOStride:0,vertexOffset:0,normalOffset:0,tCoordOffset:0,tCoordComponents:0,colorOffset:0,colorComponents:0,tcoordBO:null,customData:[],coordShift:null,coordScale:null,coordShiftAndScaleEnabled:!1,inverseShiftAndScaleMatrix:null};function rd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,nd,n),Ju.extend(e,t,n),jt.setGet(e,t,["colorBO","elementCount","stride","colorBOStride","vertexOffset","normalOffset","tCoordOffset","tCoordComponents","colorOffset","colorComponents","customData"]),jt.get(e,t,["coordShift","coordScale","coordShiftAndScaleEnabled","inverseShiftAndScaleMatrix"]),td(e,t)}var od={newInstance:jt.newInstance(rd),extend:rd};const{vtkErrorMacro:ad}=jt;function id(e,t){t.classHierarchy.push("vtkShader"),e.compile=()=>{let e=t.context.VERTEX_SHADER;if(!t.source||!t.source.length||"Unknown"===t.shaderType)return!1;if(0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0),e="Fragment"===t.shaderType?t.context.FRAGMENT_SHADER:t.context.VERTEX_SHADER,t.handle=t.context.createShader(e),t.context.shaderSource(t.handle,t.source),t.context.compileShader(t.handle),!t.context.getShaderParameter(t.handle,t.context.COMPILE_STATUS)){const e=t.context.getShaderInfoLog(t.handle);return ad(`Error compiling shader '${t.source}': ${e}`),t.context.deleteShader(t.handle),t.handle=0,!1}return!0},e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(t.context.deleteShader(t.handle),t.handle=0,t.dirty=!0)}}const sd={shaderType:"Unknown",source:"",error:"",handle:0,dirty:!1,context:null};function ld(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sd,n),jt.obj(e,t),jt.setGet(e,t,["shaderType","source","error","handle","context"]),id(e,t)}var cd={newInstance:jt.newInstance(ld,"vtkShader"),extend:ld};const{vtkErrorMacro:ud}=jt;function dd(e,t){t.classHierarchy.push("vtkShaderProgram"),e.compileShader=()=>t.vertexShader.compile()?t.fragmentShader.compile()?e.attachShader(t.vertexShader)&&e.attachShader(t.fragmentShader)?e.link()?(e.setCompiled(!0),1):(ud(`Links failed: ${t.error}`),0):(ud(t.error),0):(ud(t.fragmentShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.fragmentShader.getError()),0):(ud(t.vertexShader.getSource().split("\n").map(((e,t)=>`${t}: ${e}`)).join("\n")),ud(t.vertexShader.getError()),0),e.cleanup=()=>{"Unknown"!==t.shaderType&&0!==t.handle&&(e.release(),0!==t.vertexShaderHandle&&(t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=0),0!==t.fragmentShaderHandle&&(t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=0),t.context.deleteProgram(t.handle),t.handle=0,e.setCompiled(!1))},e.bind=()=>!(!t.linked&&!e.link()||(t.context.useProgram(t.handle),e.setBound(!0),0)),e.isBound=()=>!!t.bound,e.release=()=>{t.context.useProgram(null),e.setBound(!1)},e.setContext=e=>{t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.link=()=>{if(t.linked)return!0;if(0===t.handle)return t.error="Program has not been initialized, and/or does not have shaders.",!1;if(t.uniformLocs={},t.context.linkProgram(t.handle),!t.context.getProgramParameter(t.handle,t.context.LINK_STATUS)){const e=t.context.getProgramInfoLog(t.handle);return ud(`Error linking shader ${e}`),t.handle=0,!1}return e.setLinked(!0),t.attributeLocs={},!0},e.setUniformMatrix=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix4fv(o,!1,a),!0},e.setUniformMatrix3x3=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;const a=new Float32Array(r);return t.context.uniformMatrix3fv(o,!1,a),!0},e.setUniformf=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1f(o,r),!0)},e.setUniformfv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1fv(o,r),!0)},e.setUniformi=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1i(o,r),!0)},e.setUniformiv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform1iv(o,r),!0)},e.setUniform2f=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2f(a,r,o),!0},e.setUniform2fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2fv(o,r),!0)},e.setUniform2i=(n,r,o)=>{const a=e.findUniform(n);if(-1===a)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===o)throw new RangeError("Invalid number of values for array");return t.context.uniform2i(a,r,o),!0},e.setUniform2iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform2iv(o,r),!0)},e.setUniform3f=(n,r,o,a)=>{const i=e.findUniform(n);if(-1===i)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(void 0===a)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(i,r,o,a),!0},e.setUniform3fArray=(n,r)=>{const o=e.findUniform(n);if(-1===o)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;if(!Array.isArray(r)||3!==r.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3f(o,r[0],r[1],r[2]),!0},e.setUniform3fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3fv(o,r),!0)},e.setUniform3i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),3!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform3i(r,s[0],s[1],s[2]),!0},e.setUniform3iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform3iv(o,r),!0)},e.setUniform4f=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4f(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4fv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4fv(o,r),!0)},e.setUniform4i=function(n){const r=e.findUniform(n);if(-1===r)return t.error=`Could not set uniform ${n} . No such uniform.`,!1;for(var o=arguments.length,a=new Array(o>1?o-1:0),i=1;i<o;i++)a[i-1]=arguments[i];let s=a;if(1===s.length&&Array.isArray(s[0])&&(s=s[0]),4!==s.length)throw new RangeError("Invalid number of values for array");return t.context.uniform4i(r,s[0],s[1],s[2],s[3]),!0},e.setUniform4iv=(n,r)=>{const o=e.findUniform(n);return-1===o?(t.error=`Could not set uniform ${n} . No such uniform.`,!1):(t.context.uniform4iv(o,r),!0)},e.findUniform=e=>{if(!e||!t.linked)return-1;let n=t.uniformLocs[e];return void 0!==n?n:(n=t.context.getUniformLocation(t.handle,e),null===n?(t.error=`Uniform ${e} not found in current shader program.`,t.uniformLocs[e]=-1,-1):(t.uniformLocs[e]=n,n))},e.isUniformUsed=e=>{if(!e)return!1;let n=t.uniformLocs[e];return void 0!==n?null!==n:t.linked?(n=t.context.getUniformLocation(t.handle,e),t.uniformLocs[e]=n,null!==n):(ud("attempt to find uniform when the shader program is not linked"),!1)},e.isAttributeUsed=e=>{if(!e)return!1;if(e in t.attributeLocs)return!0;if(!t.linked)return ud("attempt to find uniform when the shader program is not linked"),!1;const n=t.context.getAttribLocation(t.handle,e);return-1!==n&&(t.attributeLocs[e]=n,!0)},e.attachShader=n=>{if(0===n.getHandle())return t.error="Shader object was not initialized, cannot attach it.",!1;if("Unknown"===n.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;if(0===t.handle){const e=t.context.createProgram();if(0===e)return t.error="Could not create shader program.",!1;t.handle=e,t.linked=!1}return"Vertex"===n.getShaderType()&&(0!==t.vertexShaderHandle&&t.context.detachShader(t.handle,t.vertexShaderHandle),t.vertexShaderHandle=n.getHandle()),"Fragment"===n.getShaderType()&&(0!==t.fragmentShaderHandle&&t.context.detachShader(t.handle,t.fragmentShaderHandle),t.fragmentShaderHandle=n.getHandle()),t.context.attachShader(t.handle,n.getHandle()),e.setLinked(!1),!0},e.detachShader=e=>{if(0===e.getHandle())return t.error="shader object was not initialized, cannot attach it.",!1;if("Unknown"===e.getShaderType())return t.error="Shader object is of type Unknown and cannot be used.",!1;switch(0===t.handle&&(t.error="This shader program has not been initialized yet."),e.getShaderType()){case"Vertex":return t.vertexShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.vertexShaderHandle=0,t.linked=!1,!0);case"Fragment":return t.fragmentShaderHandle!==e.getHandle()?(t.error="The supplied shader was not attached to this program.",!1):(t.context.detachShader(t.handle,e.getHandle()),t.fragmentShaderHandle=0,t.linked=!1,!0);default:return!1}},e.setContext=e=>{t.context=e,t.vertexShader.setContext(e),t.fragmentShader.setContext(e),t.geometryShader.setContext(e)},e.setLastCameraMTime=e=>{t.lastCameraMTime=e}}const pd={vertexShaderHandle:0,fragmentShaderHandle:0,geometryShaderHandle:0,vertexShader:null,fragmentShader:null,geometryShader:null,linked:!1,bound:!1,compiled:!1,error:"",handle:0,numberOfOutputs:0,attributesLocs:null,uniformLocs:null,md5Hash:0,context:null,lastCameraMTime:null};function fd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pd,n),t.attributesLocs={},t.uniformLocs={},t.vertexShader=cd.newInstance(),t.vertexShader.setShaderType("Vertex"),t.fragmentShader=cd.newInstance(),t.fragmentShader.setShaderType("Fragment"),t.geometryShader=cd.newInstance(),t.geometryShader.setShaderType("Geometry"),jt.obj(e,t),jt.get(e,t,["lastCameraMTime"]),jt.setGet(e,t,["error","handle","compiled","bound","md5Hash","vertexShader","fragmentShader","geometryShader","linked"]),dd(e,t)}var gd={newInstance:jt.newInstance(fd,"vtkShaderProgram"),extend:fd,substitute:function(e,t,n,r){const o="string"==typeof n?n:n.join("\n"),a=!1===r?t:new RegExp(t,"g"),i=e.replace(a,o);return{replace:i!==o,result:i}}};function md(e,t){t.classHierarchy.push("vtkOpenGLVertexArrayObject"),e.exposedMethod=()=>{},e.initialize=()=>{t.instancingExtension=null,t._openGLRenderWindow.getWebgl2()||(t.instancingExtension=t.context.getExtension("ANGLE_instanced_arrays")),!t.forceEmulation&&t._openGLRenderWindow&&t._openGLRenderWindow.getWebgl2()?(t.extension=null,t.supported=!0,t.handleVAO=t.context.createVertexArray()):(t.extension=t.context.getExtension("OES_vertex_array_object"),!t.forceEmulation&&t.extension?(t.supported=!0,t.handleVAO=t.extension.createVertexArrayOES()):t.supported=!1)},e.isReady=()=>0!==t.handleVAO||!1===t.supported,e.bind=()=>{if(e.isReady()||e.initialize(),e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(t.handleVAO):t.context.bindVertexArray(t.handleVAO);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,1):e.vertexAttribDivisor(o.index+n,1))}}}},e.release=()=>{if(e.isReady()&&t.supported)t.extension?t.extension.bindVertexArrayOES(null):t.context.bindVertexArray(null);else if(e.isReady()){const e=t.context;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];t.context.bindBuffer(e.ARRAY_BUFFER,r.buffer);for(let n=0;n<r.attributes.length;++n){const o=r.attributes[n],a=o.isMatrix?o.size:1;for(let n=0;n<a;++n)e.enableVertexAttribArray(o.index+n),e.vertexAttribPointer(o.index+n,o.size,o.type,o.normalize,o.stride,o.offset+o.stride*n/o.size),o.divisor>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(o.index+n,0):e.vertexAttribDivisor(o.index+n,0)),e.disableVertexAttribArray(o.index+n)}}}},e.shaderProgramChanged=()=>{e.release(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.handleProgram=0},e.releaseGraphicsResources=()=>{e.shaderProgramChanged(),t.handleVAO&&(t.extension?t.extension.deleteVertexArrayOES(t.handleVAO):t.context.deleteVertexArray(t.handleVAO)),t.handleVAO=0,t.supported=!0,t.handleProgram=0},e.addAttributeArray=(t,n,r,o,a,i,s,l)=>e.addAttributeArrayWithDivisor(t,n,r,o,a,i,s,l,0,!1),e.addAttributeArrayWithDivisor=(n,r,o,a,i,s,l,c,u,d)=>{if(!n)return!1;if(!n.isBound()||0===r.getHandle()||r.getType()!==$u.ARRAY_BUFFER)return!1;if(0===t.handleProgram&&(t.handleProgram=n.getHandle()),e.isReady()||e.initialize(),!e.isReady()||t.handleProgram!==n.getHandle())return!1;const p=t.context,f={};if(f.name=o,f.index=p.getAttribLocation(t.handleProgram,o),f.offset=a,f.stride=i,f.type=s,f.size=l,f.normalize=c,f.isMatrix=d,f.divisor=u,-1===f.Index)return!1;if(r.bind(),p.enableVertexAttribArray(f.index),p.vertexAttribPointer(f.index,f.size,f.type,f.normalize,f.stride,f.offset),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f.index,1):p.vertexAttribDivisor(f.index,1)),f.buffer=r.getHandle(),!t.supported){let e=!1;for(let n=0;n<t.buffers.length;++n){const r=t.buffers[n];if(r.buffer===f.buffer){e=!0;let t=!1;for(let e=0;e<r.attributes.length;++e)r.attributes[e].name===o&&(t=!0,r.attributes[e]=f);t||r.attributes.push(f)}}e||t.buffers.push({buffer:f.buffer,attributes:[f]})}return!0},e.addAttributeMatrixWithDivisor=(n,r,o,a,i,s,l,c,u)=>{const d=e.addAttributeArrayWithDivisor(n,r,o,a,i,s,l,c,u,!0);if(!d)return d;const p=t.context,f=p.getAttribLocation(t.handleProgram,o);for(let e=1;e<l;e++)p.enableVertexAttribArray(f+e),p.vertexAttribPointer(f+e,l,s,c,i,a+i*e/l),u>0&&(t.instancingExtension?t.instancingExtension.vertexAttribDivisorANGLE(f+e,1):p.vertexAttribDivisor(f+e,1));return!0},e.removeAttributeArray=n=>{if(!e.isReady()||0===t.handleProgram)return!1;if(!t.supported)for(let e=0;e<t.buffers.length;++e){const r=t.buffers[e];for(let o=0;o<r.attributes.length;++o)if(r.attributes[o].name===n)return r.attributes.splice(o,1),r.attributes.length||t.buffers.splice(e,1),!0}return!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))}}const hd={forceEmulation:!1,handleVAO:0,handleProgram:0,supported:!0,buffers:null,context:null};function vd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,hd,n),t.buffers=[],jt.obj(e,t),jt.get(e,t,["supported"]),jt.setGet(e,t,["forceEmulation"]),md(e,t)}var yd={newInstance:jt.newInstance(vd,"vtkOpenGLVertexArrayObject"),extend:vd};const Td={Start:0,Points:0,Lines:1,Tris:2,TriStrips:3,TrisEdges:4,TriStripsEdges:5,End:6};function bd(e,t){t.classHierarchy.push("vtkOpenGLHelper"),e.setOpenGLRenderWindow=e=>{t.context=e.getContext(),t.program.setContext(t.context),t.VAO.setOpenGLRenderWindow(e),t.CABO.setOpenGLRenderWindow(e)},e.releaseGraphicsResources=e=>{t.VAO.releaseGraphicsResources(),t.CABO.releaseGraphicsResources(),t.CABO.setElementCount(0)},e.drawArrays=(n,r,o,a)=>{if(t.CABO.getElementCount()){const i=e.getOpenGLMode(o),s=e.haveWideLines(n,r),l=t.context,c=l.getParameter(l.DEPTH_WRITEMASK);t.pointPicking&&l.depthMask(!1),i===l.LINES&&s?(e.updateShaders(n,r,a),l.drawArraysInstanced(i,0,t.CABO.getElementCount(),2*Math.ceil(r.getProperty().getLineWidth()))):(l.lineWidth(r.getProperty().getLineWidth()),e.updateShaders(n,r,a),l.drawArrays(i,0,t.CABO.getElementCount()),l.lineWidth(1));const u=(i===l.POINTS?1:0)||(i===l.LINES?2:3);return t.pointPicking&&l.depthMask(c),t.CABO.getElementCount()/u}return 0},e.getOpenGLMode=e=>{if(t.pointPicking)return t.context.POINTS;const n=t.primitiveType;return e===rs.POINTS||n===Td.Points?t.context.POINTS:e===rs.WIREFRAME||n===Td.Lines||n===Td.TrisEdges||n===Td.TriStripsEdges?t.context.LINES:t.context.TRIANGLES},e.haveWideLines=(e,n)=>n.getProperty().getLineWidth()>1&&!(t.CABO.getOpenGLRenderWindow()&&t.CABO.getOpenGLRenderWindow().getHardwareMaximumLineWidth()>=n.getProperty().getLineWidth()),e.getNeedToRebuildShaders=(t,n,r)=>!!(r.getNeedToRebuildShaders(e,t,n)||0===e.getProgram()||e.getShaderSourceTime().getMTime()<r.getMTime()||e.getShaderSourceTime().getMTime()<n.getMTime()),e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};o.buildShaders(a,n,r);const i=t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==e.getProgram()&&(e.setProgram(i),e.getVAO().releaseGraphicsResources()),e.getShaderSourceTime().modified()}else t.CABO.getOpenGLRenderWindow().getShaderCache().readyShaderProgram(e.getProgram());e.getVAO().bind(),o.setMapperShaderParameters(e,n,r),o.setPropertyShaderParameters(e,n,r),o.setCameraShaderParameters(e,n,r),o.setLightingShaderParameters(e,n,r),o.invokeShaderCallbacks(e,n,r)},e.setMapperShaderParameters=(n,r,o)=>{if(e.haveWideLines(n,r)){e.getProgram().setUniform2f("viewportSize",o.usize,o.vsize);const t=parseFloat(r.getProperty().getLineWidth()),n=t/2;e.getProgram().setUniformf("lineWidthStepSize",t/Math.ceil(t)),e.getProgram().setUniformf("halfLineWidth",n)}t.primitiveType===Td.Points||r.getProperty().getRepresentation()===rs.POINTS?e.getProgram().setUniformf("pointSize",r.getProperty().getPointSize()):t.pointPicking&&e.getProgram().setUniformf("pointSize",e.getPointPickingPrimitiveSize())},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform float pointSize;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl","  gl_PointSize = pointSize;"],!1).result,e.getOpenGLMode(o.getProperty().getRepresentation())===t.context.LINES&&e.haveWideLines(r,o)&&(a=gd.substitute(a,"//VTK::PositionVC::Dec",["//VTK::PositionVC::Dec","uniform vec2 viewportSize;","uniform float lineWidthStepSize;","uniform float halfLineWidth;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["//VTK::PositionVC::Impl"," if (halfLineWidth > 0.0)","   {","   float offset = float(gl_InstanceID / 2) * lineWidthStepSize - halfLineWidth;","   vec4 tmpPos = gl_Position;","   vec3 tmpPos2 = tmpPos.xyz / tmpPos.w;","   tmpPos2.x = tmpPos2.x + 2.0 * mod(float(gl_InstanceID), 2.0) * offset / viewportSize[0];","   tmpPos2.y = tmpPos2.y + 2.0 * mod(float(gl_InstanceID + 1), 2.0) * offset / viewportSize[1];","   gl_Position = vec4(tmpPos2.xyz * tmpPos.w, tmpPos.w);","   }"]).result),n.Vertex=a},e.getPointPickingPrimitiveSize=()=>t.primitiveType===Td.Points?2:t.primitiveType===Td.Lines?4:6,e.getAllocatedGPUMemoryInBytes=()=>e.getCABO().getAllocatedGPUMemoryInBytes()}const xd={context:null,program:null,shaderSourceTime:null,VAO:null,attributeUpdateTime:null,CABO:null,primitiveType:0,pointPicking:!1};function Cd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xd,n),jt.obj(e,t),t.shaderSourceTime={},jt.obj(t.shaderSourceTime),t.attributeUpdateTime={},jt.obj(t.attributeUpdateTime),jt.setGet(e,t,["program","shaderSourceTime","VAO","attributeUpdateTime","CABO","primitiveType","pointPicking"]),t.program=gd.newInstance(),t.VAO=yd.newInstance(),t.CABO=od.newInstance(),bd(e,t)}var Sd={newInstance:jt.newInstance(Cd),extend:Cd,primTypes:Td};const Ad={CLAMP_TO_EDGE:0,REPEAT:1,MIRRORED_REPEAT:2},Id={NEAREST:0,LINEAR:1,NEAREST_MIPMAP_NEAREST:2,NEAREST_MIPMAP_LINEAR:3,LINEAR_MIPMAP_NEAREST:4,LINEAR_MIPMAP_LINEAR:5};var wd={Wrap:Ad,Filter:Id};const Pd=new Float32Array(1),Od=new Int32Array(Pd.buffer);var Rd={fromHalf:function(e){const t=(32768&e)>>15,n=(31744&e)>>10,r=1023&e;return 0===n?(t?-1:1)*2**-14*(r/1024):31===n?r?NaN:1/0*(t?-1:1):(t?-1:1)*2**(n-15)*(1+r/1024)},toHalf:function(e){Pd[0]=e;const t=Od[0];let n=t>>16&32768,r=t>>12&2047;const o=t>>23&255;return o<103?n:o>142?(n|=31744,n|=(255===o?0:1)&&8388607&t,n):o<113?(r|=2048,n|=(r>>114-o)+(r>>113-o&1),n):(n|=o-112<<10|r>>1,n+=1&r,n)}};const{Wrap:Md,Filter:Ed}=wd,{VtkDataTypes:Dd}=Ds,{vtkDebugMacro:Vd,vtkErrorMacro:Ld,vtkWarningMacro:Bd}=Kt,{toHalf:Nd}=Rd;function _d(e,t){function n(e,n){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const o=[];let a=t.width*t.height*t.components;if(r&&(a*=t.depth),e!==Dd.FLOAT&&t.openGLDataType===t.context.FLOAT)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Float32Array(t))}else o.push(null);if(e!==Dd.UNSIGNED_CHAR&&t.openGLDataType===t.context.UNSIGNED_BYTE)for(let e=0;e<n.length;e++)if(n[e]){const t=n[e].length>a?n[e].subarray(0,a):n[e];o.push(new Uint8Array(t))}else o.push(null);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}if(i)for(let e=0;e<n.length;e++)if(n[e]){const t=new Uint16Array(a),r=n[e];for(let e=0;e<a;e++)t[e]=Nd(r[e]);o.push(t)}else o.push(null);if(0===o.length)for(let e=0;e<n.length;e++)o.push(n[e]);return o}function r(e){if(t._openGLRenderWindow.getWebgl2())return e;const n=[],r=t.width,o=t.height,a=t.components;if(e&&(!Fo(r)||!Fo(o))){const i=t.context.getExtension("OES_texture_half_float"),s=_o(r),l=_o(o),c=s*l*t.components;for(let u=0;u<e.length;u++)if(null!==e[u]){let d=null;const p=o/l,f=r/s;let g=!1;t.openGLDataType===t.context.FLOAT?d=new Float32Array(c):i&&t.openGLDataType===i.HALF_FLOAT_OES?(d=new Uint16Array(c),g=!0):d=new Uint8Array(c);for(let t=0;t<l;t++){const n=t*s*a,i=t*p;let l=Math.floor(i),c=Math.ceil(i);c>=o&&(c=o-1);const m=i-l,h=1-m;l=l*r*a,c=c*r*a;for(let t=0;t<s;t++){const o=t*a,i=t*f;let s=Math.floor(i),p=Math.ceil(i);p>=r&&(p=r-1);const v=i-s;s*=a,p*=a;for(let t=0;t<a;t++)d[n+o+t]=g?Rd.toHalf(Rd.fromHalf(e[u][l+s+t])*h*(1-v)+Rd.fromHalf(e[u][l+p+t])*h*v+Rd.fromHalf(e[u][c+s+t])*m*(1-v)+Rd.fromHalf(e[u][c+p+t])*m*v):e[u][l+s+t]*h*(1-v)+e[u][l+p+t]*h*v+e[u][c+s+t]*m*(1-v)+e[u][c+p+t]*m*v}}n.push(d),t.width=s,t.height=l}else n.push(null)}if(0===n.length)for(let t=0;t<e.length;t++)n.push(e[t]);return n}function o(e){return!!t._openGLRenderWindow&&(!t.resizable&&!t.renderable?.getResizable()&&(!!t._openGLRenderWindow.getWebgl2()&&(!(t._openGLRenderWindow.getGLInformations().RENDERER.value.match(/WebKit/gi)&&navigator.platform.match(/Mac/gi)&&t.oglNorm16Ext)||e!==Dd.UNSIGNED_SHORT&&e!==Dd.SHORT)))}function a(n,r){const o=n.getNumberOfComponents(),a=n.getDataType(),i=n.getData(),s=new Array(o),l=new Array(o);for(let e=0;e<o;++e){const[t,r]=n.getRange(e);s[e]=t,l[e]=r}const c=function(e,t,n){const r=new Array(n),o=new Array(n);for(let a=0;a<n;++a)r[a]=e[a],o[a]=t[a]-e[a]||1;return{scale:o,offset:r}}(s,l,o);return function(n,r,o,a){e.getOpenGLDataType(n);let i=!1;if(t._openGLRenderWindow.getWebgl2())i=t.openGLDataType===t.context.HALF_FLOAT;else{const e=t.context.getExtension("OES_texture_half_float");i=e&&t.openGLDataType===e.HALF_FLOAT_OES}const s=i&&(function(e,t){for(let n=0;n<e.length;n++){const r=e[n],o=t[n]+r;if(r<-2048||r>2048||o<-2048||o>2048)return!1}return!0}(r,o)||a);t.useHalfFloat=s}(a,c.offset,c.scale,r),t.useHalfFloat||e.getOpenGLDataType(a,!0),{numComps:o,dataType:a,data:i,scaleOffsets:c}}t.classHierarchy.push("vtkOpenGLTexture"),e.render=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if(n?t._openGLRenderWindow=n:(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow")),t.context=t._openGLRenderWindow.getContext(),t.renderable.getInterpolate()?(t.generateMipmap?e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR):e.setMinificationFilter(Ed.LINEAR),e.setMagnificationFilter(Ed.LINEAR)):(e.setMinificationFilter(Ed.NEAREST),e.setMagnificationFilter(Ed.NEAREST)),t.renderable.getRepeat()&&(e.setWrapR(Md.REPEAT),e.setWrapS(Md.REPEAT),e.setWrapT(Md.REPEAT)),t.renderable.getInputData()&&t.renderable.setImage(null),!t.handle||t.renderable.getMTime()>t.textureBuildTime.getMTime()){if(null!==t.renderable.getImage()&&(t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),t.renderable.getImage()&&t.renderable.getImageLoaded()&&(e.create2DFromImage(t.renderable.getImage()),e.activate(),e.sendParameters(),t.textureBuildTime.modified())),null!==t.renderable.getCanvas()){t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR));const n=t.renderable.getCanvas();e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}if(null!==t.renderable.getJsImageData()){const n=t.renderable.getJsImageData();t.renderable.getInterpolate()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),e.create2DFromRaw(n.width,n.height,4,Dd.UNSIGNED_CHAR,n.data,!0),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}const n=t.renderable.getInputData(0);if(n&&n.getPointData().getScalars()){const r=n.getExtent(),o=n.getPointData().getScalars(),a=[];for(let e=0;e<t.renderable.getNumberOfInputPorts();++e){const n=t.renderable.getInputData(e),r=n?n.getPointData().getScalars().getData():null;r&&a.push(r)}t.renderable.getInterpolate()&&4===o.getNumberOfComponents()&&(t.generateMipmap=!0,e.setMinificationFilter(Ed.LINEAR_MIPMAP_LINEAR)),a.length%6==0?e.createCubeFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),a):e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),t.textureBuildTime.modified()}}t.handle&&e.activate()},e.destroyTexture=()=>{e.deactivate(),t.context&&t.handle&&t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.components=0,t.width=0,t.height=0,t.depth=0,e.resetFormatAndType()},e.createTexture=()=>{t.handle||(t.handle=t.context.createTexture(),t.target&&(t.context.bindTexture(t.target,t.handle),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.bindTexture(t.target,null)))},e.getTextureUnit=()=>t._openGLRenderWindow?t._openGLRenderWindow.getTextureUnitForTexture(e):-1,e.activate=()=>{t._openGLRenderWindow.activateTexture(e),e.bind()},e.deactivate=()=>{t._openGLRenderWindow&&t._openGLRenderWindow.deactivateTexture(e)},e.releaseGraphicsResources=n=>{n&&t.handle&&(n.activateTexture(e),n.deactivateTexture(e),t.context.deleteTexture(t.handle),t.handle=0,t.numberOfDimensions=0,t.target=0,t.internalFormat=0,t.format=0,t.openGLDataType=0,t.components=0,t.width=0,t.height=0,t.depth=0,t.allocatedGPUMemoryInBytes=0),t.shaderProgram&&(t.shaderProgram.releaseGraphicsResources(n),t.shaderProgram=null)},e.bind=()=>{t.context.bindTexture(t.target,t.handle),t.autoParameters&&e.getMTime()>t.sendParametersTime.getMTime()&&e.sendParameters()},e.isBound=()=>{let e=!1;if(t.context&&t.handle){let n=0;t.target===t.context.TEXTURE_2D?n=t.context.TEXTURE_BINDING_2D:Bd("impossible case"),e=t.context.getIntegerv(n)===t.handle}return e},e.sendParameters=()=>{t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_S,e.getOpenGLWrapMode(t.wrapS)),t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_T,e.getOpenGLWrapMode(t.wrapT)),t._openGLRenderWindow.getWebgl2()&&t.context.texParameteri(t.target,t.context.TEXTURE_WRAP_R,e.getOpenGLWrapMode(t.wrapR)),t.context.texParameteri(t.target,t.context.TEXTURE_MIN_FILTER,e.getOpenGLFilterMode(t.minificationFilter)),t.context.texParameteri(t.target,t.context.TEXTURE_MAG_FILTER,e.getOpenGLFilterMode(t.magnificationFilter)),t._openGLRenderWindow.getWebgl2()&&(t.context.texParameteri(t.target,t.context.TEXTURE_BASE_LEVEL,t.baseLevel),t.context.texParameteri(t.target,t.context.TEXTURE_MAX_LEVEL,t.maxLevel)),t.sendParametersTime.modified()},e.getInternalFormat=(n,r)=>(t._forceInternalFormat||(t.internalFormat=e.getDefaultInternalFormat(n,r)),t.internalFormat||Vd(`Unable to find suitable internal format for T=${n} NC= ${r}`),[t.context.R32F,t.context.RG32F,t.context.RGB32F,t.context.RGBA32F].includes(t.internalFormat)&&!t.context.getExtension("OES_texture_float_linear")&&Bd("Failed to load OES_texture_float_linear. Texture filtering is not available for *32F internal formats."),t.internalFormat),e.getDefaultInternalFormat=(e,n)=>{let r=0;return r=t._openGLRenderWindow.getDefaultTextureInternalFormat(e,n,t.oglNorm16Ext,t.useHalfFloat),r||(r||(Vd("Unsupported internal texture type!"),Vd(`Unable to find suitable internal format for T=${e} NC= ${n}`)),r)},e.setInternalFormat=n=>{t._forceInternalFormat=!0,n!==t.internalFormat&&(t.internalFormat=n,e.modified())},e.getFormat=(n,r)=>(t.format=e.getDefaultFormat(n,r),t.format),e.getDefaultFormat=(e,n)=>{if(t._openGLRenderWindow.getWebgl2())switch(n){case 1:return t.context.RED;case 2:return t.context.RG;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}else switch(n){case 1:return t.context.LUMINANCE;case 2:return t.context.LUMINANCE_ALPHA;case 3:default:return t.context.RGB;case 4:return t.context.RGBA}},e.resetFormatAndType=()=>{t.format=0,t.internalFormat=0,t._forceInternalFormat=!1,t.openGLDataType=0},e.getDefaultDataType=e=>{if(t._openGLRenderWindow.getWebgl2())switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.SHORT:return t.context.SHORT;case t.oglNorm16Ext&&!t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.UNSIGNED_SHORT;case t.useHalfFloat&&Dd.SHORT:case t.useHalfFloat&&Dd.UNSIGNED_SHORT:return t.context.HALF_FLOAT;case Dd.FLOAT:case Dd.VOID:default:return t.context.FLOAT}switch(e){case Dd.UNSIGNED_CHAR:return t.context.UNSIGNED_BYTE;case Dd.FLOAT:case Dd.VOID:default:if(t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))return t.context.FLOAT;{const e=t.context.getExtension("OES_texture_half_float");if(e&&t.context.getExtension("OES_texture_half_float_linear"))return e.HALF_FLOAT_OES}return t.context.UNSIGNED_BYTE}},e.getOpenGLDataType=function(n){let r=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return t.openGLDataType&&!r||(t.openGLDataType=e.getDefaultDataType(n)),t.openGLDataType},e.getShiftAndScale=()=>{let e=0,n=1;switch(t.openGLDataType){case t.context.BYTE:n=127.5,e=n-128;break;case t.context.UNSIGNED_BYTE:n=255,e=0;break;case t.context.SHORT:n=32767.5,e=n-32768;break;case t.context.UNSIGNED_SHORT:n=65536,e=0;break;case t.context.INT:n=2147483647.5,e=n-2147483648;break;case t.context.UNSIGNED_INT:n=4294967295,e=0;case t.context.FLOAT:}return{shift:e,scale:n}},e.getOpenGLFilterMode=e=>{switch(e){case Ed.NEAREST:return t.context.NEAREST;case Ed.LINEAR:return t.context.LINEAR;case Ed.NEAREST_MIPMAP_NEAREST:return t.context.NEAREST_MIPMAP_NEAREST;case Ed.NEAREST_MIPMAP_LINEAR:return t.context.NEAREST_MIPMAP_LINEAR;case Ed.LINEAR_MIPMAP_NEAREST:return t.context.LINEAR_MIPMAP_NEAREST;case Ed.LINEAR_MIPMAP_LINEAR:return t.context.LINEAR_MIPMAP_LINEAR;default:return t.context.NEAREST}},e.getOpenGLWrapMode=e=>{switch(e){case Md.CLAMP_TO_EDGE:return t.context.CLAMP_TO_EDGE;case Md.REPEAT:return t.context.REPEAT;case Md.MIRRORED_REPEAT:return t.context.MIRRORED_REPEAT;default:return t.context.CLAMP_TO_EDGE}},e.create2DFromRaw=function(a,i,s,l,c){let u=arguments.length>5&&void 0!==arguments[5]&&arguments[5];if(e.getOpenGLDataType(l,!0),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=[c],p=n(l,d),f=r(p);return t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,u),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=f[0]&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,f[0])):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,f[0]),t.generateMipmap&&t.context.generateMipmap(t.target),u&&t.context.pixelStorei(t.context.UNPACK_FLIP_Y_WEBGL,!1),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createCubeFromRaw=(a,i,s,l,c)=>{if(e.getOpenGLDataType(l),e.getInternalFormat(l,s),e.getFormat(l,s),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_CUBE_MAP,t.components=s,t.width=a,t.height=i,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),t.maxLevel=c.length/6-1,e.createTexture(),e.bind();const u=r(n(l,c)),d=[];let p=t.width,f=t.height;for(let e=0;e<u.length;e++){e%6==0&&0!==e&&(p/=2,f/=2),d[e]=at(l,f*p*t.components);for(let n=0;n<f;++n){const r=n*p*t.components,o=(f-n-1)*p*t.components;d[e].set(u[e].slice(o,o+p*t.components),r)}}t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(l)&&t.context.texStorage2D(t.target,6,t.internalFormat,t.width,t.height);for(let e=0;e<6;e++){let n=0,r=t.width,a=t.height;for(;r>=1&&a>=1;){let i=null;n<=t.maxLevel&&(i=d[6*n+e]),o(l)?null!=i&&t.context.texSubImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,0,0,r,a,t.format,t.openGLDataType,i):t.context.texImage2D(t.context.TEXTURE_CUBE_MAP_POSITIVE_X+e,n,t.internalFormat,r,a,0,t.format,t.openGLDataType,i),n++,r/=2,a/=2}}return t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*s*t._openGLRenderWindow.getDefaultTextureByteSize(l,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.createDepthFromRaw=(n,r,a,i)=>(e.getOpenGLDataType(a),t.format=t.context.DEPTH_COMPONENT,t._openGLRenderWindow.getWebgl2()?a===Dd.FLOAT?t.internalFormat=t.context.DEPTH_COMPONENT32F:t.internalFormat=t.context.DEPTH_COMPONENT16:t.internalFormat=t.context.DEPTH_COMPONENT,t.internalFormat&&t.format&&t.openGLDataType?(t.target=t.context.TEXTURE_2D,t.components=1,t.width=n,t.height=r,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(a)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=i&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,i)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,i),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(a,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0):(Ld("Failed to determine texture parameters."),!1)),e.create2DFromImage=n=>{if(e.getOpenGLDataType(Dd.UNSIGNED_CHAR),e.getInternalFormat(Dd.UNSIGNED_CHAR,4),e.getFormat(Dd.UNSIGNED_CHAR,4),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=4,t.depth=1,t.numberOfDimensions=2,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1);const r=!(t._openGLRenderWindow.getWebgl2()||Fo(n.width)&&Fo(n.height)),a=document.createElement("canvas");a.width=r?_o(n.width):n.width,a.height=r?_o(n.height):n.height,t.width=a.width,t.height=a.height;const i=a.getContext("2d");i.translate(0,a.height),i.scale(1,-1),i.drawImage(n,0,0,n.width,n.height,0,0,a.width,a.height);const s=a;return o(Dd.UNSIGNED_CHAR)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=s&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,s)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,s),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(Dd.UNSIGNED_CHAR,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create2DFilterableFromRaw=function(t,n,r,o,a){let i=arguments.length>5&&void 0!==arguments[5]&&arguments[5];return e.create2DFilterableFromDataArray(t,n,Ds.newInstance({numberOfComponents:r,dataType:o,values:a}),i)},e.create2DFilterableFromDataArray=function(t,n,r){let o=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const{numComps:i,dataType:s,data:l}=a(r,o);e.create2DFromRaw(t,n,i,s,l)},e.create3DFromRaw=(a,i,s,l,c,u)=>{if(e.getOpenGLDataType(c),e.getInternalFormat(c,l),e.getFormat(c,l),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_3D,t.components=l,t.width=a,t.height=i,t.depth=s,t.numberOfDimensions=3,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind();const d=r(n(c,[u],!0));return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(c)?(t.context.texStorage3D(t.target,1,t.internalFormat,t.width,t.height,t.depth),null!=d[0]&&t.context.texSubImage3D(t.target,0,0,0,0,t.width,t.height,t.depth,t.format,t.openGLDataType,d[0])):t.context.texImage3D(t.target,0,t.internalFormat,t.width,t.height,t.depth,0,t.format,t.openGLDataType,d[0]),t.generateMipmap&&t.context.generateMipmap(t.target),t.allocatedGPUMemoryInBytes=t.width*t.height*t.depth*t.components*t._openGLRenderWindow.getDefaultTextureByteSize(c,t.oglNorm16Ext,t.useHalfFloat),e.deactivate(),!0},e.create3DFilterableFromRaw=function(t,n,r,o,a,i){let s=arguments.length>6&&void 0!==arguments[6]&&arguments[6];return e.create3DFilterableFromDataArray(t,n,r,Ds.newInstance({numberOfComponents:o,dataType:a,values:i}),s)},e.create3DFilterableFromDataArray=function(n,r,i,s){let l=arguments.length>4&&void 0!==arguments[4]&&arguments[4];const{numComps:c,dataType:u,data:d,scaleOffsets:p}=a(s,l),f=n*r*i,g=[],m=[];for(let e=0;e<c;++e)g[e]=0,m[e]=1;t.volumeInfo={scale:m,offset:g,dataComputedScale:p.scale,dataComputedOffset:p.offset,width:n,height:r,depth:i};const h=structuredClone(p);if(t._openGLRenderWindow.getWebgl2()){if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=32767;return e.create3DFromRaw(n,r,i,c,u,d)}if(t.oglNorm16Ext&&!t.useHalfFloat&&u===Dd.UNSIGNED_SHORT){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=65535;return e.create3DFromRaw(n,r,i,c,u,d)}if(u===Dd.FLOAT||t.useHalfFloat&&(u===Dd.SHORT||u===Dd.UNSIGNED_SHORT))return e.create3DFromRaw(n,r,i,c,u,d);if(u===Dd.UNSIGNED_CHAR){for(let e=0;e<c;++e)t.volumeInfo.scale[e]=255;return e.create3DFromRaw(n,r,i,c,u,d)}const o=new Float32Array(f*c);t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;let a=0;const s=h.scale.map((e=>1/e));for(let e=0;e<f;e++)for(let e=0;e<c;e++)o[a]=(d[a]-h.offset[e])*s[e],a++;return e.create3DFromRaw(n,r,i,c,Dd.FLOAT,o)}let v=(e,t,n,r,o)=>{e[t]=n},y=Dd.UNSIGNED_CHAR;if(u===Dd.UNSIGNED_CHAR)for(let e=0;e<c;++e)h.offset[e]=0,h.scale[e]=255;else t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear")?(y=Dd.FLOAT,v=(e,t,n,r,o)=>{e[t]=(n-r)/o}):(y=Dd.UNSIGNED_CHAR,v=(e,t,n,r,o)=>{e[t]=255*(n-r)/o});if(e.getOpenGLDataType(y),e.getInternalFormat(y,c),e.getFormat(y,c),!t.internalFormat||!t.format||!t.openGLDataType)return Ld("Failed to determine texture parameters."),!1;t.target=t.context.TEXTURE_2D,t.components=c,t.depth=1,t.numberOfDimensions=2;let T=t.context.getParameter(t.context.MAX_TEXTURE_SIZE);T>4096&&(y===Dd.FLOAT||c>=3)&&(T=4096);let b=1,x=1;f>T*T&&(b=Math.ceil(Math.sqrt(f/(T*T))),x=b);let C=Math.sqrt(f)/b;C=_o(C);const S=Math.floor(C*b/n),A=Math.ceil(i/S),I=_o(r*A/x);let w;t.width=C,t.height=I,t._openGLRenderWindow.activateTexture(e),e.createTexture(),e.bind(),t.volumeInfo.xreps=S,t.volumeInfo.yreps=A,t.volumeInfo.xstride=b,t.volumeInfo.ystride=x,t.volumeInfo.offset=h.offset,t.volumeInfo.scale=h.scale;const P=C*I*c;w=y===Dd.FLOAT?new Float32Array(P):new Uint8Array(P);let O=0;const R=Math.floor(n/b),M=Math.floor(r/x);for(let e=0;e<A;e++){const o=Math.min(S,i-e*S),a=c*(t.width-o*Math.floor(n/b));for(let t=0;t<M;t++){for(let a=0;a<o;a++){const o=c*((e*S+a)*n*r+x*t*n);for(let e=0;e<R;e++)for(let t=0;t<c;t++)v(w,O,d[o+b*e*c+t],h.offset[t],h.scale[t]),O++}O+=a}}return t.context.pixelStorei(t.context.UNPACK_ALIGNMENT,1),o(y)?(t.context.texStorage2D(t.target,1,t.internalFormat,t.width,t.height),null!=w&&t.context.texSubImage2D(t.target,0,0,0,t.width,t.height,t.format,t.openGLDataType,w)):t.context.texImage2D(t.target,0,t.internalFormat,t.width,t.height,0,t.format,t.openGLDataType,w),e.deactivate(),!0},e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.getMaximumTextureSize=e=>e&&e.isCurrent()?e.getIntegerv(e.MAX_TEXTURE_SIZE):-1}const Fd={_openGLRenderWindow:null,_forceInternalFormat:!1,context:null,handle:0,sendParametersTime:null,textureBuildTime:null,numberOfDimensions:0,target:0,format:0,openGLDataType:0,components:0,width:0,height:0,depth:0,autoParameters:!0,wrapS:Md.CLAMP_TO_EDGE,wrapT:Md.CLAMP_TO_EDGE,wrapR:Md.CLAMP_TO_EDGE,minificationFilter:Ed.NEAREST,magnificationFilter:Ed.NEAREST,minLOD:-1e3,maxLOD:1e3,baseLevel:0,maxLevel:1e3,generateMipmap:!1,useHalfFloat:!0,oglNorm16Ext:null,allocatedGPUMemoryInBytes:0};function kd(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Fd,n),Zt.extend(e,t,n),t.sendParametersTime={},yt(t.sendParametersTime,{mtime:0}),t.textureBuildTime={},yt(t.textureBuildTime,{mtime:0}),St(e,t,["format","openGLDataType"]),At(e,t,["keyMatrixTime","minificationFilter","magnificationFilter","wrapS","wrapT","wrapR","generateMipmap","oglNorm16Ext"]),bt(e,t,["width","height","volumeInfo","components","handle","target","allocatedGPUMemoryInBytes"]),Ot(0,t,["openGLRenderWindow"]),_d(e,t)}const Gd=Dt(kd,"vtkOpenGLTexture");var Ud={newInstance:Gd,extend:kd,...wd};rn("vtkTexture",Gd);var zd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexMC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// picking support\n//VTK::Picking::Dec\n\nvoid main()\n{\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::PrimID::Impl\n\n  //VTK::PositionVC::Impl\n\n  //VTK::Light::Impl\n\n  //VTK::Picking::Impl\n}\n",Wd="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the polydata mappers fragment shader\n\nuniform int PrimitiveIDOffset;\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// optional color passed in from the vertex shader, vertexColor\n//VTK::Color::Dec\n\n// optional surface normal declaration\n//VTK::Normal::Dec\n\n// extra lighting parameters\n//VTK::Light::Dec\n\n// define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// Depth Peeling Support\n//VTK::DepthPeeling::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// label outline \n//VTK::LabelOutline::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n//VTK::LabelOutlineHelperFunction\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Clip::Impl\n\n  //VTK::Color::Impl\n\n  // Generate the normal if we are not passed in one\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Light::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",Hd=function(e,t){e.replaceShaderCoincidentOffset=(n,r,o)=>{const a=e.getCoincidentParameters(r,o);if(a&&(0!==a.factor||0!==a.offset)){let e=n.Fragment;e=gd.substitute(e,"//VTK::Coincident::Dec",["uniform float cfactor;","uniform float coffset;"]).result,t.context.getExtension("EXT_frag_depth")&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepthEXT = gl_FragCoord.z + 0.000016*coffset;").result),t._openGLRenderWindow.getWebgl2()&&(0!==a.factor?(e=gd.substitute(e,"//VTK::UniformFlow::Impl",["float cscale = length(vec2(dFdx(gl_FragCoord.z),dFdy(gl_FragCoord.z)));","//VTK::UniformFlow::Impl"],!1).result,e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + cfactor*cscale + 0.000016*coffset;").result):e=gd.substitute(e,"//VTK::Depth::Impl","gl_FragDepth = gl_FragCoord.z + 0.000016*coffset;").result),n.Fragment=e}}},jd=function(e,t){e.applyShaderReplacements=(e,t,n)=>{let r=null;if(t&&(r=t.ShaderReplacements),r)for(let t=0;t<r.length;t++){const o=r[t];if(n&&o.replaceFirst||!n&&!o.replaceFirst){const t=o.shaderType,n=e[t],r=gd.substitute(n,o.originalValue,o.replacementValue,o.replaceAll);e[t]=r.result}}},e.buildShaders=(n,r,o)=>{e.getReplacedShaderTemplate(n,r,o),t.lastRenderPassShaderReplacement=t.currentRenderPass?t.currentRenderPass.getShaderReplacement():null,t.lastRenderPassShaderReplacement&&t.lastRenderPassShaderReplacement(n);const a=t.renderable.getViewSpecificProperties().OpenGL;e.applyShaderReplacements(n,a,!0),e.replaceShaderValues(n,r,o),e.applyShaderReplacements(n,a)},e.getReplacedShaderTemplate=(n,r,o)=>{const a=t.renderable.getViewSpecificProperties().OpenGL;e.getShaderTemplate(n,r,o);let i=n.Vertex;if(a){const e=a.VertexShaderCode;void 0!==e&&""!==e&&(i=e)}n.Vertex=i;let s=n.Fragment;if(a){const e=a.FragmentShaderCode;void 0!==e&&""!==e&&(s=e)}n.Fragment=s;let l=n.Geometry;if(a){const e=a.GeometryShaderCode;void 0!==e&&(l=e)}n.Geometry=l}};const{FieldAssociations:Kd}=ol,{primTypes:$d}=Sd,{Representation:qd,Shading:Xd}=ds,{ScalarMode:Yd}=ic,{Filter:Zd,Wrap:Qd}=Ud,{vtkErrorMacro:Jd}=Kt,ep={type:"StartEvent"},tp={type:"EndEvent"},{CoordinateSystem:np}=Zi;function rp(e,t){function n(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}t.classHierarchy.push("vtkOpenGLPolyDataMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLActor=e.getFirstAncestorOfType("vtkOpenGLActor"),t._openGLRenderer=t.openGLActor.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=$d.Start;e<$d.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;const s=t.lastBoundBO.getReferenceByName("lastLightComplexity");let l=["uniform float ambient;","uniform float diffuse;","uniform float specular;","uniform float opacityUniform; // the fragment opacity","uniform vec3 ambientColorUniform;","uniform vec3 diffuseColorUniform;"];s&&(l=l.concat(["uniform vec3 specularColorUniform;","uniform float specularPowerUniform;"]));let c=["vec3 ambientColor;","  vec3 diffuseColor;","  float opacity;"];s&&(c=c.concat(["  vec3 specularColor;","  float specularPower;"])),c=c.concat(["  ambientColor = ambientColorUniform;","  diffuseColor = diffuseColorUniform;","  opacity = opacityUniform;"]),s&&(c=c.concat(["  specularColor = specularColorUniform;","  specularPower = specularPowerUniform;"])),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges||(l=l.concat(["varying vec4 vertexColorVSOutput;"]),o=gd.substitute(o,"//VTK::Color::Dec",["attribute vec4 scalarColor;","varying vec4 vertexColorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["vertexColorVSOutput =  scalarColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result),0===t.lastBoundBO.getCABO().getColorComponents()||t.drawingEdges?(t.renderable.getAreScalarsMappedFromCells()||t.renderable.getInterpolateScalarsBeforeMapping())&&t.renderable.getColorCoordinates()&&!t.drawingEdges?i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  vec4 texColor = texture2D(texture1, tcoordVCVSOutput.st);","  diffuseColor = texColor.rgb;","  ambientColor = texColor.rgb;","  opacity = opacity*texColor.a;"])).result:(r.getBackfaceProperty()&&!t.drawingEdges&&(l=l.concat(["uniform float opacityUniformBF; // the fragment opacity","uniform float ambientIntensityBF; // the material ambient","uniform float diffuseIntensityBF; // the material diffuse","uniform vec3 ambientColorUniformBF; // ambient material color","uniform vec3 diffuseColorUniformBF; // diffuse material color"]),s?(l=l.concat(["uniform float specularIntensityBF; // the material specular intensity","uniform vec3 specularColorUniformBF; // intensity weighted color","uniform float specularPowerUniformBF;"]),c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  specularColor = specularIntensityBF * specularColorUniformBF;","  specularPower = specularPowerUniformBF;","  opacity = opacityUniformBF; }"])):c=c.concat(["if (gl_FrontFacing == false) {","  ambientColor = ambientIntensityBF * ambientColorUniformBF;","  diffuseColor = diffuseIntensityBF * diffuseColorUniformBF;","  opacity = opacityUniformBF; }"])),t.haveCellScalars&&!t.drawingEdges&&(l=l.concat(["uniform samplerBuffer texture1;"])),i=gd.substitute(i,"//VTK::Color::Impl",c).result):i=gd.substitute(i,"//VTK::Color::Impl",c.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])).result,i=gd.substitute(i,"//VTK::Color::Dec",l).result,e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderLight=(e,n,r)=>{let o=e.Fragment;const a=t.lastBoundBO.getReferenceByName("lastLightComplexity"),i=t.lastBoundBO.getReferenceByName("lastLightCount");let s=[];switch(a){case 0:o=gd.substitute(o,"//VTK::Light::Impl",["  gl_FragData[0] = vec4(ambientColor * ambient + diffuseColor * diffuse, opacity);","  //VTK::Light::Impl"],!1).result;break;case 1:o=gd.substitute(o,"//VTK::Light::Impl",["  float df = max(0.0, normalVCVSOutput.z);","  float sf = pow(df, specularPower);","  vec3 diffuseL = df * diffuseColor;","  vec3 specularL = sf * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"],!1).result;break;case 2:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  float df;"];for(let e=0;e<i;++e)s=s.concat([`  df = max(0.0, dot(normalVCVSOutput, -lightDirectionVC${e}));`,`  diffuseL += ((df) * lightColor${e});`,`  if (dot(normalVCVSOutput, lightDirectionVC${e}) < 0.0)`,"    {",`    float sf = sign(df)*pow(max(1e-5,\n                                              dot(reflect(lightDirectionVC${e},normalVCVSOutput),\n                                                  normalize(-vertexVC.xyz))),\n                                         specularPower);`,`    specularL += (sf * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;case 3:for(let e=0;e<i;++e)s=s.concat([`uniform vec3 lightColor${e};`,`uniform vec3 lightDirectionVC${e}; // normalized`,`uniform vec3 lightHalfAngleVC${e}; // normalized`,`uniform vec3 lightPositionVC${e};`,`uniform vec3 lightAttenuation${e};`,`uniform float lightConeAngle${e};`,`uniform float lightExponent${e};`,`uniform int lightPositional${e};`]);o=gd.substitute(o,"//VTK::Light::Dec",s).result,s=["vec3 diffuseL = vec3(0,0,0);","  vec3 specularL = vec3(0,0,0);","  vec3 vertLightDirectionVC;","  float attenuation;","  float df;"];for(let e=0;e<i;++e)s=s.concat(["  attenuation = 1.0;",`  if (lightPositional${e} == 0)`,"    {",`      vertLightDirectionVC = lightDirectionVC${e};`,"    }","  else","    {",`    vertLightDirectionVC = vertexVC.xyz - lightPositionVC${e};`,"    float distanceVC = length(vertLightDirectionVC);","    vertLightDirectionVC = normalize(vertLightDirectionVC);","    attenuation = 1.0 /",`      (lightAttenuation${e}.x`,`       + lightAttenuation${e}.y * distanceVC`,`       + lightAttenuation${e}.z * distanceVC * distanceVC);`,"    // per OpenGL standard cone angle is 90 or less for a spot light",`    if (lightConeAngle${e} <= 90.0)`,"      {",`      float coneDot = dot(vertLightDirectionVC, lightDirectionVC${e});`,"      // if inside the cone",`      if (coneDot >= cos(radians(lightConeAngle${e})))`,"        {",`        attenuation = attenuation * pow(coneDot, lightExponent${e});`,"        }","      else","        {","        attenuation = 0.0;","        }","      }","    }","    df = max(0.0, attenuation*dot(normalVCVSOutput, -vertLightDirectionVC));",`    diffuseL += ((df) * lightColor${e});`,"    if (dot(normalVCVSOutput, vertLightDirectionVC) < 0.0)","      {",`      float sf = sign(df)*attenuation*pow(max(1e-5,\n                                                           dot(reflect(lightDirectionVC${e},\n                                                                       normalVCVSOutput),\n                                                               normalize(-vertexVC.xyz))),\n                                                       specularPower);`,`    specularL += ((sf) * lightColor${e});`,"    }"]);s=s.concat(["  diffuseL = diffuseL * diffuseColor;","  specularL = specularL * specularColor;","  gl_FragData[0] = vec4(ambientColor * ambient + diffuseL * diffuse + specularL * specular, opacity);","  //VTK::Light::Impl"]),o=gd.substitute(o,"//VTK::Light::Impl",s,!1).result;break;default:Jd("bad light complexity")}e.Fragment=o},e.replaceShaderNormal=(e,n,r)=>{if(t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex,o=e.Geometry,a=e.Fragment;t.lastBoundBO.getCABO().getNormalOffset()?(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * normalMC;"]).result,o=gd.substitute(o,"//VTK::Normal::Dec",["in vec3 normalVCVSOutput[];","out vec3 normalVCGSOutput;"]).result,o=gd.substitute(o,"//VTK::Normal::Impl",["normalVCGSOutput = normalVCVSOutput[i];"]).result,a=gd.substitute(a,"//VTK::Normal::Dec",["varying vec3 normalVCVSOutput;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalVCVSOutput);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.haveCellNormals?(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform mat3 normalMatrix;","uniform samplerBuffer textureN;"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput = normalize(normalMatrix *","    texelFetchBuffer(textureN, gl_PrimitiveID + PrimitiveIDOffset).xyz);","  if (gl_FrontFacing == false) { normalVCVSOutput = -normalVCVSOutput; }"]).result):t.lastBoundBO.getOpenGLMode(r.getProperty().getRepresentation())===t.context.LINES?(a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["vec3 normalVCVSOutput;","  if (abs(fdx.x) > 0.0)","    { fdx = normalize(fdx); normalVCVSOutput = normalize(cross(vec3(fdx.y, -fdx.x, 0.0), fdx)); }","  else { fdy = normalize(fdy); normalVCVSOutput = normalize(cross(vec3(fdy.y, -fdy.x, 0.0), fdy));}"]).result):(a=gd.substitute(a,"//VTK::Normal::Dec",["uniform int cameraParallel;"]).result,a=gd.substitute(a,"//VTK::UniformFlow::Impl",["  vec3 fdx = dFdx(vertexVC.xyz);","  vec3 fdy = dFdy(vertexVC.xyz);","  //VTK::UniformFlow::Impl"]).result,a=gd.substitute(a,"//VTK::Normal::Impl",["  fdx = normalize(fdx);","  fdy = normalize(fdy);","  vec3 normalVCVSOutput = normalize(cross(fdx,fdy));","  if (cameraParallel == 1 && normalVCVSOutput.z < 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }","  if (cameraParallel == 0 && dot(normalVCVSOutput,vertexVC.xyz) > 0.0) { normalVCVSOutput = -1.0*normalVCVSOutput; }"]).result),e.Vertex=n,e.Geometry=o,e.Fragment=a}},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r);let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(o=gd.substitute(o,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["vertexVCVSOutput = MCVCMatrix * vertexMC;","  gl_Position = MCPCMatrix * vertexMC;"]).result,o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Dec",["in vec4 vertexVCVSOutput[];","out vec4 vertexVCGSOutput;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["vertexVCGSOutput = vertexVCVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;"]).result):(o=gd.substitute(o,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,o=gd.substitute(o,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;if(t.drawingEdges)return;n=gd.substitute(n,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result;const a=t.openGLActor.getActiveTextures();let i=2,s=2;if(a&&a.length>0&&(i=a[0].getComponents(),a[0].getTarget()===t.context.TEXTURE_CUBE_MAP&&(s=3)),t.renderable.getColorTextureMap()&&(i=t.renderable.getColorTextureMap().getPointData().getScalars().getNumberOfComponents(),s=2),2===s){if(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,a&&a.length>=1)switch(i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}}else switch(n=gd.substitute(n,"//VTK::TCoord::Dec","attribute vec3 tcoordMC; varying vec3 tcoordVCVSOutput;").result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec3 tcoordVCVSOutput[];","out vec3 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];").result,o=gd.substitute(o,"//VTK::TCoord::Dec",["varying vec3 tcoordVCVSOutput;","uniform samplerCube texture1;"]).result,i){case 1:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;"]).result;break;case 2:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.r;","  diffuseColor = diffuseColor*tcolor.r;","  opacity = opacity * tcolor.g;"]).result;break;default:o=gd.substitute(o,"//VTK::TCoord::Impl",["  vec4 tcolor = textureCube(texture1, tcoordVCVSOutput);","  ambientColor = ambientColor*tcolor.rgb;","  diffuseColor = diffuseColor*tcolor.rgb;","  opacity = opacity * tcolor.a;"]).result}e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes();o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`uniform vec4 clipPlanes[${e}];`,`varying float clipDistancesVSOutput[${e}];`]).result,o=gd.substitute(o,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;",`varying float clipDistancesVSOutput[${e}];`]).result,a=gd.substitute(a,"//VTK::Clip::Impl",[`for (int planeNum = 0; planeNum < ${e}; planeNum++)`,"    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getCoincidentParameters=(e,n)=>{let r={factor:0,offset:0};const o=n.getProperty();if(t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset||o.getEdgeVisibility()&&o.getRepresentation()===qd.SURFACE){const e=t.lastBoundBO.getPrimitiveType();e===$d.Points||o.getRepresentation()===qd.POINTS?r=t.renderable.getCoincidentTopologyPointOffsetParameter():e===$d.Lines||o.getRepresentation()===qd.WIREFRAME?r=t.renderable.getCoincidentTopologyLineOffsetParameters():e!==$d.Tris&&e!==$d.TriStrips||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters()),e!==$d.TrisEdges&&e!==$d.TriStripsEdges||(r=t.renderable.getCoincidentTopologyPolygonOffsetParameters(),r.factor/=2,r.offset/=2)}const a=t._openGLRenderer.getSelector();return a&&a.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(r.offset-=2),r},e.replaceShaderPicking=(e,n,r)=>{let o=e.Fragment,a=e.Vertex;if(o=gd.substitute(o,"//VTK::Picking::Dec",["uniform int picking;","//VTK::Picking::Dec"]).result,t._openGLRenderer.getSelector()){switch(t.lastSelectionState!==Wl.ID_LOW24&&t.lastSelectionState!==Wl.ID_HIGH24||(a=gd.substitute(a,"//VTK::Picking::Dec",["flat out int vertexIDVSOutput;\n","uniform int VertexIDOffset;\n"]).result,a=gd.substitute(a,"//VTK::Picking::Impl","  vertexIDVSOutput = gl_VertexID + VertexIDOffset;\n").result,o=gd.substitute(o,"//VTK::Picking::Dec","flat in int vertexIDVSOutput;\n").result,o=gd.substitute(o,"//VTK::Picking::Impl",["  int idx = vertexIDVSOutput;","//VTK::Picking::Impl"]).result),t.lastSelectionState){case Wl.ID_LOW24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx%256)/255.0, float((idx/256)%256)/255.0, float((idx/65536)%256)/255.0, 1.0);").result;break;case Wl.ID_HIGH24:o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = vec4(float(idx)/255.0, 0.0, 0.0, 1.0);").result;break;default:o=gd.substitute(o,"//VTK::Picking::Dec","uniform vec3 mapperIndex;").result,o=gd.substitute(o,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result}e.Fragment=o,e.Vertex=a}},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderColor(n,r,o),e.replaceShaderNormal(n,r,o),e.replaceShaderLight(n,r,o),e.replaceShaderTCoord(n,r,o),e.replaceShaderPicking(n,r,o),e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}},e.getNeedToRebuildShaders=(e,n,r)=>{let o=0,a=0;const i=e.getPrimitiveType(),s=t.currentInput;let l=!1;const c=s.getPointData().getNormals(),u=s.getCellData().getNormals(),d=r.getProperty().getInterpolation()===Xd.FLAT,p=r.getProperty().getRepresentation(),f=e.getOpenGLMode(p,i);if(f===t.context.TRIANGLES||u&&!c||!d&&c?l=!0:d||f!==t.context.LINES||(l=!0),r.getProperty().getLighting()&&l){o=0;const e=n.getLightsByReference();for(let t=0;t<e.length;++t){const n=e[t];n.getSwitch()>0&&(a++,0===o&&(o=1)),1===o&&(a>1||1!==n.getIntensity()||!n.lightTypeIsHeadLight())&&(o=2),o<3&&n.getPositional()&&(o=3)}}let g=!1;const m=t.lastBoundBO.getReferenceByName("lastLightComplexity"),h=t.lastBoundBO.getReferenceByName("lastLightCount");return m===o&&h===a||(t.lastBoundBO.set({lastLightComplexity:o},!0),t.lastBoundBO.set({lastLightCount:a},!0),g=!0),(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(g=!0),!!(t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()||e.getShaderSourceTime().getMTime()<t.selectionStateChanged.getMTime()||g)&&(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,!0)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(n,r,o)=>{if(n.getProgram().isUniformUsed("PrimitiveIDOffset")&&n.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),n.getProgram().isUniformUsed("VertexIDOffset")&&n.getProgram().setUniformi("VertexIDOffset",t.vertexIDOffset),n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){const e=t.lastBoundBO.getReferenceByName("lastLightComplexity");n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("normalMC")&&n.getCABO().getNormalOffset()&&e>0?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"normalMC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,!1)||Jd("Error setting normalMC in shader VAO."):n.getVAO().removeAttributeArray("normalMC"),t.renderable.getCustomShaderAttributes().forEach(((e,r)=>{n.getProgram().isAttributeUsed(`${e}MC`)&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),`${e}MC`,n.getCABO().getCustomData()[r].offset,n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getCustomData()[r].components,!1)||Jd(`Error setting ${e}MC in shader VAO.`))})),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),!1)||Jd("Error setting tcoordMC in shader VAO."):n.getVAO().removeAttributeArray("tcoordMC"),n.getProgram().isAttributeUsed("scalarColor")&&n.getCABO().getColorComponents()?n.getVAO().addAttributeArray(n.getProgram(),n.getCABO().getColorBO(),"scalarColor",n.getCABO().getColorOffset(),n.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,4,!0)||Jd("Error setting scalarColor in shader VAO."):n.getVAO().removeAttributeArray("scalarColor"),n.getAttributeUpdateTime().modified()}if(t.renderable.getNumberOfClippingPlanes()){const e=t.renderable.getNumberOfClippingPlanes(),r=[],a=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,i=a?d(t.tmpMat4,o.getMatrix()):o.getMatrix();a&&(m(i,i),T(i,i,a),m(i,i));for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(i,n,e);for(let t=0;t<4;t++)r.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",r)}t.internalColorTexture&&n.getProgram().isUniformUsed("texture1")&&n.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const a=t.openGLActor.getActiveTextures();if(a)for(let e=0;e<a.length;++e){const t=a[e].getTextureUnit(),r=`texture${t+1}`;n.getProgram().isUniformUsed(r)&&n.getProgram().setUniformi(r,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}n.setMapperShaderParameters(r,o,t._openGLRenderer.getTiledSizeAndOrigin());const i=t._openGLRenderer.getSelector();n.getProgram().setUniform3fArray("mapperIndex",i?i.getPropColorValue():[0,0,0]),n.getProgram().setUniformi("picking",i?i.getCurrentPass()+1:0)},e.setLightingShaderParameters=(e,n,r)=>{const o=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(o<2)return;const a=e.getProgram();let i=0;const s=n.getLightsByReference();for(let e=0;e<s.length;++e){const r=s[e];if(r.getSwitch()>0){const e=r.getColorByReference(),o=r.getIntensity();t.lightColor[0]=e[0]*o,t.lightColor[1]=e[1]*o,t.lightColor[2]=e[2]*o;const s=r.getDirection(),l=n.getActiveCamera().getViewMatrix(),c=[...s];r.lightTypeIsSceneLight()&&(c[0]=l[0]*s[0]+l[1]*s[1]+l[2]*s[2],c[1]=l[4]*s[0]+l[5]*s[1]+l[6]*s[2],c[2]=l[8]*s[0]+l[9]*s[1]+l[10]*s[2],qo(c)),t.lightDirection[0]=c[0],t.lightDirection[1]=c[1],t.lightDirection[2]=c[2],qo(t.lightDirection),a.setUniform3fArray(`lightColor${i}`,t.lightColor),a.setUniform3fArray(`lightDirectionVC${i}`,t.lightDirection),i++}}if(o<3)return;const l=n.getActiveCamera().getViewMatrix();m(l,l),i=0;for(let e=0;e<s.length;++e){const t=s[e];if(t.getSwitch()>0){const e=t.getTransformedPosition(),n=new Float64Array(3);Vn(n,e,l),a.setUniform3fArray(`lightAttenuation${i}`,t.getAttenuationValuesByReference()),a.setUniformi(`lightPositional${i}`,t.getPositional()),a.setUniformf(`lightExponent${i}`,t.getExponent()),a.setUniformf(`lightConeAngle${i}`,t.getConeAngle()),a.setUniform3fArray(`lightPositionVC${i}`,[n[0],n[1],n[2]]),i++}}},e.setCameraShaderParameters=(e,a,i)=>{const s=e.getProgram(),l=t.openGLCamera.getKeyMatrices(a),c=a.getActiveCamera(),u=t.openGLCamera.getKeyMatrixTime().getMTime(),d=s.getLastCameraMTime(),p=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,f=i.getIsIdentity(),m=f?{mcwc:null,normalMatrix:null}:t.openGLActor.getKeyMatrices();if(i.getCoordinateSystem()===np.DISPLAY){const e=t._openGLRenderer.getTiledSizeAndOrigin();g(t.tmpMat4),t.tmpMat4[0]=2/e.usize,t.tmpMat4[12]=-1,t.tmpMat4[5]=2/e.vsize,t.tmpMat4[13]=-1,T(t.tmpMat4,t.tmpMat4,p),s.setUniformMatrix("MCPCMatrix",t.tmpMat4)}else s.setUniformMatrix("MCPCMatrix",n([l.wcpc,m.mcwc,p],r,t.tmpMat4));s.isUniformUsed("MCVCMatrix")&&s.setUniformMatrix("MCVCMatrix",n([l.wcvc,m.mcwc,p],r,t.tmpMat4)),s.isUniformUsed("normalMatrix")&&s.setUniformMatrix3x3("normalMatrix",n([l.normalMatrix,m.normalMatrix],o,t.tmpMat3)),d!==u&&(s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",c.getParallelProjection()),s.setLastCameraMTime(u)),f||s.setLastCameraMTime(0)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram();let a=r.getProperty(),i=a.getOpacity(),s=t.drawingEdges?a.getEdgeColorByReference():a.getAmbientColorByReference(),l=t.drawingEdges?a.getEdgeColorByReference():a.getDiffuseColorByReference(),c=t.drawingEdges?1:a.getAmbient(),u=t.drawingEdges?0:a.getDiffuse(),d=t.drawingEdges?0:a.getSpecular();const p=a.getSpecularPower();o.setUniformf("opacityUniform",i),o.setUniform3fArray("ambientColorUniform",s),o.setUniform3fArray("diffuseColorUniform",l),o.setUniformf("ambient",c),o.setUniformf("diffuse",u);const f=t.lastBoundBO.getReferenceByName("lastLightComplexity");if(f<1)return;let g=a.getSpecularColorByReference();if(o.setUniform3fArray("specularColorUniform",g),o.setUniformf("specularPowerUniform",p),o.setUniformf("specular",d),o.isUniformUsed("ambientIntensityBF")){if(a=r.getBackfaceProperty(),i=a.getOpacity(),s=a.getAmbientColor(),c=a.getAmbient(),l=a.getDiffuseColor(),u=a.getDiffuse(),g=a.getSpecularColor(),d=a.getSpecular(),o.setUniformf("ambientIntensityBF",c),o.setUniformf("diffuseIntensityBF",u),o.setUniformf("opacityUniformBF",i),o.setUniform3fArray("ambientColorUniformBF",s),o.setUniform3fArray("diffuseColorUniformBF",l),f<1)return;o.setUniformf("specularIntensityBF",d),o.setUniform3fArray("specularColorUniformBF",g),o.setUniformf("specularPowerUniformBF",p)}},e.updateMaximumPointCellIds=(e,n)=>{const r=t._openGLRenderer.getSelector();if(r){if(t.selectionWebGLIdsToVTKIds?.points?.length){const e=t.selectionWebGLIdsToVTKIds.points.length;r.setMaximumPointId(e-1)}if(t.selectionWebGLIdsToVTKIds?.cells?.length){const e=t.selectionWebGLIdsToVTKIds.cells.length;r.setMaximumCellId(e-1)}r.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.pointPicking=!0)}},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t.vertexIDOffset=0;const o=function(e){const t=e.getSelector();return t?t.getCurrentPass():Wl.MIN_KNOWN_PASS-1}(t._openGLRenderer);t.lastSelectionState!==o&&(t.selectionStateChanged.modified(),t.lastSelectionState=o),t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().renderProp(r),e.updateBufferObjects(n,r),t.renderable.getColorTextureMap()&&t.internalColorTexture.activate(),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=r.getProperty().getEdgeVisibility()&&o===qd.SURFACE,i=t._openGLRenderer.getSelector(),s=i&&i.getFieldAssociation()===Kd.FIELD_ASSOCIATION_POINTS&&(t.lastSelectionState===Wl.ID_LOW24||t.lastSelectionState===Wl.ID_HIGH24);for(let i=$d.Start;i<$d.End;i++)t.primitives[i].setPointPicking(s),t.primitives[i].getCABO().getElementCount()&&(t.drawingEdges=a&&(i===$d.TrisEdges||i===$d.TriStripsEdges),t.drawingEdges&&(t.renderDepth||t.lastSelectionState>=0)||(t.lastBoundBO=t.primitives[i],t.primitiveIDOffset+=t.primitives[i].drawArrays(n,r,o,e),t.vertexIDOffset+=t.primitives[i].getCABO().getElementCount()))},e.renderPieceFinish=(e,n)=>{t.LastBoundBO&&t.LastBoundBO.getVAO().release(),t.renderable.getColorTextureMap()&&t.internalColorTexture.deactivate()},e.renderPiece=(n,r)=>{if(e.invokeEvent(ep),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(tp),!t.currentInput)return void Jd("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context,a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()},e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(null===o)return;t.renderable.mapScalars(o,1);const a=t.renderable.getColorMapColors();t.haveCellScalars=!1;const i=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(i!==Yd.USE_CELL_DATA&&i!==Yd.USE_CELL_FIELD_DATA&&i!==Yd.USE_FIELD_DATA&&o.getPointData().getScalars()||i===Yd.USE_POINT_FIELD_DATA||!a||(t.haveCellScalars=!0));let s=r.getProperty().getInterpolation()!==Xd.FLAT?o.getPointData().getNormals():null;null===s&&o.getCellData().getNormals()&&(t.haveCellNormals=!0,s=o.getCellData().getNormals());const l=r.getProperty().getRepresentation();let c=o.getPointData().getTCoords();t.openGLActor.getActiveTextures()||(c=null);let u=!1;if(t.renderable.getColorCoordinates()){c=t.renderable.getColorCoordinates(),u=t.renderable.getAreScalarsMappedFromCells(),t.internalColorTexture||(t.internalColorTexture=Ud.newInstance({resizable:!0}));const e=t.internalColorTexture;e.setMinificationFilter(Zd.NEAREST),e.setMagnificationFilter(Zd.NEAREST),e.setWrapS(Qd.CLAMP_TO_EDGE),e.setWrapT(Qd.CLAMP_TO_EDGE),e.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t.renderable.getColorTextureMap(),r=n.getExtent(),o=n.getPointData().getScalars();e.create2DFromRaw(r[1]-r[0]+1,r[3]-r[2]+1,o.getNumberOfComponents(),o.getDataType(),o.getData()),e.activate(),e.sendParameters(),e.deactivate()}const d=`${o.getMTime()}A${l}B${o.getMTime()}C${s?s.getMTime():1}D${a?a.getMTime():1}E${r.getProperty().getEdgeVisibility()}F${c?c.getMTime():1}`;if(t.VBOBuildString!==d){const n={points:o.getPoints(),normals:s,tcoords:c,colors:a,cellOffset:0,vertexOffset:0,useTCoordsPerCell:u,haveCellScalars:t.haveCellScalars,haveCellNormals:t.haveCellNormals,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>o.getPointData().getArrayByName(e)))};t.renderable.getPopulateSelectionSettings()&&(t.selectionWebGLIdsToVTKIds={points:null,cells:null});const i=[{inRep:"verts",cells:o.getVerts()},{inRep:"lines",cells:o.getLines()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()},{inRep:"polys",cells:o.getPolys()},{inRep:"strips",cells:o.getStrips()}],p=r.getProperty().getEdgeVisibility()&&l===qd.SURFACE;for(let e=$d.Start;e<$d.End;e++)e!==$d.TrisEdges&&e!==$d.TriStripsEdges?(n.cellOffset+=t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,l,n,t.selectionWebGLIdsToVTKIds),n.vertexOffset+=t.primitives[e].getCABO().getElementCount()):p?t.primitives[e].getCABO().createVBO(i[e].cells,i[e].inRep,qd.WIREFRAME,{...n,tcoords:null,colors:null,haveCellScalars:!1,haveCellNormals:!1}):t.primitives[e].releaseGraphicsResources();t.renderable.getPopulateSelectionSettings()&&(t.renderable.setSelectionWebGLIdsToVTKIds(t.selectionWebGLIdsToVTKIds),e.updateMaximumPointCellIds()),t.VBOBuildString=d}t.VBOBuildTime.modified()},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const op={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null,tmpMat4:null,ambientColor:[],diffuseColor:[],specularColor:[],lightColor:[],lightDirection:[],lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastSelectionState:Wl.MIN_KNOWN_PASS-1,selectionStateChanged:null,selectionWebGLIdsToVTKIds:null,pointPicking:!1};function ap(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,op,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=$d,t.tmpMat3=pe(new Float64Array(9)),t.tmpMat4=g(new Float64Array(16));for(let e=$d.Start;e<$d.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.selectionStateChanged={},yt(t.selectionStateChanged,{mtime:0}),rp(e,t)}const ip=Dt(ap,"vtkOpenGLPolyDataMapper");var sp={newInstance:ip,extend:ap};rn("vtkMapper",ip);const{ColorMode:lp,ScalarMode:cp,GetArray:up}=hl;function dp(e,t){t.classHierarchy.push("vtkMapper2D"),e.createDefaultLookupTable=()=>{t.lookupTable=Dl.newInstance()},e.getColorModeAsString=()=>jt.enumToString(lp,t.colorMode),e.setColorModeToDefault=()=>e.setColorMode(0),e.setColorModeToMapScalars=()=>e.setColorMode(1),e.setColorModeToDirectScalars=()=>e.setColorMode(2),e.getScalarModeAsString=()=>jt.enumToString(cp,t.scalarMode),e.setScalarModeToDefault=()=>e.setScalarMode(0),e.setScalarModeToUsePointData=()=>e.setScalarMode(1),e.setScalarModeToUseCellData=()=>e.setScalarMode(2),e.setScalarModeToUsePointFieldData=()=>e.setScalarMode(3),e.setScalarModeToUseCellFieldData=()=>e.setScalarMode(4),e.setScalarModeToUseFieldData=()=>e.setScalarMode(5),e.getAbstractScalars=(e,n,r,o,a)=>{if(!e||!t.scalarVisibility)return{scalars:null,cellFLag:!1};let i=null,s=!1;if(n===cp.DEFAULT)i=e.getPointData().getScalars(),i||(i=e.getCellData().getScalars(),s=!0);else if(n===cp.USE_POINT_DATA)i=e.getPointData().getScalars();else if(n===cp.USE_CELL_DATA)i=e.getCellData().getScalars(),s=!0;else if(n===cp.USE_POINT_FIELD_DATA){const t=e.getPointData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_CELL_FIELD_DATA){const t=e.getCellData();s=!0,i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}else if(n===cp.USE_FIELD_DATA){const t=e.getFieldData();i=r===up.BY_ID?t.getArrayByIndex(o):t.getArrayByName(a)}return{scalars:i,cellFlag:s}},e.getLookupTable=()=>(t.lookupTable||e.createDefaultLookupTable(),t.lookupTable),e.getMTime=()=>{let e=t.mtime;if(null!==t.lookupTable){const n=t.lookupTable.getMTime();e=n>e?n:e}return e},e.mapScalars=(n,r)=>{const o=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;if(!o)return void(t.colorMapColors=null);const a=`${e.getMTime()}${o.getMTime()}${r}`;if(t.colorBuildString===a)return;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]);const i=e.getLookupTable();i&&(i.build(),t.colorMapColors=i.mapScalars(o,t.colorMode,t.fieldDataTupleId)),t.colorBuildString=`${e.getMTime()}${o.getMTime()}${r}`},e.getPrimitiveCount=()=>{const t=e.getInputData();return{points:t.getPoints().getNumberOfValues()/3,verts:t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells(),lines:t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells(),triangles:t.getPolys().getNumberOfValues()-3*t.getPolys().getNumberOfCells()}}}const pp={static:!1,lookupTable:null,scalarVisibility:!1,scalarRange:[0,1],useLookupTableScalarRange:!1,colorMode:0,scalarMode:0,arrayAccessMode:1,renderTime:0,colorByArrayName:null,transformCoordinate:null,viewSpecificProperties:null,customShaderAttributes:[]};function fp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pp,n),Bs(e,t,n),jt.get(e,t,["colorMapColors"]),jt.setGet(e,t,["arrayAccessMode","colorByArrayName","colorMode","lookupTable","renderTime","scalarMode","scalarVisibility","static","transformCoordinate","useLookupTableScalarRange","viewSpecificProperties","customShaderAttributes"]),jt.setGetArray(e,t,["scalarRange"],2),t.viewSpecificProperties||(t.viewSpecificProperties={}),dp(e,t)}var gp={newInstance:jt.newInstance(fp,"vtkMapper2D"),extend:fp};const mp=1,{primTypes:hp}=Sd,{ScalarMode:vp}=gp,{vtkErrorMacro:yp}=Kt,Tp={type:"StartEvent"},bp={type:"EndEvent"};function xp(e,t){t.classHierarchy.push("vtkOpenGLPolyDataMapper2D"),e.buildPass=n=>{n&&(t.openGLActor2D=e.getFirstAncestorOfType("vtkOpenGLActor2D"),t._openGLRenderer=t.openGLActor2D.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()))},e.overlayPass=t=>{t&&e.render()},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model Coordinates\n// WC - WC world coordinates\n// VC - View Coordinates\n// DC - Display Coordinates\n\nin vec4 vertexWC;\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// material property values\n//VTK::Color::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nuniform mat4 WCVCMatrix;  // World to view matrix\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  gl_Position = WCVCMatrix*vertexWC;\n\n  //VTK::TCoord::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::PositionVC::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyData2DFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nuniform int PrimitiveIDOffset;\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// Scalar coloring\n//VTK::Color::Dec\n\n// Depth Peeling\n//VTK::DepthPeeling::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Apple Bug\n//VTK::PrimID::Dec\n\nvoid main()\n{\n  // Apple Bug\n  //VTK::PrimID::Impl\n\n  //VTK::Color::Impl\n  //VTK::TCoord::Impl\n\n  //VTK::DepthPeeling::Impl\n  //VTK::Picking::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n}\n",e.Geometry=""},e.render=()=>{const n=t._openGLRenderWindow.getContext();if(t.context!==n){t.context=n;for(let e=hp.Start;e<hp.End;e++)t.primitives[e].setOpenGLRenderWindow(t._openGLRenderWindow)}const r=t.openGLActor2D.getRenderable(),o=t._openGLRenderer.getRenderable();e.renderPiece(o,r)},e.renderPiece=(n,r)=>{e.invokeEvent(Tp),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent(bp),t.currentInput?t.currentInput.getPoints&&t.currentInput.getPoints().getNumberOfValues()&&(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):yp("No input!")},e.renderPieceStart=(n,r)=>{t.primitiveIDOffset=0,t._openGLRenderer.getSelector()&&(t._openGLRenderer.getSelector().getCurrentPass(),t._openGLRenderer.getSelector().renderProp(r)),e.updateBufferObjects(n,r),t.lastBoundBO=null},e.getNeedToRebuildShaders=(e,n,r)=>e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime(),e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return!!(o<e.getMTime()||o<t._openGLRenderWindow.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentInput.getMTime()||t.renderable.getTransformCoordinate()&&o<n.getMTime())},e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,n.getProperty().getOpacity());const o=t.renderable.getColorMapColors();t.haveCellScalars=!1;const a=t.renderable.getScalarMode();t.renderable.getScalarVisibility()&&(a!==vp.USE_CELL_DATA&&a!==vp.USE_CELL_FIELD_DATA&&a!==vp.USE_FIELD_DATA&&r.getPointData().getScalars()||a===vp.USE_POINT_FIELD_DATA||!o||(t.haveCellScalars=!0));const i=n.getProperty().getRepresentation();let s=r.getPointData().getTCoords();t.openGLActor2D.getActiveTextures()||(s=null);const l=t.renderable.getTransformCoordinate(),c=e.getRenderWindow().getViews()[0].getViewportSize(e),u=`${r.getMTime()}A${i}B${r.getMTime()}C${o?o.getMTime():1}D${s?s.getMTime():1}E${l?e.getMTime():1}F${c}`;if(t.VBOBuildString!==u){let n=r.getPoints();if(l){const t=Tc.newInstance(),r=n.getNumberOfPoints();t.setNumberOfPoints(r);const o=[];for(let a=0;a<r;++a){n.getPoint(a,o),l.setValue(o);const r=l.getComputedDoubleViewportValue(e);t.setPoint(a,r[0],r[1],0)}n=t}const a={points:n,tcoords:s,colors:o,cellOffset:0,haveCellScalars:t.haveCellSCalars,customAttributes:t.renderable.getCustomShaderAttributes().map((e=>r.getPointData().getArrayByName(e)))};a.cellOffset+=t.primitives[hp.Points].getCABO().createVBO(r.getVerts(),"verts",i,a),a.cellOffset+=t.primitives[hp.Lines].getCABO().createVBO(r.getLines(),"lines",i,a),a.cellOffset+=t.primitives[hp.Tris].getCABO().createVBO(r.getPolys(),"polys",i,a),a.cellOffset+=t.primitives[hp.TriStrips].getCABO().createVBO(r.getStrips(),"strips",i,a),t.VBOBuildTime.modified(),t.VBOBuildString=u}},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation();t.context.depthMask(!0);for(let a=hp.Start;a<hp.End;a++)t.primitives[a].getCABO().getElementCount()&&(t.lastBoundBO=t.primitives[a],t.primitiveIDOffset+=t.primitives[a].drawArrays(n,r,o,e))},e.renderPieceFinish=(e,n)=>{t.lastBoundBO&&t.lastBoundBO.getVAO().release()},e.replaceShaderValues=(t,n,r)=>{e.replaceShaderColor(t,n,r),e.replaceShaderTCoord(t,n,r),e.replaceShaderPicking(t,n,r),e.replaceShaderPositionVC(t,n,r)},e.replaceShaderColor=(e,n,r)=>{let o=e.Vertex,a=e.Geometry,i=e.Fragment;t.haveCellScalars&&(i=gd.substitute(i,"//VTK::Color::Dec",["uniform samplerBuffer texture1;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = texelFetchBuffer(texture1, gl_PrimitiveID + PrimitiveIDOffset);"]).result),0!==t.lastBoundBO.getCABO().getColorComponents()?(o=gd.substitute(o,"//VTK::Color::Dec",["in vec4 diffuseColor;","out vec4 fcolorVSOutput;"]).result,o=gd.substitute(o,"//VTK::Color::Impl",["fcolorVSOutput = diffuseColor;"]).result,a=gd.substitute(a,"//VTK::Color::Dec",["in vec4 fcolorVSOutput[];\n","out vec4 fcolorGSOutput;"]).result,a=gd.substitute(a,"//VTK::Color::Impl",["fcolorGSOutput = fcolorVSOutput[i];"]).result,i=gd.substitute(i,"//VTK::Color::Dec",["in vec4 fcolorVSOutput;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = fcolorVSOutput;"]).result):(i=gd.substitute(i,"//VTK::Color::Dec",["uniform vec4 diffuseColor;"]).result,i=gd.substitute(i,"//VTK::Color::Impl",["gl_FragData[0] = diffuseColor;"]).result),e.Vertex=o,e.Geometry=a,e.Fragment=i},e.replaceShaderTCoord=(e,n,r)=>{if(t.lastBoundBO.getCABO().getTCoordOffset()){let n=e.Vertex,r=e.Geometry,o=e.Fragment;const a=t.lastBoundBO.getCABO().getTCoordComponents();1===a?(n=gd.substitute(n,"//VTK::TCoord::Dec",["in float tcoordMC;","out float tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput[];\n","out float tcoordVCGSOutput;"]).result,r=gd.substitute(r,["//VTK::TCoord::Impl","tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in float tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, vec2(tcoordVCVSOutput,0));"]).result):2===a&&(n=gd.substitute(n,"//VTK::TCoord::Dec",["in vec2 tcoordMC;","out vec2 tcoordVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::TCoord::Impl",["tcoordVCVSOutput = tcoordMC;"]).result,r=gd.substitute(r,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput[];\n","out vec2 tcoordVCGSOutput;"]).result,r=gd.substitute(r,"//VTK::TCoord::Impl",["tcoordVCGSOutput = tcoordVCVSOutput[i];"]).result,o=gd.substitute(o,"//VTK::TCoord::Dec",["in vec2 tcoordVCVSOutput;","uniform sampler2D texture1;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["gl_FragData[0] = gl_FragData[0]*texture2D(texture1, tcoordVCVSOutput.st);"]).result),t.haveCellScalars&&(r=gd.substitute(r,"//VTK::PrimID::Impl",["gl_PrimitiveID = gl_PrimitiveIDIn;"]).result),e.Vertex=n,e.Geometry=r,e.Fragment=o}},e.replaceShaderPicking=(e,t,n)=>{let r=e.Fragment;r=gd.substitute(r,"//VTK::Picking::Dec",["uniform vec3 mapperIndex;","uniform int picking;"]).result,r=gd.substitute(r,"//VTK::Picking::Impl","  gl_FragData[0] = picking != 0 ? vec4(mapperIndex,1.0) : gl_FragData[0];").result,e.Fragment=r},e.replaceShaderPositionVC=(e,n,r)=>{t.lastBoundBO.replaceShaderPositionVC(e,n,r)},e.invokeShaderCallbacks=(e,n,r)=>{const o=t.renderable.getViewSpecificProperties().ShadersCallbacks;o&&o.forEach((t=>{t.callback(t.userData,e,n,r)}))},e.setMapperShaderParameters=(e,n,r)=>{if(e.getProgram().isUniformUsed("PrimitiveIDOffset")&&e.getProgram().setUniformi("PrimitiveIDOffset",t.primitiveIDOffset),e.getProgram().isAttributeUsed("vertexWC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"vertexWC",e.getCABO().getVertexOffset(),e.getCABO().getStride(),t.context.FLOAT,3,!1)||yp("Error setting vertexWC in shader VAO.")),e.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())){t.renderable.getCustomShaderAttributes().forEach(((n,r)=>{e.getProgram().isAttributeUsed(`${n}MC`)&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),`${n}MC`,e.getCABO().getCustomData()[r].offset,e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getCustomData()[r].components,!1)||yp(`Error setting ${n}MC in shader VAO.`))})),e.getProgram().isAttributeUsed("tcoordMC")&&e.getCABO().getTCoordOffset()?e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"tcoordMC",e.getCABO().getTCoordOffset(),e.getCABO().getStride(),t.context.FLOAT,e.getCABO().getTCoordComponents(),!1)||yp("Error setting tcoordMC in shader VAO."):e.getVAO().removeAttributeArray("tcoordMC"),t.internalColorTexture&&e.getProgram().isUniformUsed("texture1")&&e.getProgram().setUniformi("texture1",t.internalColorTexture.getTextureUnit());const o=t.openGLActor2D.getActiveTextures();if(o)for(let t=0;t<o.length;++t){const n=o[t].getTextureUnit(),r=`texture${n+1}`;e.getProgram().isUniformUsed(r)&&e.getProgram().setUniformi(r,n)}e.setMapperShaderParameters(n,r,t._openGLRenderer.getTiledSizeAndOrigin());const a=t._openGLRenderer.getSelector();e.getProgram().setUniform3fArray("mapperIndex",a?a.getPropColorValue():[0,0,0]),e.getProgram().setUniformi("picking",a?a.getCurrentPass()+1:0)}},e.setPropertyShaderParameters=(e,n,r)=>{const o=t.renderable.getColorMapColors();if(!o||0===o.getNumberOfComponents()){const t=e.getProgram(),n=r.getProperty(),o=n.getOpacity(),a=n.getColor(),i=[a[0],a[1],a[2],o];t.setUniform4f("diffuseColor",i)}},e.setLightingShaderParameters=(e,t,n)=>{},e.setCameraShaderParameters=(e,n,o)=>{const a=e.getProgram(),i=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,s=n.getRenderWindow().getViews()[0].getViewportSize(n),l=n.getViewport(),c=o.getActualPositionCoordinate().getComputedDoubleViewportValue(n),u=[0,0,1,1],d=[0,0,1,1];if(d[0]=l[0]>=u[0]?l[0]:u[0],d[1]=l[1]>=u[1]?l[1]:u[1],d[2]=l[2]<=u[2]?l[2]:u[2],d[3]=l[3]<=u[3]?l[3]:u[3],d[0]>=d[2])return;if(d[1]>=d[3])return;s[0]=Mo(s[0]*(d[2]-d[0])/(l[2]-l[0])),s[1]=Mo(s[1]*(d[3]-d[1])/(l[3]-l[1]));const p=t._openGLRenderer.getParent().getSize(),f=Mo(c[0]-(d[0]-l[0])*p[0]),h=Mo(c[1]-(d[1]-l[1])*p[1]),v=-f;let y=-f+s[0];const T=-h;let b=-h+s[1];v===y&&(y=v+1),T===b&&(b=T+1);const x=g(new Float64Array(16));var C,S,A;x[0]=2/(y-v),x[5]=2/(b-T),x[3]=-1*(y+v)/(y-v),x[7]=-1*(b+T)/(b-T),x[10]=0,x[11]=o.getProperty().getDisplayLocation()===mp?-1:1,x[15]=1,m(x,x),a.setUniformMatrix("WCVCMatrix",(C=[x,i],S=r,A=t.tmpMat4,S.identity(A),C.reduce(((e,t,n)=>0===n?t?S.copy(e,t):S.identity(e):t?S.multiply(e,e,t):e),A)))},e.getAllocatedGPUMemoryInBytes=()=>{let e=0;return t.primitives.forEach((t=>{e+=t.getAllocatedGPUMemoryInBytes()})),e}}const Cp={context:null,VBOBuildTime:0,VBOBuildString:null,primitives:null,primTypes:null,shaderRebuildString:null};const Sp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Cp,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.primitives=[],t.primTypes=hp,t.tmpMat4=g(new Float64Array(16));for(let e=hp.Start;e<hp.End;e++)t.primitives[e]=Sd.newInstance(),t.primitives[e].setPrimitiveType(e),t.primitives[e].set({lastLightComplexity:0,lastLightCount:0,lastSelectionPass:!1},!0);At(e,t,["context"]),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),xp(e,t)}),"vtkOpenGLPolyDataMapper2D");rn("vtkMapper2D",Sp);const{VectorMode:Ap}=Pl;function Ip(e,t){e.strokeStyle=t.strokeColor,e.lineWidth=t.strokeSize,e.fillStyle=t.fontColor,e.font=`${t.fontStyle} ${t.fontSize}px ${t.fontFamily}`}function wp(e,t){return e=>{const n=e.getLastSize(),r=(n[0]/700)**.8,o=(n[1]/700)**.8,a=Math.min(r,o),i=e.getAxisTextStyle(),s=e.getTickTextStyle();Object.assign(i,t.axisTextStyle),Object.assign(s,t.tickTextStyle),i.fontSize=Math.max(24*a,12),e.getLastAspectRatio()>1?s.fontSize=Math.max(20*a,10):s.fontSize=Math.max(16*a,10);const l=e.updateTextureAtlas();e.setTopTitle(!1);const c=e.getBoxSizeByReference();if(e.getLastAspectRatio()>1)e.setTickLabelPixelOffset(.3*s.fontSize),l.titleWidth<=l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize?(e.setTopTitle(!0),e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.98-c[0],-.92])):(e.setAxisTitlePixelOffset(.2*s.fontSize),c[0]=2*(l.titleHeight+e.getAxisTitlePixelOffset()+l.tickWidth+e.getTickLabelPixelOffset()+.8*s.fontSize)/n[0],e.setBoxPosition([.99-c[0],-.92])),c[1]=Math.max(1.2,Math.min(1.84/o,1.84));else{e.setAxisTitlePixelOffset(1.2*s.fontSize),e.setTickLabelPixelOffset(.1*s.fontSize);const t=2*(.8*s.fontSize+l.titleHeight+e.getAxisTitlePixelOffset())/n[1],r=2*l.tickWidth/n[0];c[0]=Math.min(1.9,Math.max(1.4,1.4*r*(e.getTicks().length+3))),c[1]=t,e.setBoxPosition([-.5*c[0],-.97])}e.recomputeBarSegments(l)}}function Pp(e,t){return e=>{const t=e.getLastTickBounds(),n=go().domain([t[0],t[1]]),r=n.ticks(5),o=n.tickFormat(5);e.setTicks(r),e.setTickStrings(r.map(o))}}function Op(e,t){t.classHierarchy.push("vtkScalarBarActorHelper"),e.setRenderable=n=>{t.renderable!==n&&(t.renderable=n,t.barActor.setProperty(n.getProperty()),t.barActor.setParentProp(n),t.barActor.setCoordinateSystemToDisplay(),t.tmActor.setProperty(n.getProperty()),t.tmActor.setParentProp(n),t.tmActor.setCoordinateSystemToDisplay(),t.generateTicks=n.generateTicks,t.axisTextStyle={...n.getAxisTextStyle()},t.tickTextStyle={...n.getTickTextStyle()},e.modified())},e.updateAPISpecificData=(n,r,o)=>{t.lastSize[0]===n[0]&&t.lastSize[1]===n[1]||(t.lastSize[0]=n[0],t.lastSize[1]=n[1],t.lastAspectRatio=n[0]/n[1],t.forceUpdate=!0);const a=t.renderable.getScalarsToColors();if(a&&t.renderable.getVisibility()&&(t.barMapper.setLookupTable(a),t.camera=r,t.renderWindow=o,t.forceUpdate||Math.max(a.getMTime(),e.getMTime(),t.renderable.getMTime())>t.lastRebuildTime.getMTime())){const n=a.getMappingRange();if(t.lastTickBounds=[...n],t.renderable.getGenerateTicks()(e),t.renderable.getAutomated())t.renderable.getAutoLayout()(e);else{t.axisTextStyle={...t.renderable.getAxisTextStyle()},t.tickTextStyle={...t.renderable.getTickTextStyle()},t.barPosition=[...t.renderable.getBarPosition()],t.barSize=[...t.renderable.getBarSize()],t.boxPosition=[...t.renderable.getBoxPosition()],t.boxSize=[...t.renderable.getBoxSize()],t.axisTitlePixelOffset=t.renderable.getAxisTitlePixelOffset(),t.tickLabelPixelOffset=t.renderable.getTickLabelPixelOffset();const n=e.updateTextureAtlas();e.recomputeBarSegments(n)}e.updatePolyDataForLabels(),e.updatePolyDataForBarSegments(),t.lastRebuildTime.modified(),t.forceUpdate=!1}},e.updateTextureAtlas=()=>{t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left";const n={},r=new Map;let o=0,a=1;Ip(t.tmContext,t.axisTextStyle);let i=t.tmContext.measureText(t.renderable.getAxisLabel()),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.axisTextStyle};r.set(t.renderable.getAxisLabel(),s),a+=s.height,o=s.width,n.titleWidth=s.width,n.titleHeight=s.height,n.tickWidth=0,n.tickHeight=0,Ip(t.tmContext,t.tickTextStyle);const l=[...e.getTickStrings(),"NaN","Below","Above"];for(let e=0;e<l.length;e++)r.has(l[e])||(i=t.tmContext.measureText(l[e]),s={height:i.actualBoundingBoxAscent+2,startingHeight:a,width:i.width+2,textStyle:t.tickTextStyle},r.set(l[e],s),a+=s.height,o<s.width&&(o=s.width),n.tickWidth<s.width&&(n.tickWidth=s.width),n.tickHeight<s.height&&(n.tickHeight=s.height));return o=_o(o),a=_o(a),r.forEach((e=>{e.tcoords=[0,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight-e.height)/a,e.width/o,(a-e.startingHeight)/a,0,(a-e.startingHeight)/a]})),t.tmCanvas.width=o,t.tmCanvas.height=a,t.tmContext.textBaseline="bottom",t.tmContext.textAlign="left",t.tmContext.clearRect(0,0,o,a),r.forEach(((e,n)=>{Ip(t.tmContext,e.textStyle),t.tmContext.fillText(n,1,e.startingHeight+e.height-1)})),t.tmTexture.setCanvas(t.tmCanvas),t.tmTexture.modified(),t._tmAtlas=r,n},e.computeBarSize=e=>{t.vertical=t.boxSize[1]>t.boxSize[0];const n=2*e.tickHeight/t.lastSize[1],r=[1,1];if(t.vertical){const o=2*(e.tickWidth+t.tickLabelPixelOffset)/t.lastSize[0];if(t.topTitle){const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0]-o,t.barSize[1]=t.boxSize[1]-n}else{const n=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[0];t.barSize[0]=t.boxSize[0]-n-o,t.barSize[1]=t.boxSize[1]}t.barPosition[0]=t.boxPosition[0]+o,t.barPosition[1]=t.boxPosition[1],r[1]=n}else{const n=(2*e.tickWidth-8)/t.lastSize[0],o=2*(e.titleHeight+t.axisTitlePixelOffset)/t.lastSize[1];t.barSize[0]=t.boxSize[0],t.barPosition[0]=t.boxPosition[0],t.barSize[1]=t.boxSize[1]-o,t.barPosition[1]=t.boxPosition[1],r[0]=n}return r},e.recomputeBarSegments=n=>{const r=e.computeBarSize(n);t.barSegments=[];const o=[0,0],a=t.vertical?1:0,i=t.vertical?.01:.02;function s(e,n){t.barSegments.push({corners:[[...o],[o[0]+r[0],o[1]],[o[0]+r[0],o[1]+r[1]],[o[0],o[1]+r[1]]],scalars:n,title:e}),o[a]+=r[a]+i}t.renderable.getDrawNanAnnotation()&&t.renderable.getScalarsToColors().getNanColor()&&s("NaN",[NaN,NaN,NaN,NaN]),t.renderable.getDrawBelowRangeSwatch()&&t.renderable.getScalarsToColors().getUseBelowRangeColor?.()&&s("Below",[-.1,-.1,-.1,-.1]);const l=t.renderable.getScalarsToColors().getUseAboveRangeColor?.();o[a]+=i;const c=r[a];r[a]=l?1-2*i-r[a]-o[a]:1-i-o[a],s("ticks",t.vertical?[0,0,.995,.995]:[0,.995,.995,0]),t.renderable.getDrawAboveRangeSwatch()&&l&&(r[a]=c,o[a]+=i,s("Above",[1.1,1.1,1.1,1.1]))};const n=new Float64Array(3);e.createPolyDataForOneLabel=(e,r,o,a,i,s)=>{const l=t._tmAtlas.get(e);if(!l)return;let c=s.ptIdx,u=s.cellIdx;n[0]=(.5*r[0]+.5)*t.lastSize[0],n[1]=(.5*r[1]+.5)*t.lastSize[1],n[2]=r[2],n[0]+=i[0],n[1]+=i[1];const d=[],p="vertical"===a?[1,0]:[0,1];"vertical"===a?(d[0]=l.width,d[1]=-l.height,"middle"===o[0]?n[1]-=l.width/2:"right"===o[0]&&(n[1]-=l.width),"middle"===o[1]?n[0]+=l.height/2:"top"===o[1]&&(n[0]+=l.height)):(d[0]=l.width,d[1]=l.height,"middle"===o[0]?n[0]-=l.width/2:"right"===o[0]&&(n[0]-=l.width),"middle"===o[1]?n[1]-=l.height/2:"top"===o[1]&&(n[1]-=l.height)),s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[0],s.tcoords[2*c+1]=l.tcoords[1],c++,n[p[0]]+=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[2],s.tcoords[2*c+1]=l.tcoords[3],c++,n[p[1]]+=d[1],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[4],s.tcoords[2*c+1]=l.tcoords[5],c++,n[p[0]]-=d[0],s.points[3*c]=n[0],s.points[3*c+1]=n[1],s.points[3*c+2]=n[2],s.tcoords[2*c]=l.tcoords[6],s.tcoords[2*c+1]=l.tcoords[7],c++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-3,s.polys[4*u+3]=c-2,u++,s.polys[4*u]=3,s.polys[4*u+1]=c-4,s.polys[4*u+2]=c-2,s.polys[4*u+3]=c-1,s.ptIdx+=4,s.cellIdx+=2};const r=new Float64Array(3);e.updatePolyDataForLabels=()=>{const n=e.getTickStrings().length+t.barSegments.length,o=4*n,a=2*n,i=new Float64Array(3*o),s=new Uint16Array(4*a),l=new Float32Array(2*o),c={ptIdx:0,cellIdx:0,polys:s,points:i,tcoords:l},u=t.vertical?0:1,d=t.vertical?1:0;r[2]=-.99;const p=t.vertical?["right","middle"]:["middle","bottom"];let f=[0,1];const g=[0,0];t.vertical?(g[0]=-t.tickLabelPixelOffset,t.topTitle?(r[0]=t.boxPosition[0]+.5*t.boxSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)):(r[0]=t.barPosition[0]+t.barSize[0],r[1]=t.barPosition[1]+.5*t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","top"],"vertical",[t.axisTitlePixelOffset,0],c)),f=[-1,0]):(g[1]=t.tickLabelPixelOffset,r[0]=t.barPosition[0]+.5*t.barSize[0],r[1]=t.barPosition[1]+t.barSize[1],e.createPolyDataForOneLabel(t.renderable.getAxisLabel(),r,["middle","bottom"],"horizontal",[0,t.axisTitlePixelOffset],c)),r[u]=t.barPosition[u]+(.5*f[u]+.5)*t.barSize[u],r[d]=t.barPosition[d]+.5*t.barSize[d];let m=null;for(let n=0;n<t.barSegments.length;n++){const o=t.barSegments[n];"ticks"===o.title?m=o:(r[d]=t.barPosition[d]+.5*t.barSize[d]*(o.corners[2][d]+o.corners[0][d]),e.createPolyDataForOneLabel(o.title,r,p,"horizontal",g,c))}const h=t.barPosition[d]+t.barSize[d]*m.corners[0][d],v=t.barSize[d]*(m.corners[2][d]-m.corners[0][d]),y=e.getTicks(),T=e.getTickStrings();for(let n=0;n<y.length;n++){const o=(y[n]-t.lastTickBounds[0])/(t.lastTickBounds[1]-t.lastTickBounds[0]);r[d]=h+v*o,e.createPolyDataForOneLabel(T[n],r,p,"horizontal",g,c)}const b=Ds.newInstance({numberOfComponents:2,values:l,name:"TextureCoordinates"});t.tmPolyData.getPointData().setTCoords(b),t.tmPolyData.getPoints().setData(i,3),t.tmPolyData.getPoints().modified(),t.tmPolyData.getPolys().setData(s,1),t.tmPolyData.getPolys().modified(),t.tmPolyData.modified()},e.updatePolyDataForBarSegments=()=>{const e=t.renderable.getScalarsToColors();let n=0;t.renderable.getDrawNanAnnotation()&&e.getNanColor()&&(n+=1),t.renderable.getDrawBelowRangeSwatch()&&e.getUseBelowRangeColor?.()&&(n+=1),t.renderable.getDrawAboveRangeSwatch()&&e.getUseAboveRangeColor?.()&&(n+=1);const o=4*(1+n),a=o;let i=1;e.getVectorMode()===Ap.COMPONENT&&(i=e.getVectorComponent()+1);const s=new Float64Array(3*o),l=new Uint16Array(5*a),c=new Float32Array(o*i);let u=0,d=0;for(let e=0;e<t.barSegments.length;e++){const n=t.barSegments[e];for(let e=0;e<4;e++){r[0]=t.barPosition[0]+n.corners[e][0]*t.barSize[0],r[1]=t.barPosition[1]+n.corners[e][1]*t.barSize[1],s[3*u]=(.5*r[0]+.5)*t.lastSize[0],s[3*u+1]=(.5*r[1]+.5)*t.lastSize[1],s[3*u+2]=r[2];for(let r=0;r<i;r++)c[u*i+r]=t.lastTickBounds[0]+n.scalars[e]*(t.lastTickBounds[1]-t.lastTickBounds[0]);u++}l[5*d]=4,l[5*d+1]=u-4,l[5*d+2]=u-3,l[5*d+3]=u-2,l[5*d+4]=u-1,d++}const p=Ds.newInstance({numberOfComponents:i,values:c,name:"Scalars"});t.polyData.getPointData().setScalars(p),t.polyData.getPoints().setData(s,3),t.polyData.getPoints().modified(),t.polyData.getPolys().setData(l,1),t.polyData.getPolys().modified(),t.polyData.modified()}}const Rp=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{renderable:null};Object.assign(t,{},n),jt.obj(e,t),jt.setGet(e,t,["axisTitlePixelOffset","tickLabelPixelOffset","renderable","topTitle","ticks","tickStrings"]),jt.get(e,t,["lastSize","lastAspectRatio","lastTickBounds","axisTextStyle","tickTextStyle","barActor","tmActor"]),jt.getArray(e,t,["boxPosition","boxSize"]),jt.setArray(e,t,["boxPosition","boxSize"],2),t.forceUpdate=!1,t.lastRebuildTime={},jt.obj(t.lastRebuildTime,{mtime:0}),t.lastSize=[-1,-1],t.tmCanvas=document.createElement("canvas"),t.tmContext=t.tmCanvas.getContext("2d"),t._tmAtlas=new Map,t.barMapper=ic.newInstance(),t.barMapper.setInterpolateScalarsBeforeMapping(!0),t.barMapper.setUseLookupTableScalarRange(!0),t.polyData=yu.newInstance(),t.barMapper.setInputData(t.polyData),t.barActor=hs.newInstance(),t.barActor.setMapper(t.barMapper),t.tmPolyData=yu.newInstance(),t.tmMapper=ic.newInstance(),t.tmMapper.setInputData(t.tmPolyData),t.tmTexture=Su.newInstance({resizable:!0}),t.tmTexture.setInterpolate(!1),t.tmActor=hs.newInstance({parentProp:e}),t.tmActor.setMapper(t.tmMapper),t.tmActor.addTexture(t.tmTexture),t.barPosition=[0,0],t.barSize=[0,0],t.boxPosition=[.88,-.92],t.boxSize=[.1,1.1],t.lastTickBounds=[],Op(e,t)}),"vtkScalarBarActorHelper");function Mp(e,t){t.classHierarchy.push("vtkScalarBarActor"),e.setTickTextStyle=n=>{t.tickTextStyle={...t.tickTextStyle,...n},e.modified()},e.setAxisTextStyle=n=>{t.axisTextStyle={...t.axisTextStyle,...n},e.modified()},e.resetAutoLayoutToDefault=()=>{e.setAutoLayout(wp(0,t))},e.resetGenerateTicksToDefault=()=>{e.setGenerateTicks(Pp())}}function Ep(e){return{automated:!0,autoLayout:null,axisLabel:"Scalar Value",barPosition:[0,0],barSize:[0,0],boxPosition:[.88,-.92],boxSize:[.1,1.1],scalarToColors:null,axisTitlePixelOffset:36,axisTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:18,fontFamily:"serif"},tickLabelPixelOffset:14,tickTextStyle:{fontColor:"white",fontStyle:"normal",fontSize:14,fontFamily:"serif"},generateTicks:null,drawNanAnnotation:!0,drawBelowRangeSwatch:!0,drawAboveRangeSwatch:!0,...e}}function Dp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ep(n)),t.autoLayout||(t.autoLayout=wp(0,t)),t.generateTicks||(t.generateTicks=Pp()),hs.extend(e,t,n),e.getProperty().setDiffuse(0),e.getProperty().setAmbient(1),jt.setGet(e,t,["automated","autoLayout","axisTitlePixelOffset","axisLabel","scalarsToColors","tickLabelPixelOffset","generateTicks","drawNanAnnotation","drawBelowRangeSwatch","drawAboveRangeSwatch"]),jt.get(e,t,["axisTextStyle","tickTextStyle"]),jt.getArray(e,t,["barPosition","barSize","boxPosition","boxSize"]),jt.setArray(e,t,["barPosition","barSize","boxPosition","boxSize"],2),Mp(e,t)}var Vp={newInstance:jt.newInstance(Dp,"vtkScalarBarActor"),extend:Dp,newScalarBarActorHelper:Rp};function Lp(e,t){t.classHierarchy.push("vtkOpenGLScalarBarActor"),e.buildPass=n=>{n&&(t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.scalarBarActorHelper.getRenderable()||t.scalarBarActorHelper.setRenderable(t.renderable),e.prepareNodes(),e.addMissingNode(t.scalarBarActorHelper.getBarActor()),e.addMissingNode(t.scalarBarActorHelper.getTmActor()),e.removeUnusedNodes())},e.opaquePass=(e,n)=>{if(e){const e=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,n=t._openGLRenderer.getTiledSizeAndOrigin();t.scalarBarActorHelper.updateAPISpecificData([n.usize,n.vsize],e,t._openGLRenderWindow.getRenderable())}}}const Bp={};const Np=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bp,n),Zt.extend(e,t,n),t.scalarBarActorHelper=Vp.newScalarBarActorHelper(),Lp(e,t)}),"vtkOpenGLScalarBarActor");rn("vtkScalarBarActor",Np);const{vtkErrorMacro:_p}=Kt;function Fp(e,t){t.classHierarchy.push("vtkOpenGLSkybox"),e.buildPass=n=>{if(n){t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getParent(),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(n.getActiveCamera())}},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementOpaqueActorCount()}},e.opaquePass=(n,r)=>{if(n&&!t._openGLRenderer.getSelector()){e.updateBufferObjects(),t.context.depthMask(!0),t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.tris.getProgram()),t.openGLTexture.render(t._openGLRenderWindow);const n=t.openGLTexture.getTextureUnit();t.tris.getProgram().setUniformi("sbtexture",n);const r=t._openGLRenderer.getRenderable(),o=t.openGLCamera.getKeyMatrices(r),a=new Float64Array(16);if(h(a,o.wcpc),t.tris.getProgram().setUniformMatrix("IMCPCMatrix",a),"box"===t.lastFormat){const e=r.getActiveCamera().getPosition();t.tris.getProgram().setUniform3f("camPos",e[0],e[1],e[2])}t.tris.getVAO().bind(),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate()}},e.updateBufferObjects=()=>{if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=1;const n=Ds.newInstance({numberOfComponents:3,values:e});n.setName("points");const r=new Uint16Array(8);r[0]=3,r[1]=0,r[2]=1,r[3]=3,r[4]=3,r[5]=0,r[6]=3,r[7]=2;const o=Ds.newInstance({numberOfComponents:1,values:r});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:n,cellOffset:0})}t.renderable.getFormat()!==t.lastFormat&&(t.lastFormat=t.renderable.getFormat(),"box"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec3 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = wpos.xyz/wpos.w;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec3 TexCoords;\n             uniform samplerCube sbtexture;\n             uniform vec3 camPos;\n             void main () {\n               // skybox looks from inside out\n               // which means we have to adjust\n               // our tcoords. Otherwise text would\n               // be flipped\n               vec3 tc = normalize(TexCoords - camPos);\n               if (abs(tc.z) < max(abs(tc.x),abs(tc.y)))\n               {\n                 tc = vec3(1.0, 1.0, -1.0) * tc;\n               }\n               else\n               {\n                 tc = vec3(-1.0, 1.0, 1.0) * tc;\n               }\n               gl_FragData[0] = textureCube(sbtexture, tc);\n             }","")),"background"===t.lastFormat&&t.tris.setProgram(t._openGLRenderWindow.getShaderCache().readyShaderProgramArray("//VTK::System::Dec\n             attribute vec3 vertexMC;\n             uniform mat4 IMCPCMatrix;\n             varying vec2 TexCoords;\n             void main () {\n              gl_Position = vec4(vertexMC.xyz, 1.0);\n              vec4 wpos = IMCPCMatrix * gl_Position;\n              TexCoords = vec2(vertexMC.x, vertexMC.y)*0.5 + 0.5;\n             }","//VTK::System::Dec\n             //VTK::Output::Dec\n             varying vec2 TexCoords;\n             uniform sampler2D sbtexture;\n             void main () {\n               gl_FragData[0] = texture2D(sbtexture, TexCoords);\n             }","")),t.tris.getShaderSourceTime().modified(),t.tris.getVAO().bind(),t.tris.getVAO().addAttributeArray(t.tris.getProgram(),t.tris.getCABO(),"vertexMC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||_p("Error setting vertexMC in shader VAO."));const e=t.renderable.getTextures();e.length||_p("vtkSkybox requires a texture map"),t.openGLTexture.getRenderable()!==e[0]&&(t.openGLTexture.releaseGraphicsResources(t._openGLRenderWindow),t.openGLTexture.setRenderable(e[0]))}}const kp={context:null};const Gp=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,kp,n),Zt.extend(e,t,n),t.openGLTexture=Ud.newInstance(),t.tris=Sd.newInstance(),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={normalMatrix:pe(new Float64Array(9)),mcwc:g(new Float64Array(16))},At(e,t,["context"]),bt(e,t,["activeTextures"]),Fp(e,t)}));rn("vtkSkybox",Gp);const{FieldAssociations:Up}=ol;function zp(e,t){t.classHierarchy.push("vtkHardwareSelector"),e.getSourceDataAsync=async(e,t,n,r,o)=>{},e.selectAsync=async(t,n,r,o,a)=>{const i=await e.getSourceDataAsync(t,n,r,o,a);return i?i.generateSelection(n,r,o,a):[]}}const Wp={fieldAssociation:Up.FIELD_ASSOCIATION_CELLS,captureZValues:!1};function Hp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wp,n),jt.obj(e,t),jt.setGet(e,t,["fieldAssociation","captureZValues"]),zp(e,t)}var jp={newInstance:jt.newInstance(Hp,"vtkHardwareSelector"),extend:Hp};function Kp(e,t){t.classHierarchy.push("vtkFramebuffer"),e.getBothMode=()=>t.context.FRAMEBUFFER,e.saveCurrentBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.saveCurrentBindings(n),e.saveCurrentBuffers(n)},e.saveCurrentBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling saveCurrentBindings");const n=t.context;t.previousDrawBinding=n.getParameter(t.context.FRAMEBUFFER_BINDING),t.previousActiveFramebuffer=t._openGLRenderWindow.getActiveFramebuffer()},e.saveCurrentBuffers=e=>{},e.restorePreviousBindingsAndBuffers=t=>{const n=void 0!==t?t:e.getBothMode();e.restorePreviousBindings(n),e.restorePreviousBuffers(n)},e.restorePreviousBindings=e=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling restorePreviousBindings");const n=t.context;n.bindFramebuffer(n.FRAMEBUFFER,t.previousDrawBinding),t._openGLRenderWindow.setActiveFramebuffer(t.previousActiveFramebuffer)},e.restorePreviousBuffers=e=>{},e.bind=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,r=n;null===r&&(r=t.context.FRAMEBUFFER),t.context.bindFramebuffer(r,t.glFramebuffer);for(let e=0;e<t.colorBuffers.length;e++)t.colorBuffers[e].bind();t._openGLRenderWindow.setActiveFramebuffer(e)},e.create=(e,n)=>{t.context?(t.glFramebuffer=t.context.createFramebuffer(),t.glFramebuffer.width=e,t.glFramebuffer.height=n):et("you must set the OpenGLRenderWindow before calling create")},e.setColorBuffer=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const r=t.context;if(!r)return void et("you must set the OpenGLRenderWindow before calling setColorBuffer");let o=r.COLOR_ATTACHMENT0;if(n>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");o+=n}t.colorBuffers[n]=e,r.framebufferTexture2D(r.FRAMEBUFFER,o,r.TEXTURE_2D,e.getHandle(),0)},e.removeColorBuffer=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;const n=t.context;if(!n)return void et("you must set the OpenGLRenderWindow before calling removeColorBuffer");let r=n.COLOR_ATTACHMENT0;if(e>0){if(!t._openGLRenderWindow.getWebgl2())return void et("Using multiple framebuffer attachments requires WebGL 2");r+=e}n.framebufferTexture2D(n.FRAMEBUFFER,r,n.TEXTURE_2D,null,0),t.colorBuffers=t.colorBuffers.splice(e,1)},e.setDepthBuffer=e=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const n=t.context;n.framebufferTexture2D(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.TEXTURE_2D,e.getHandle(),0)}else et("Attaching depth buffer textures to fbo requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling setDepthBuffer")},e.removeDepthBuffer=()=>{if(t.context)if(t._openGLRenderWindow.getWebgl2()){const e=t.context;e.framebufferTexture2D(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.TEXTURE_2D,null,0)}else et("Attaching depth buffer textures to framebuffers requires WebGL 2");else et("you must set the OpenGLRenderWindow before calling removeDepthBuffer")},e.getGLFramebuffer=()=>t.glFramebuffer,e.setOpenGLRenderWindow=n=>{t._openGLRenderWindow!==n&&(e.releaseGraphicsResources(),t._openGLRenderWindow=n,t.context=null,n&&(t.context=t._openGLRenderWindow.getContext()))},e.releaseGraphicsResources=()=>{t.glFramebuffer&&t.context.deleteFramebuffer(t.glFramebuffer)},e.getSize=()=>null==t.glFramebuffer?null:[t.glFramebuffer.width,t.glFramebuffer.height],e.populateFramebuffer=()=>{if(!t.context)return void et("you must set the OpenGLRenderWindow before calling populateFrameBuffer");e.bind();const n=t.context,r=Ud.newInstance();r.setOpenGLRenderWindow(t._openGLRenderWindow),r.setMinificationFilter(Id.LINEAR),r.setMagnificationFilter(Id.LINEAR),r.create2DFromRaw(t.glFramebuffer.width,t.glFramebuffer.height,4,ys.UNSIGNED_CHAR,null),e.setColorBuffer(r),t.depthTexture=n.createRenderbuffer(),n.bindRenderbuffer(n.RENDERBUFFER,t.depthTexture),n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t.glFramebuffer.width,t.glFramebuffer.height),n.framebufferRenderbuffer(n.FRAMEBUFFER,n.DEPTH_ATTACHMENT,n.RENDERBUFFER,t.depthTexture)},e.getColorTexture=()=>t.colorBuffers[0]}const $p={glFramebuffer:null,colorBuffers:null,depthTexture:null,previousDrawBinding:0,previousReadBinding:0,previousDrawBuffer:0,previousReadBuffer:0,previousActiveFramebuffer:null};function qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,$p,n),yt(e,t),t.colorBuffers&&et("you cannot initialize colorBuffers through the constructor. You should call setColorBuffer() instead."),t.colorBuffers=[],It(e,t,["colorBuffers"]),Kp(e,t)}var Xp={newInstance:Dt(qp,"vtkFramebuffer"),extend:qp};function Yp(e,t){t.classHierarchy.push("vtkSelectionNode"),e.getBounds=()=>t.points.getBounds()}const Zp={contentType:-1,fieldType:-1,properties:null,selectionList:[]};function Qp(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zp,n),jt.obj(e,t),t.properties={},jt.setGet(e,t,["contentType","fieldType","properties","selectionList"]),Yp(e,t)}var Jp={newInstance:jt.newInstance(Qp,"vtkSelectionNode"),extend:Qp,SelectionContent:{GLOBALIDS:0,PEDIGREEIDS:1,VALUES:2,INDICES:3,FRUSTUM:4,LOCATIONS:5,THRESHOLDS:6,BLOCKS:7,QUERY:8},SelectionField:{CELL:0,POINT:1,FIELD:2,VERTEX:3,EDGE:4,ROW:5}};const{PassTypes:ef}=Hl,{SelectionContent:tf,SelectionField:nf}=Jp,{FieldAssociations:rf}=ol,{vtkErrorMacro:of}=jt;function af(e){return`${e.propID} ${e.compositeID}`}function sf(e,t,n,r){return n?n[4*(t*(r[2]-r[0]+1)+e)+3]:0}function lf(e,t,n,r){if(!n)return 0;const o=4*(t*(r[2]-r[0]+1)+e),a=n[o],i=n[o+1];return 256*(256*n[o+2]+i)+a}function cf(e,t){let n=t;return n<<=24,n|=e,n}function uf(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<e.area[0]||t[0]>e.area[2]||t[1]<e.area[1]||t[1]>e.area[3])return null;const n=[t[0]-e.area[0],t[1]-e.area[1]],o=lf(n[0],n[1],e.pixBuffer[ef.ACTOR_PASS],e.area);if(o<=0||o-1>=e.props.length)return null;const a={valid:!0};a.propID=o-1,a.prop=e.props[a.propID];let i=lf(n[0],n[1],e.pixBuffer[ef.COMPOSITE_INDEX_PASS],e.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,e.captureZValues){const r=4*(n[1]*(e.area[2]-e.area[0]+1)+n[0]);a.zValue=(256*e.zBuffer[r]+e.zBuffer[r+1])/65535,a.displayPosition=t}if(e.pixBuffer[ef.ID_LOW24]&&0===sf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area))return a;const s=lf(n[0],n[1],e.pixBuffer[ef.ID_LOW24],e.area),l=lf(n[0],n[1],e.pixBuffer[ef.ID_HIGH24],e.area);return a.attributeID=cf(s,l),a}const a=[t[0],t[1]],i=[0,0];let s=uf(e,t,0,r);if(s&&s.valid)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[0]=a[0]+t,s=uf(e,i,0,r),s&&s.valid)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=uf(e,i,0,r),s&&s.valid))return s;if(i[1]=a[1]+t,s=uf(e,i,0,r),s&&s.valid)return s}}return r[0]=t[0],r[1]=t[1],null}function df(e,t,n,r,o){const a=[];let i=0;return t.forEach(((t,s)=>{const l=Jp.newInstance();switch(l.setContentType(tf.INDICES),e){case rf.FIELD_ASSOCIATION_CELLS:l.setFieldType(nf.CELL);break;case rf.FIELD_ASSOCIATION_POINTS:l.setFieldType(nf.POINT);break;default:of("Unknown field association")}l.getProperties().propID=t.info.propID,l.getProperties().prop=t.info.prop,l.getProperties().compositeID=t.info.compositeID,l.getProperties().attributeID=t.info.attributeID,l.getProperties().pixelCount=t.pixelCount,n&&(l.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],l.getProperties().worldPosition=o.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,r)),l.setSelectionList(t.attributeIDs),a[i]=l,i++})),a}function pf(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=uf(e,[n,t],0,u);if(r&&r.valid){const t=af(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return df(e.fieldAssociation,c,e.captureZValues,e.renderer,e.openGLRenderWindow)}function ff(e,t){t.classHierarchy.push("vtkOpenGLHardwareSelector"),e.releasePixBuffers=()=>{t.rawPixBuffer=[],t.pixBuffer=[],t.zBuffer=null},e.beginSelection=()=>{t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t.maxAttributeId=0;const n=t._openGLRenderWindow.getSize();if(t.framebuffer){t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers();const e=t.framebuffer.getSize();e&&e[0]===n[0]&&e[1]===n[1]?t.framebuffer.bind():(t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer())}else t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.create(n[0],n[1]),t.framebuffer.populateFramebuffer();if(t._openGLRenderer.clear(),t._openGLRenderer.setSelector(e),t.hitProps={},t.propPixels={},t.props=[],e.releasePixBuffers(),t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS){const e=t._openGLRenderWindow.getContext(),n=e.isEnabled(e.BLEND);e.disable(e.BLEND),t._openGLRenderWindow.traverseAllPasses(),n&&e.enable(e.BLEND)}},e.endSelection=()=>{t.hitProps={},t._openGLRenderer.setSelector(null),t.framebuffer.restorePreviousBindingsAndBuffers()},e.preCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending=e.isEnabled(e.BLEND),e.disable(e.BLEND)},e.postCapturePass=()=>{const e=t._openGLRenderWindow.getContext();t.originalBlending&&e.enable(e.BLEND)},e.select=()=>{let n=null;return e.captureBuffers()&&(n=e.generateSelection(t.area[0],t.area[1],t.area[2],t.area[3]),e.releasePixBuffers()),n},e.getSourceDataAsync=async(n,r,o,a,i)=>{if(t._renderer=n,void 0===r){const n=t._openGLRenderWindow.getSize();e.setArea(0,0,n[0]-1,n[1]-1)}else e.setArea(r,o,a,i);if(!e.captureBuffers())return!1;const s={area:[...t.area],pixBuffer:[...t.pixBuffer],captureZValues:t.captureZValues,zBuffer:t.zBuffer,props:[...t.props],fieldAssociation:t.fieldAssociation,renderer:n,openGLRenderWindow:t._openGLRenderWindow,generateSelection:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return pf(s,...t)}};return s},e.captureBuffers=()=>{if(!t._renderer||!t._openGLRenderWindow)return of("Renderer and view must be set before calling Select."),!1;t._openGLRenderer=t._openGLRenderWindow.getViewNodeFor(t._renderer),t._openGLRenderWindow.getRenderable().preRender(),e.invokeEvent({type:"StartEvent"}),t.originalBackground=t._renderer.getBackgroundByReference(),t._renderer.setBackground(0,0,0,0);const n=t._openGLRenderWindow.getRenderPasses();for(e.beginSelection(),t.currentPass=ef.MIN_KNOWN_PASS;t.currentPass<=ef.MAX_KNOWN_PASS;t.currentPass++)e.passRequired(t.currentPass)&&(e.preCapturePass(t.currentPass),t.captureZValues&&t.currentPass===ef.ACTOR_PASS&&"function"==typeof n[0].requestDepth&&"function"==typeof n[0].getFramebuffer?(n[0].requestDepth(),t._openGLRenderWindow.traverseAllPasses()):t._openGLRenderWindow.traverseAllPasses(),e.postCapturePass(t.currentPass),e.savePixelBuffer(t.currentPass),e.processPixelBuffers());return e.endSelection(),t._renderer.setBackground(t.originalBackground),e.invokeEvent({type:"EndEvent"}),!0},e.processPixelBuffers=()=>{t.props.forEach(((n,r)=>{e.isPropHit(r)&&n.processSelectorPixelBuffers(e,t.propPixels[r])}))},e.passRequired=e=>{if(e===ef.ID_HIGH24){if(t.fieldAssociation===rf.FIELD_ASSOCIATION_POINTS)return t.maximumPointId>16777215;if(t.fieldAssociation===rf.FIELD_ASSOCIATION_CELLS)return t.maximumCellId>16777215}return!0},e.savePixelBuffer=n=>{if(t.pixBuffer[n]=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),!t.rawPixBuffer[n]){const e=(t.area[2]-t.area[0]+1)*(t.area[3]-t.area[1]+1)*4;t.rawPixBuffer[n]=new Uint8Array(e),t.rawPixBuffer[n].set(t.pixBuffer[n])}if(n===ef.ACTOR_PASS){if(t.captureZValues){const e=t._openGLRenderWindow.getRenderPasses();if("function"==typeof e[0].requestDepth&&"function"==typeof e[0].getFramebuffer){const n=e[0].getFramebuffer();n.saveCurrentBindingsAndBuffers(),n.bind(),t.zBuffer=t._openGLRenderWindow.getPixelData(t.area[0],t.area[1],t.area[2],t.area[3]),n.restorePreviousBindingsAndBuffers()}}e.buildPropHitList(t.rawPixBuffer[n])}},e.buildPropHitList=e=>{let n=0;for(let r=0;r<=t.area[3]-t.area[1];r++)for(let o=0;o<=t.area[2]-t.area[0];o++){let a=lf(o,r,e,t.area);a>0&&(a--,a in t.hitProps||(t.hitProps[a]=!0,t.propPixels[a]=[]),t.propPixels[a].push(4*n)),++n}},e.renderProp=n=>{t.currentPass===ef.ACTOR_PASS&&(e.setPropColorValueFromInt(t.props.length+1),t.props.push(n))},e.renderCompositeIndex=n=>{t.currentPass===ef.COMPOSITE_INDEX_PASS&&e.setPropColorValueFromInt(n+1)},e.renderAttributeId=e=>{e<0||(t.maxAttributeId=e>t.maxAttributeId?e:t.maxAttributeId)},e.passTypeToString=e=>jt.enumToString(ef,e),e.isPropHit=e=>Boolean(t.hitProps[e]),e.setPropColorValueFromInt=e=>{t.propColorValue[0]=e%256/255,t.propColorValue[1]=Math.floor(e/256)%256/255,t.propColorValue[2]=Math.floor(e/65536)%256/255},e.getPixelInformation=(n,r,o)=>{const a=r<0?0:r;if(0===a){if(o[0]=n[0],o[1]=n[1],n[0]<t.area[0]||n[0]>t.area[2]||n[1]<t.area[1]||n[1]>t.area[3])return null;const e=[n[0]-t.area[0],n[1]-t.area[1]],r=lf(e[0],e[1],t.pixBuffer[ef.ACTOR_PASS],t.area);if(r<=0||r-1>=t.props.length)return null;const a={valid:!0};a.propID=r-1,a.prop=t.props[a.propID];let i=lf(e[0],e[1],t.pixBuffer[ef.COMPOSITE_INDEX_PASS],t.area);if((i<0||i>16777215)&&(i=0),a.compositeID=i-1,t.captureZValues){const r=4*(e[1]*(t.area[2]-t.area[0]+1)+e[0]);a.zValue=(256*t.zBuffer[r]+t.zBuffer[r+1])/65535,a.displayPosition=n}if(t.pixBuffer[ef.ID_LOW24]&&0===sf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area))return a;const s=lf(e[0],e[1],t.pixBuffer[ef.ID_LOW24],t.area),l=lf(e[0],e[1],t.pixBuffer[ef.ID_HIGH24],t.area);return a.attributeID=cf(s,l),a}const i=[n[0],n[1]],s=[0,0];let l=e.getPixelInformation(n,0,o);if(l&&l.valid)return l;for(let t=1;t<a;++t){for(let n=i[1]>t?i[1]-t:0;n<=i[1]+t;++n){if(s[1]=n,i[0]>=t&&(s[0]=i[0]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[0]=i[0]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}for(let n=i[0]>=t?i[0]-(t-1):0;n<=i[0]+(t-1);++n){if(s[0]=n,i[1]>=t&&(s[1]=i[1]-t,l=e.getPixelInformation(s,0,o),l&&l.valid))return l;if(s[1]=i[1]+t,l=e.getPixelInformation(s,0,o),l&&l.valid)return l}}return o[0]=n[0],o[1]=n[1],null},e.generateSelection=(n,r,o,a)=>{const i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=Math.floor(a),u=new Map,d=[0,0];for(let n=s;n<=c;n++)for(let r=i;r<=l;r++){const o=[r,n],a=e.getPixelInformation(o,0,d);if(a&&a.valid){const e=af(a);if(u.has(e)){const n=u.get(e);n.pixelCount++,t.captureZValues&&a.zValue<n.info.zValue&&(n.info=a),-1===n.attributeIDs.indexOf(a.attributeID)&&n.attributeIDs.push(a.attributeID)}else u.set(e,{info:a,pixelCount:1,attributeIDs:[a.attributeID]})}}return df(t.fieldAssociation,u,t.captureZValues,t._renderer,t._openGLRenderWindow)},e.getRawPixelBuffer=e=>t.rawPixBuffer[e],e.getPixelBuffer=e=>t.pixBuffer[e],e.attach=(e,n)=>{t._openGLRenderWindow=e,t._renderer=n};const n=e.setArea;e.setArea=function(){return!!n(...arguments)&&(t.area[0]=Math.floor(t.area[0]),t.area[1]=Math.floor(t.area[1]),t.area[2]=Math.floor(t.area[2]),t.area[3]=Math.floor(t.area[3]),!0)}}const gf={area:void 0,currentPass:-1,propColorValue:null,props:null,maximumPointId:0,maximumCellId:0,idOffset:1};function mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gf,n),jp.extend(e,t,n),t.propColorValue=[0,0,0],t.props=[],t.area||(t.area=[0,0,0,0]),jt.setGetArray(e,t,["area"],4),jt.setGet(e,t,["_renderer","currentPass","_openGLRenderWindow","maximumPointId","maximumCellId"]),jt.setGetArray(e,t,["propColorValue"],3),jt.moveToProtected(e,t,["renderer","openGLRenderWindow"]),jt.event(e,t,"event"),ff(e,t)}var hf={newInstance:jt.newInstance(mf,"vtkOpenGLHardwareSelector"),extend:mf,...Hl};const{vtkErrorMacro:vf}=Kt,{Representation:yf}=ds,{ObjectType:Tf}=Ju,{PassTypes:bf}=hf,xf={type:"StartEvent"},Cf={type:"EndEvent"};function Sf(e,t){t.classHierarchy.push("vtkOpenGLGlyph3DMapper");const n={...e};e.renderPiece=(n,r)=>{if(e.invokeEvent(xf),t.renderable.getStatic()||t.renderable.update(),t.currentInput=t.renderable.getInputData(1),e.invokeEvent(Cf),!t.currentInput)return void vf("No input!");if(!t.currentInput.getPoints||!t.currentInput.getPoints().getNumberOfValues())return;const o=t.context;t._openGLRenderWindow.getWebgl2()?(t.hardwareSupport=!0,t.extension=null):t.extension||(t.extension=t.context.getExtension("ANGLE_instanced_arrays"),t.hardwareSupport=!!t.extension);const a=r.getProperty().getBackfaceCulling(),i=r.getProperty().getFrontfaceCulling();a||i?i?(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.FRONT)):(t._openGLRenderWindow.enableCullFace(),o.cullFace(o.BACK)):t._openGLRenderWindow.disableCullFace(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)},e.multiply4x4WithOffset=(e,t,n,r)=>{const o=t[0],a=t[1],i=t[2],s=t[3],l=t[4],c=t[5],u=t[6],d=t[7],p=t[8],f=t[9],g=t[10],m=t[11],h=t[12],v=t[13],y=t[14],T=t[15];let b=n[r],x=n[r+1],C=n[r+2],S=n[r+3];e[0]=b*o+x*l+C*p+S*h,e[1]=b*a+x*c+C*f+S*v,e[2]=b*i+x*u+C*g+S*y,e[3]=b*s+x*d+C*m+S*T,b=n[r+4],x=n[r+5],C=n[r+6],S=n[r+7],e[4]=b*o+x*l+C*p+S*h,e[5]=b*a+x*c+C*f+S*v,e[6]=b*i+x*u+C*g+S*y,e[7]=b*s+x*d+C*m+S*T,b=n[r+8],x=n[r+9],C=n[r+10],S=n[r+11],e[8]=b*o+x*l+C*p+S*h,e[9]=b*a+x*c+C*f+S*v,e[10]=b*i+x*u+C*g+S*y,e[11]=b*s+x*d+C*m+S*T,b=n[r+12],x=n[r+13],C=n[r+14],S=n[r+15],e[12]=b*o+x*l+C*p+S*h,e[13]=b*a+x*c+C*f+S*v,e[14]=b*i+x*u+C*g+S*y,e[15]=b*s+x*d+C*m+S*T},e.replaceShaderNormal=(e,r,o)=>{if(t.hardwareSupport&&t.lastBoundBO.getReferenceByName("lastLightComplexity")>0){let n=e.Vertex;t.lastBoundBO.getCABO().getNormalOffset()&&(n=gd.substitute(n,"//VTK::Normal::Dec",["attribute vec3 normalMC;","attribute mat3 gNormal;","uniform mat3 normalMatrix;","varying vec3 normalVCVSOutput;"]).result,n=gd.substitute(n,"//VTK::Normal::Impl",["normalVCVSOutput = normalMatrix * gNormal * 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vertexColorVSOutput;"]).result,n=gd.substitute(n,"//VTK::Color::Impl",["vertexColorVSOutput = gColor;"]).result,r=gd.substitute(r,"//VTK::Color::Dec",["in vec4 vertexColorVSOutput[];","out vec4 vertexColorGSOutput;"]).result,r=gd.substitute(r,"//VTK::Color::Impl",["vertexColorGSOutput = vertexColorVSOutput[i];"]).result,s=s.concat(["  diffuseColor = vertexColorVSOutput.rgb;","  ambientColor = vertexColorVSOutput.rgb;","  opacity = opacity*vertexColorVSOutput.a;"])),o=gd.substitute(o,"//VTK::Color::Impl",s).result,o=gd.substitute(o,"//VTK::Color::Dec",i).result,e.Vertex=n,e.Geometry=r,e.Fragment=o}n.replaceShaderColor(e,r,o)},e.replaceShaderPositionVC=(e,r,o)=>{if(t.hardwareSupport){let n=e.Vertex;t.lastBoundBO.getReferenceByName("lastLightComplexity")>0?(n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","vertexVCVSOutput = MCVCMatrix * gVertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result,n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result):(n=gd.substitute(n,"//VTK::Camera::Dec",["attribute mat4 gMatrix;","uniform mat4 MCPCMatrix;"]).result,n=gd.substitute(n,"//VTK::PositionVC::Impl",["vec4 gVertexMC = gMatrix * vertexMC;","  gl_Position = MCPCMatrix * gVertexMC;"]).result),e.Vertex=n}n.replaceShaderPositionVC(e,r,o)},e.replaceShaderPicking=(e,r,o)=>{if(t.hardwareSupport){let t=e.Fragment,n=e.Vertex;n=gd.substitute(n,"//VTK::Picking::Dec",["attribute vec3 mapperIndexVS;","varying vec3 mapperIndexVSOutput;"]).result,n=gd.substitute(n,"//VTK::Picking::Impl","  mapperIndexVSOutput = mapperIndexVS;").result,e.Vertex=n,t=gd.substitute(t,"//VTK::Picking::Dec",["varying vec3 mapperIndexVSOutput;","uniform vec3 mapperIndex;","uniform int picking;"]).result,t=gd.substitute(t,"//VTK::Picking::Impl",["  vec4 pickColor = picking == 2 ? vec4(mapperIndexVSOutput,1.0) : vec4(mapperIndex,1.0);","  gl_FragData[0] = picking != 0 ? pickColor : gl_FragData[0];"]).result,e.Fragment=t}else n.replaceShaderPicking(e,r,o)},e.updateGlyphShaderParameters=(n,r,o,a,i,s,l,c)=>{const u=o.getProgram();if(n){const e=t.normalMatrix,n=s,r=9*l,o=t.tmpMat3,a=e[0],i=e[1],c=e[2],d=e[3],p=e[4],f=e[5],g=e[6],m=e[7],h=e[8],v=n[r],y=n[r+1],T=n[r+2],b=n[r+3],x=n[r+4],C=n[r+5],S=n[r+6],A=n[r+7],I=n[r+8];o[0]=v*a+y*d+T*g,o[1]=v*i+y*p+T*m,o[2]=v*c+y*f+T*h,o[3]=b*a+x*d+C*g,o[4]=b*i+x*p+C*m,o[5]=b*c+x*f+C*h,o[6]=S*a+A*d+I*g,o[7]=S*i+A*p+I*m,o[8]=S*c+A*f+I*h,u.setUniformMatrix3x3("normalMatrix",t.tmpMat3)}if(e.multiply4x4WithOffset(t.tmpMat4,t.mcpcMatrix,i,16*l),u.setUniformMatrix("MCPCMatrix",t.tmpMat4),r&&(e.multiply4x4WithOffset(t.tmpMat4,t.mcvcMatrix,i,16*l),u.setUniformMatrix("MCVCMatrix",t.tmpMat4)),a){const e=a.getData();t.tmpColor[0]=e[4*l]/255,t.tmpColor[1]=e[4*l+1]/255,t.tmpColor[2]=e[4*l+2]/255,u.setUniform3fArray("ambientColorUniform",t.tmpColor),u.setUniform3fArray("diffuseColorUniform",t.tmpColor)}c&&u.setUniform3fArray("mapperIndex",c.getPropColorValue())},e.renderPieceDraw=(n,r)=>{const o=r.getProperty().getRepresentation(),a=t.context,i=r.getProperty().getEdgeVisibility()&&o===yf.SURFACE,s=t.openGLCamera.getKeyMatrices(n),l=t.openGLActor.getKeyMatrices();ve(t.normalMatrix,s.normalMatrix,l.normalMatrix),T(t.mcpcMatrix,s.wcpc,l.mcwc),T(t.mcvcMatrix,s.wcvc,l.mcwc);const c=t.renderable.getMatrixArray(),u=t.renderable.getNormalArray(),d=t.renderable.getColorArray(),p=c.length/16;let f=!1;t._openGLRenderer.getSelector()&&t._openGLRenderer.getSelector().getCurrentPass()===bf.COMPOSITE_INDEX_PASS&&(f=!0);for(let s=t.primTypes.Start;s<t.primTypes.End;s++){const l=t.primitives[s].getCABO();if(l.getElementCount()){t.drawingEdges=i&&(s===t.primTypes.TrisEdges||s===t.primTypes.TriStripsEdges),t.lastBoundBO=t.primitives[s],t.primitives[s].updateShaders(n,r,e);const g=t.primitives[s].getProgram(),m=t.primitives[s].getOpenGLMode(o),h=g.isUniformUsed("normalMatrix"),v=g.isUniformUsed("MCVCMatrix");if(t.hardwareSupport)t.extension?t.extension.drawArraysInstancedANGLE(m,0,l.getElementCount(),p):a.drawArraysInstanced(m,0,l.getElementCount(),p);else for(let n=0;n<p;++n)f&&t._openGLRenderer.getSelector().renderCompositeIndex(n),e.updateGlyphShaderParameters(h,v,t.primitives[s],d,c,u,n,f?t._openGLRenderer.getSelector():null),a.drawArrays(m,0,l.getElementCount())}}},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.glyphBOBuildTime.getMTime()>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime()))return e.getProgram().isAttributeUsed("gMatrix")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.matrixBuffer,"gMatrix",0,64,t.context.FLOAT,4,!1,1)||vf("Error setting gMatrix in shader VAO."):e.getVAO().removeAttributeArray("gMatrix"),e.getProgram().isAttributeUsed("gNormal")?e.getVAO().addAttributeMatrixWithDivisor(e.getProgram(),t.normalBuffer,"gNormal",0,36,t.context.FLOAT,3,!1,1)||vf("Error setting gNormal in shader VAO."):e.getVAO().removeAttributeArray("gNormal"),e.getProgram().isAttributeUsed("gColor")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.colorBuffer,"gColor",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting gColor in shader VAO."):e.getVAO().removeAttributeArray("gColor"),e.getProgram().isAttributeUsed("mapperIndexVS")?e.getVAO().addAttributeArrayWithDivisor(e.getProgram(),t.pickBuffer,"mapperIndexVS",0,4,t.context.UNSIGNED_BYTE,4,!0,1,!1)||vf("Error setting mapperIndexVS in shader VAO."):e.getVAO().removeAttributeArray("mapperIndexVS"),n.setMapperShaderParameters(e,r,o),void e.getAttributeUpdateTime().modified();n.setMapperShaderParameters(e,r,o)},e.getNeedToRebuildBufferObjects=(e,r)=>(t.renderable.buildArrays(),t.VBOBuildTime.getMTime()<t.renderable.getBuildTime().getMTime()||n.getNeedToRebuildBufferObjects(e,r)),e.getNeedToRebuildShaders=(e,r,o)=>!!(n.getNeedToRebuildShaders(e,r,o)||e.getShaderSourceTime().getMTime()<t.renderable.getMTime()||e.getShaderSourceTime().getMTime()<t.currentInput.getMTime()),e.buildBufferObjects=(e,r)=>{if(t.hardwareSupport){const e=t.renderable.getMatrixArray(),n=t.renderable.getNormalArray(),r=t.renderable.getColorArray();if(t.matrixBuffer||(t.matrixBuffer=Ju.newInstance(),t.matrixBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.normalBuffer=Ju.newInstance(),t.normalBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorBuffer=Ju.newInstance(),t.colorBuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.pickBuffer=Ju.newInstance(),t.pickBuffer.setOpenGLRenderWindow(t._openGLRenderWindow)),t.renderable.getBuildTime().getMTime()>t.glyphBOBuildTime.getMTime()){t.matrixBuffer.upload(e,Tf.ARRAY_BUFFER),t.normalBuffer.upload(n,Tf.ARRAY_BUFFER),r?t.colorBuffer.upload(r.getData(),Tf.ARRAY_BUFFER):t.colorBuffer.releaseGraphicsResources();const o=e.length/16,a=new Uint8Array(4*o);for(let e=0;e<o;++e){let t=e+1;const n=4*e;a[n]=t%256,t-=a[n],t/=256,a[n+1]=t%256,t-=a[n+1],t/=256,a[n+2]=t%256,a[n+3]=255}t.pickBuffer.upload(a,Tf.ARRAY_BUFFER),t.glyphBOBuildTime.modified()}}return n.buildBufferObjects(e,r)}}const Af={normalMatrix:null,mcpcMatrix:null,mcwcMatrix:null};const If=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Af,n),sp.extend(e,t,n),t.tmpMat3=pe(new Float64Array(9)),t.normalMatrix=pe(new Float64Array(9)),t.mcpcMatrix=g(new Float64Array(16)),t.mcvcMatrix=g(new Float64Array(16)),t.tmpColor=[],t.glyphBOBuildTime={},yt(t.glyphBOBuildTime,{mtime:0}),Sf(e,t)}),"vtkOpenGLGlyph3DMapper");rn("vtkGlyph3DMapper",If);const{vtkErrorMacro:wf}=jt;class Pf{constructor(){this.segmentMapping={},this.segments=[null],this.faces=[]}addSegment(e){const t=e[0],n=e[e.length-1];if(t===n||e.length<2)return;const r=this.segmentMapping[t],o=this.segmentMapping[n];if(void 0!==r&&void 0!==o)if(Math.abs(r)===Math.abs(o)){const a=r<o?o:r,i=this.segments[a];if(r>0)for(let t=1;t<e.length-1;t++)i.push(e[t]);else for(let t=1;t<e.length-1;t++)i.unshift(e[e.length-1-t]);this.faces.push(i),this.segments[a]=null,this.segmentMapping[t]=void 0,this.segmentMapping[n]=void 0}else{const t=Math.abs(r),n=Math.abs(o),a=this.segments[t],i=this.segments[n];this.segments[t]=null,this.segments[n]=null,this.segmentMapping[a[0]]=void 0,this.segmentMapping[i[0]]=void 0,this.segmentMapping[a[a.length-1]]=void 0,this.segmentMapping[i[i.length-1]]=void 0,this.addSegment(e),this.addSegment(a),this.addSegment(i)}else if(void 0!==r){if(r>0){const t=this.segments[r];for(let n=1;n<e.length;n++)t.push(e[n]);this.segmentMapping[n]=r}else{const t=this.segments[-r];this.segmentMapping[n]=r;for(let n=1;n<e.length;n++)t.unshift(e[n])}this.segmentMapping[t]=void 0}else if(void 0!==o){if(o>0){const n=this.segments[o];for(let t=1;t<e.length;t++)n.push(e[e.length-1-t]);this.segmentMapping[t]=o}else{const n=this.segments[-o];this.segmentMapping[t]=o;for(let t=1;t<e.length;t++)n.unshift(e[e.length-t-1])}this.segmentMapping[n]=void 0}else{const r=this.segments.length;this.segments.push(e),this.segmentMapping[t]=-r,this.segmentMapping[n]=r}}}function Of(e,t){t.classHierarchy.push("vtkClosedPolyLineToSurfaceFilter"),e.requestData=(e,t)=>{const n=e[0];if(!n)return void wf("Invalid or missing input");const r=yu.newInstance();r.shallowCopy(n);const o=new Pf,a=n.getLines().getData();let i=0;for(;i<a.length;){const e=a[i++],t=[];for(let n=0;n<e;n++)t.push(a[i+n]);o.addSegment(t),i+=e}const{faces:s}=o;let l=s.length;for(let e=0;e<s.length;e++)l+=s[e].length;const c=new Uint16Array(l);i=0;for(let e=0;e<s.length;e++){const t=s[e];c[i++]=t.length;for(let e=0;e<t.length;e++)c[i++]=t[e]}r.setPolys(fc.newInstance({values:c,name:"faces"})),t[0]=r}}const Rf={};function Mf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rf,n),jt.obj(e,t),jt.algo(e,t,1,1),Of(e,t)}var Ef={newInstance:jt.newInstance(Mf,"vtkClosedPolyLineToSurfaceFilter"),extend:Mf};const{vtkErrorMacro:Df}=Kt;function Vf(e,t){t.classHierarchy.push("vtkCutter");const n={...e};e.getMTime=()=>{let e=n.getMTime();return t.cutFunction?(e=Math.max(e,t.cutFunction.getMTime()),e):e},e.requestData=(e,n)=>{const r=e[0];if(!r)return void Df("Invalid or missing input");if(!t.cutFunction)return void Df("Missing cut function");const o=yu.newInstance();(function(e,n){const r=e.getPoints(),o=r.getData(),a=r.getNumberOfPoints(),i=[],s=[],l=[];(!t.cutScalars||t.cutScalars.length<a)&&(t.cutScalars=new Float32Array(a));let c=0,u=0;for(;c<o.length;)t.cutScalars[u++]=t.cutFunction.evaluateFunction(o[c++],o[c++],o[c++]);const d=[],p=new Array(3),f=new Array(3),g=[];for(const n=function(e){const t=e.getPolys().getData(),n=e.getStrips().getData(),r={cellSize:0,cell:[],done:!1,polyIdx:0,stripIdx:0,remainingStripLength:0,next(){if(r.polyIdx<t.length){r.cellSize=t[r.polyIdx];const e=r.polyIdx+1,n=e+r.cellSize;r.polyIdx=n;let o=0;for(let a=e;a<n;++a)r.cell[o++]=t[a]}else if(r.stripIdx<n.length){r.cellSize=3,0===r.remainingStripLength&&(r.remainingStripLength=n[r.stripIdx]-2,r.stripIdx+=3);const e=r.stripIdx-2,t=r.stripIdx+1;r.stripIdx++,r.remainingStripLength--;let o=0;for(let a=e;a<t;++a)r.cell[o++]=n[a]}else{if(r.done)throw new Error("Iterator is done");r.done=!0}}};return r.next(),r}(e);!n.done;n.next()){if(n.cellSize<=2)continue;for(let e=0;e<n.cellSize;)g[e]=t.cutScalars[n.cell[e++]];const e=g[0]>0;let r=!0;for(let t=1;t<n.cell.length;t++)if(g[t]>0!==e){r=!1;break}if(r)continue;const a=[];for(let e=0;e<n.cellSize;e++){const r=e+1===n.cellSize?0:e+1,i=g[e]>0;if(g[r]>0===i)continue;let s=e,l=r,c=g[l]-g[s];c<=0&&(s=r,l=e,c*=-1);let u=0;0!==c&&(u=(t.cutValue-g[s])/c);const d=n.cell[s],m=n.cell[l];p[0]=o[3*d],p[1]=o[3*d+1],p[2]=o[3*d+2],f[0]=o[3*m],f[1]=o[3*m+1],f[2]=o[3*m+2];const h=[p[0]+u*(f[0]-p[0]),p[1]+u*(f[1]-p[1]),p[2]+u*(f[2]-p[2])];a.push({pointEdge1:d,pointEdge2:m,intersectedPoint:h,newPointID:-1})}for(let e=0;e<a.length;e++){const t=a[e];let n=!1;for(let r=0;r<d.length;r++){const o=d[r],i=t.pointEdge1===o.pointEdge1&&t.pointEdge2===o.pointEdge2,s=t.intersectedPoint[0]===o.intersectedPoint[0]&&t.intersectedPoint[1]===o.intersectedPoint[1]&&t.intersectedPoint[2]===o.intersectedPoint[2];if(i||s){n=!0,a[e].newPointID=d[r].newPointID;break}}n||(i.push(t.intersectedPoint[0]),i.push(t.intersectedPoint[1]),i.push(t.intersectedPoint[2]),a[e].newPointID=i.length/3-1,d.push(a[e]))}const c=a.length;2===c?s.push(c,a[0].newPointID,a[1].newPointID):c>2&&(l.push(c),a.forEach((e=>{l.push(e.newPointID)})))}n.getPoints().setData(it(r.getDataType(),i),3),0!==s.length&&n.getLines().setData(Uint16Array.from(s)),0!==l.length&&n.getPolys().setData(Uint16Array.from(l))})(r,o),n[0]=o}}const Lf={cutFunction:null,cutScalars:null,cutValue:0};function Bf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Lf,n),yt(e,t),Rt(e,t,1,1),At(e,t,["cutFunction","cutValue"]),Vf(e,t)}var Nf={newInstance:Dt(Bf,"vtkCutter"),extend:Bf};const _f=e=>e,Ff=1e-6;class kf{constructor(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.matrix=g(new Float64Array(16)),this.tmp=new Float64Array(3),this.angleConv=e?l:_f}rotateFromDirections(e,t){const n=new Float64Array(3),r=new Float64Array(3),o=new Float64Array(16);In(n,e[0],e[1],e[2]),In(r,t[0],t[1],t[2]),Mn(n,n),Mn(r,r);const a=En(n,r);return a>=1||(Dn(this.tmp,n,r),Sn(this.tmp)<Ff&&(Dn(this.tmp,[1,0,0],e),Sn(this.tmp)<Ff&&Dn(this.tmp,[0,1,0],e)),O(o,Math.acos(a),this.tmp),T(this.matrix,this.matrix,o)),this}rotate(e,t){return In(this.tmp,...t),Mn(this.tmp,this.tmp),C(this.matrix,this.matrix,this.angleConv(e),this.tmp),this}rotateX(e){return S(this.matrix,this.matrix,this.angleConv(e)),this}rotateY(e){return A(this.matrix,this.matrix,this.angleConv(e)),this}rotateZ(e){return I(this.matrix,this.matrix,this.angleConv(e)),this}translate(e,t,n){return In(this.tmp,e,t,n),b(this.matrix,this.matrix,this.tmp),this}scale(e,t,n){return In(this.tmp,e,t,n),x(this.matrix,this.matrix,this.tmp),this}multiply(e){return T(this.matrix,this.matrix,e),this}multiply3x3(e){return T(this.matrix,this.matrix,[e[0],e[1],e[2],0,e[3],e[4],e[5],0,e[6],e[7],e[8],0,0,0,0,1]),this}invert(){return h(this.matrix,this.matrix),this}identity(){return g(this.matrix),this}apply(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:-1;if(aa(vo,this.matrix))return this;const r=-1===n?e.length:t+3*n;for(let n=t;n<r;n+=3)In(this.tmp,e[n],e[n+1],e[n+2]),Vn(this.tmp,this.tmp,this.matrix),e[n]=this.tmp[0],e[n+1]=this.tmp[1],e[n+2]=this.tmp[2];return this}getMatrix(){return this.matrix}setMatrix(e){return e&&16===e.length&&d(this.matrix,e),this}}var Gf=function(){return new kf(!0)},Uf=function(){return new kf(!1)};const zf=[2,0,1,2,2,3,2,4,5,2,6,7,2,0,2,2,1,3,2,4,6,2,5,7,2,0,4,2,1,5,2,2,6,2,3,7],Wf=[4,0,1,3,2,4,4,6,7,5,4,8,10,11,9,4,12,13,15,14,4,16,18,19,17,4,20,21,23,22];function Hf(e,t){t.classHierarchy.push("vtkCubeSource"),e.setBounds=function(){let t=[];if(Array.isArray(arguments.length<=0?void 0:arguments[0]))t=arguments.length<=0?void 0:arguments[0];else for(let e=0;e<arguments.length;e++)t.push(e<0||arguments.length<=e?void 0:arguments[e]);6===t.length&&(e.setXLength(t[1]-t[0]),e.setYLength(t[3]-t[2]),e.setZLength(t[5]-t[4]),e.setCenter([(t[0]+t[1])/2,(t[2]+t[3])/2,(t[4]+t[5])/2]))},e.requestData=function(e,n){if(t.deleted)return;const r=yu.newInstance();n[0]=r;const o=jt.newTypedArray(t.pointType,72);r.getPoints().setData(o,3);const a=jt.newTypedArray(t.pointType,72),i=Ds.newInstance({name:"Normals",values:a,numberOfComponents:3});r.getPointData().setNormals(i);let s=2;!0===t.generate3DTextureCoordinates&&(s=3);const l=jt.newTypedArray(t.pointType,24*s),c=Ds.newInstance({name:"TextureCoordinates",values:l,numberOfComponents:s});r.getPointData().setTCoords(c);const u=[0,0,0],d=[0,0,0],p=[0,0];let f=0;u[0]=-t.xLength/2,d[0]=-1,d[1]=0,d[2]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[0]=(u[2]+.5)*(1-2*e),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*e-1,l[f*s+1]=2*n-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[1]+=t.yLength}u[0]+=t.xLength,d[0]+=2}u[1]=-t.yLength/2,d[1]=-1,d[0]=0,d[2]=0;for(let e=0;e<2;e++){u[0]=-t.xLength/2;for(let n=0;n<2;n++){p[0]=(u[0]+.5)*(2*e-1),u[2]=-t.zLength/2;for(let r=0;r<2;r++)p[1]=-1*(u[2]+.5),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*n-1,l[f*s+1]=2*e-1,l[f*s+2]=2*r-1),f++,u[2]+=t.zLength;u[0]+=t.xLength}u[1]+=t.yLength,d[1]+=2}u[2]=-t.zLength/2,d[2]=-1,d[0]=0,d[1]=0;for(let e=0;e<2;e++){u[1]=-t.yLength/2;for(let n=0;n<2;n++){p[1]=u[1]+.5,u[0]=-t.xLength/2;for(let r=0;r<2;r++)p[0]=(u[0]+.5)*(2*e-1),o[3*f]=u[0],o[3*f+1]=u[1],o[3*f+2]=u[2],a[3*f]=d[0],a[3*f+1]=d[1],a[3*f+2]=d[2],2===s?(l[f*s]=p[0],l[f*s+1]=p[1]):(l[f*s]=2*r-1,l[f*s+1]=2*n-1,l[f*s+2]=2*e-1),f++,u[0]+=t.xLength;u[1]+=t.yLength}u[2]+=t.zLength,d[2]+=2}if(t.rotations&&Gf().rotateX(t.rotations[0]).rotateY(t.rotations[1]).rotateZ(t.rotations[2]).apply(o).apply(a),t.center&&Uf().translate(...t.center).apply(o),t.matrix){Uf().setMatrix(t.matrix).apply(o);const e=[t.matrix[0],t.matrix[1],t.matrix[2],0,t.matrix[4],t.matrix[5],t.matrix[6],0,t.matrix[8],t.matrix[9],t.matrix[10],0,0,0,0,1];Uf().setMatrix(e).apply(a)}t.generateFaces?r.getPolys().deepCopy(t._polys):r.getPolys().initialize(),t.generateLines?(r.getLines().deepCopy(t._lineCells),r.getPointData().setNormals(null)):r.getLines().initialize(),r.modified()}}const jf={xLength:1,yLength:1,zLength:1,pointType:"Float64Array",generate3DTextureCoordinates:!1,generateFaces:!0,generateLines:!1};function Kf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,jf,n),jt.obj(e,t),jt.setGet(e,t,["xLength","yLength","zLength","generate3DTextureCoordinates","generateFaces","generateLines"]),jt.setGetArray(e,t,["center","rotations"],3),jt.setGetArray(e,t,["matrix"],16),t._polys=fc.newInstance({values:Uint16Array.from(Wf)}),t._lineCells=fc.newInstance({values:Uint16Array.from(zf)}),jt.moveToProtected(e,t,["polys","lineCells"]),jt.algo(e,t,0,1),Hf(e,t)}var $f={newInstance:jt.newInstance(Kf,"vtkCubeSource"),extend:Kf};const{vtkErrorMacro:qf}=jt;function Xf(e,t){t.classHierarchy.push("vtkImageDataOutlineFilter");const n={...e};e.requestData=(e,n)=>{const r=e[0];if(!r||!r.isA("vtkImageData"))return void qf("Invalid or missing input");const o=r.getSpatialExtent();o?(t._cubeSource.setBounds(o),t._cubeSource.setMatrix(r.getIndexToWorld()),n[0]=t._cubeSource.getOutputData()):qf("Unable to fetch spatial extents of input image.")},e.getMTime=()=>Math.max(n.getMTime(),t._cubeSource.getMTime()),e.setGenerateFaces=t._cubeSource.setGenerateFaces,e.setGenerateLines=t._cubeSource.setGenerateLines,e.getGenerateFaces=t._cubeSource.getGenerateFaces,e.getGenerateLines=t._cubeSource.getGenerateLines}const Yf={};function Zf(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Yf,n),jt.obj(e,t),jt.algo(e,t,1,1),t._cubeSource=$f.newInstance(),jt.moveToProtected(e,t,["cubeSource","tmpOut"]),Xf(e,t)}var Qf={newInstance:jt.newInstance(Zf,"vtkImageDataOutlineFilter"),extend:Zf};let Jf;function eg(e,t){t.classHierarchy.push("vtkAbstractTransform","vtkHomogeneousTransform","vtkTransform"),e.transformPoint=(e,n)=>(Vn(n,e,t.matrix),n),e.transformPoints=(e,n)=>{const r=new Float64Array(3),o=new Float64Array(3);for(let a=0;a<e.length;a+=3)r[0]=e[a],r[1]=e[a+1],r[2]=e[a+2],Vn(o,r,t.matrix),n[a]=o[0],n[a+1]=o[1],n[a+2]=o[2];return n},e.preMultiply=()=>{e.setPreMultiplyFlag(!0)},e.postMultiply=()=>{e.setPreMultiplyFlag(!1)},e.transformMatrix=(e,n)=>(t.preMultiplyFlag?T(n,t.matrix,e):T(n,e,t.matrix),n),e.transformMatrices=(e,n)=>{const r=new Float64Array(16),o=new Float64Array(16),a=t.preMultiplyFlag?()=>T(o,t.matrix,r):()=>T(o,r,t.matrix);for(let t=0;t<e.length;t+=16){for(let n=0;n<16;++n)r[n]=e[t+n];a();for(let e=0;e<16;++e)n[t+e]=o[e]}return n},e.getInverse=()=>Jf({matrix:za.invertMatrix(Array.from(t.matrix),[],4),preMultiplyFlag:t.preMultiplyFlag})}const tg={preMultiplyFlag:!1,matrix:[...vo]};function ng(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tg,n),jt.obj(e,t),jt.setGet(e,t,["preMultiplyFlag"]),jt.setGetArray(e,t,["matrix"],16),eg(e,t)}Jf=jt.newInstance(ng,"vtkTransform");var rg={newInstance:Jf,extend:ng};const og={NEAREST:0,LINEAR:1};var ag={InterpolationType:og};const{vtkErrorMacro:ig}=Kt;function sg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function lg(e,t,n){return t.identity(n),e.reduce(((e,n,r)=>0===r?n?t.copy(e,n):t.identity(e):n?t.multiply(e,e,n):e),n)}function cg(e,t){function n(n){[t._scalars,t._colorTransferFunc,t._pwFunc].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageResliceMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t._openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderer.getRenderable();t._openGLCamera=t._openGLRenderer.getViewNodeFor(r.getActiveCamera());const o=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),o&&!o.isDeleted()&&o!==t._openGLRenderWindow&&n(o),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow)}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t._openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),t.currentInput?(e.updateResliceGeometry(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r),e.invokeEvent({type:"EndEvent"})):ig("No input!")},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===og.NEAREST?(t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.openGLTexture.setMinificationFilter(Id.LINEAR),t.openGLTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||t.VBOBuildTime.getMTime()<t.resliceGeom.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()?.getScalars();if(!a)return;const i=a.getNumberOfComponents();let s=`${o.getMTime()}A${a.getMTime()}`;const l=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(l?.oglObject?.getHandle()&&l?.hash===s)t.openGLTexture=l.oglObject;else{t.openGLTexture=Ud.newInstance(),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.openGLTexture.resetFormatAndType(),t.openGLTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.openGLTexture,s),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}const c=r.getProperty(),u=c.getIndependentComponents(),d=u?i:1,p=u?2*d:1,f=c.getRGBTransferFunction();s=sg(c,f,d);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===s)t.colorTexture=g.oglObject;else{const n=1024,r=new Uint8ClampedArray(n*p*3);if(t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=c.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,s),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const m=c.getPiecewiseFunction();s=sg(c,m,d);const h=t._openGLRenderWindow.getGraphicsResourceForObject(m);if(h?.oglObject?.getHandle()&&h?.hash===s)t.pwfTexture=h.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance(),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),m){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=c.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);m&&(t._openGLRenderWindow.setGraphicsResourceForObject(m,t.pwfTexture,s),m!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(m,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=m)}const v=`${t.resliceGeom.getMTime()}A${t.renderable.getSlabThickness()}`;if(!t.tris.getCABO().getElementCount()||t.VBOBuildString!==v){const e=Ds.newInstance({numberOfComponents:3,values:t.resliceGeom.getPoints().getData()});e.setName("points");const n=Ds.newInstance({numberOfComponents:1,values:t.resliceGeom.getPolys().getData()}),r={points:e,cellOffset:0};if(t.renderable.getSlabThickness()>0){const e=t.resliceGeom.getPointData().getNormals();e?r.normals=e:ig("Slab mode requested without normals")}t.tris.getCABO().createVBO(n,"polys",rs.SURFACE,r)}t.VBOBuildString=v,t.VBOBuildTime.modified()},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();if(n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())){a.isUniformUsed("texture1")&&a.setUniformi("texture1",t.openGLTexture.getTextureUnit()),a.isAttributeUsed("vertexWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexWC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting vertexWC in shader VAO.")),a.isAttributeUsed("normalWC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"normalWC",n.getCABO().getNormalOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||ig("Error setting normalWC in shader VAO.")),a.isUniformUsed("slabThickness")&&a.setUniformf("slabThickness",t.renderable.getSlabThickness()),a.isUniformUsed("spacing")&&a.setUniform3fv("spacing",t.currentInput.getSpacing()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabType")&&a.setUniformi("slabType",t.renderable.getSlabType()),a.isUniformUsed("slabTrapezoid")&&a.setUniformi("slabTrapezoid",t.renderable.getSlabTrapezoidIntegration());const e=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null;if(a.isUniformUsed("WCTCMatrix")){const n=t.currentInput,r=n.getDimensions();d(t.tmpMat4,n.getIndexToWorld()),b(t.tmpMat4,t.tmpMat4,[-.5,-.5,-.5]),x(t.tmpMat4,t.tmpMat4,r),h(t.tmpMat4,t.tmpMat4),e&&T(t.tmpMat4,t.tmpMat4,e),a.setUniformMatrix("WCTCMatrix",t.tmpMat4)}a.isUniformUsed("vboScaling")&&a.setUniform3fv("vboScaling",n.getCABO().getCoordScale()??[1,1,1]),n.getAttributeUpdateTime().modified()}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,o)=>{const a=t._openGLCamera.getKeyMatrices(n),i=t._openGLImageSlice.getKeyMatrices(),s=e.getCABO().getCoordShiftAndScaleEnabled()?e.getCABO().getInverseShiftAndScaleMatrix():null,l=e.getProgram();l.isUniformUsed("MCPCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCPCMatrix",lg([a.wcpc,i.mcwc,s],r,t.tmpMat4))),l.isUniformUsed("MCVCMatrix")&&(g(t.tmpMat4),l.setUniformMatrix("MCVCMatrix",lg([a.wcvc,i.mcwc,s],r,t.tmpMat4)))},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.openGLTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.openGLTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d),o.setUniform4fv("backgroundColor",t.renderable.getBackgroundColor())},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=t.renderable.getSlabThickness(),s=t.renderable.getSlabType(),l=t.renderable.getSlabTrapezoidIntegration();let c=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(c=!0),!(!c&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a&&t.lastSlabThickness===i&&t.lastSlabType===s&&t.lastSlabTrapezoidIntegration===l||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,t.lastSlabThickness=i,t.lastSlabType=s,t.lastSlabTrapezoidIntegration=l,0))},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\n// all variables that represent positions or directions have a suffix\n// indicating the coordinate system they are in. The possible values are\n// MC - Model coordinates\n// WC - World coordinates\n// VC - View coordinates\n// DC - Display coordinates\n// TC - Texture coordinates\n\n// frag position in VC\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvoid main()\n{\n  //VTK::PositionVC::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Picking::Impl\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkImageResliceMapperFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the gpu image mapper fragment shader\n\n// VC position of this fragment\n//VTK::PositionVC::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\n// picking support\n//VTK::Picking::Dec\n\n// handle coincident offsets\n//VTK::Coincident::Dec\n\n//VTK::ZBuffer::Dec\n\n// the output of this shader\n//VTK::Output::Dec\n\nvoid main()\n{\n  // VC position of this fragment. This should not branch/return/discard.\n  //VTK::PositionVC::Impl\n\n  // Place any calls that require uniform flow (e.g. dFdx) here.\n  //VTK::UniformFlow::Impl\n\n  // Set gl_FragDepth here (gl_FragCoord.z by default)\n  //VTK::Depth::Impl\n\n  // Early depth peeling abort:\n  //VTK::DepthPeeling::PreColor\n\n  //VTK::TCoord::Impl\n\n  if (gl_FragData[0].a <= 0.0)\n    {\n    discard;\n    }\n\n  //VTK::DepthPeeling::Impl\n\n  //VTK::Picking::Impl\n\n  // handle coincident offsets\n  //VTK::Coincident::Impl\n\n  //VTK::ZBuffer::Impl\n\n  //VTK::RenderPassFragmentShader::Impl\n}\n",e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{if(e.replaceShaderTCoord(n,r,o),e.replaceShaderPositionVC(n,r,o),t.haveSeenDepthRequest){let e=n.Fragment;e=gd.substitute(e,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,e=gd.substitute(e,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result,n.Fragment=e}e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderTCoord=(e,n,r)=>{let o=e.Vertex;const a=e.Geometry;let i=e.Fragment;const s=t.renderable.getSlabThickness();o=gd.substitute(o,"//VTK::TCoord::Dec",["uniform mat4 WCTCMatrix;","out vec3 fragTexCoord;"]).result,o=gd.substitute(o,"//VTK::TCoord::Impl",["fragTexCoord = (WCTCMatrix * vertexWC).xyz;"]).result;const l=t.openGLTexture.getComponents(),c=r.getProperty().getIndependentComponents();let u=["in vec3 fragTexCoord;","uniform highp sampler3D texture1;","uniform mat4 WCTCMatrix;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;"];if(c){for(let e=1;e<l;e++)u=u.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(l){case 1:u=u.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:u=u.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:u=u.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:ig("Unsupported number of independent coordinates.")}}s>0&&(u=u.concat(["uniform vec3 spacing;","uniform float slabThickness;","uniform int slabType;","uniform int slabTrapezoid;","uniform vec3 vboScaling;"]),u=u.concat(["vec4 compositeValue(vec4 currVal, vec4 valToComp, int trapezoid)","{","  vec4 retVal = vec4(1.0);","  if (slabType == 0) // min","  {","    retVal = min(currVal, valToComp);","  }","  else if (slabType == 1) // max","  {","    retVal = max(currVal, valToComp);","  }","  else if (slabType == 3) // sum","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  else // mean","  {","    retVal = currVal + (trapezoid > 0 ? 0.5 * valToComp : valToComp); ","  }","  return retVal;","}"])),i=gd.substitute(i,"//VTK::TCoord::Dec",u).result;let d=["if (any(greaterThan(fragTexCoord, vec3(1.0))) || any(lessThan(fragTexCoord, vec3(0.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}","vec4 tvalue = texture(texture1, fragTexCoord);"];if(s>0&&(d=d.concat(["// Get the first and last samples","int numSlices = 1;","float scaling = min(min(spacing.x, spacing.y), spacing.z) * 0.5;","vec3 normalxspacing = scaling * normalWCVSOutput;","float distTraveled = length(normalxspacing);","int trapezoid = 0;","while (distTraveled < slabThickness * 0.5)","{","  distTraveled += length(normalxspacing);","  float fnumSlices = float(numSlices);","  if (distTraveled > slabThickness * 0.5)","  {","    // Before stepping outside the slab, sample at the boundaries","    normalxspacing = normalWCVSOutput * slabThickness * 0.5 / fnumSlices;","    trapezoid = slabTrapezoid;","  }","  vec3 fragTCoordNeg = (WCTCMatrix * vec4(vertexWCVSOutput.xyz - fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordNeg);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","  vec3 fragTCoordPos = (WCTCMatrix * vec4(vertexWCVSOutput.xyz + fnumSlices * normalxspacing * vboScaling, 1.0)).xyz;","  if (!any(greaterThan(fragTCoordNeg, vec3(1.0))) && !any(lessThan(fragTCoordNeg, vec3(0.0))))","  {","    vec4 newVal = texture(texture1, fragTCoordPos);","    tvalue = compositeValue(tvalue, newVal, trapezoid);","    numSlices += 1;","  }","}","// Finally, if slab type is *mean*, divide the sum by the numSlices","if (slabType == 2)","{","  tvalue = tvalue / float(numSlices);","}"])),c){const e=["r","g","b","a"];for(let t=0;t<l;++t)d=d.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(l){case 1:d=d.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:d=d.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:d=d.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:ig("Unsupported number of independent coordinates.")}}else switch(l){case 1:d=d.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:d=d.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:d=d.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",d).result,e.Vertex=o,e.Fragment=i,e.Geometry=a},e.replaceShaderPositionVC=(n,r,o)=>{let a=n.Vertex;const i=n.Geometry;let s=n.Fragment;const l=t.renderable.getSlabThickness();let c=["attribute vec4 vertexWC;"];c=c.concat([`//${e.getMTime()}${t.resliceGeomUpdateString}`]),l>0&&(c=c.concat(["attribute vec3 normalWC;","varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),a=gd.substitute(a,"//VTK::PositionVC::Dec",c).result;let u=["gl_Position = MCPCMatrix * vertexWC;"];l>0&&(u=u.concat(["normalWCVSOutput = normalWC;","vertexWCVSOutput = vertexWC;"])),a=gd.substitute(a,"//VTK::PositionVC::Impl",u).result,a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;","uniform mat4 MCVCMatrix;"]).result;let d=[];l>0&&(d=d.concat(["varying vec3 normalWCVSOutput;","varying vec4 vertexWCVSOutput;"])),s=gd.substitute(s,"//VTK::PositionVC::Dec",d).result,n.Vertex=a,n.Geometry=i,n.Fragment=s},e.updateResliceGeometry=()=>{let e="";const n=t.currentInput,r=n?.getBounds();let o=!0,a=2;const i=t.renderable.getSlicePolyData(),s=t.renderable.getSlicePlane();if(i)e=e.concat(`PolyData${i.getMTime()}`);else if(s){e=e.concat(`Plane${s.getMTime()}`);const t=ie();n&&(e=e.concat(`Image${n.getMTime()}`),de(t,...n.getDirection()),ge(t,t));const r=[...s.getNormal()];Ln(r,r,t),[o,a]=function(e){za.normalize(e);const t=[0,0,0];for(let r=0;r<3;++r){(n=t)[0]=0,n[1]=0,n[2]=0,t[r]=1;const o=za.dot(e,t);if(o<-.999||o>.999)return[!0,r]}var n;return[!1,2]}(r)}else{const o=ni.newInstance();o.setNormal(0,0,1);let a=[0,1,0,1,0,1];n&&(a=r),o.setOrigin(a[0],a[2],.5*(a[5]+a[4])),t.renderable.setSlicePlane(o),e=e.concat(`Plane${s?.getMTime()}`),n&&(e=e.concat(`Image${n.getMTime()}`))}if(!t.resliceGeom||t.resliceGeomUpdateString!==e){if(i)t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(i.getPoints().getData(),3),t.resliceGeom.getPolys().setData(i.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(i.getPointData().getNormals());else if(s)if(o){const e=new Float32Array(12),r=n.worldToIndex(s.getOrigin(),[0,0,0]),o=[(a+1)%3,(a+2)%3].sort(),i=n.getSpatialExtent();let l=0;for(let t=0;t<2;++t)for(let n=0;n<2;++n)e[l+a]=r[a],e[l+o[0]]=i[2*o[0]+n],e[l+o[1]]=i[2*o[1]+t],l+=3;t.transform.setMatrix(n.getIndexToWorld()),t.transform.transformPoints(e,e);const c=new Uint16Array(8);c[0]=3,c[1]=0,c[2]=1,c[3]=3,c[4]=3,c[5]=0,c[6]=3,c[7]=2;const u=s.getNormal();za.normalize(u);const d=new Float32Array(12);for(let e=0;e<4;++e)d[3*e]=u[0],d[3*e+1]=u[1],d[3*e+2]=u[2];t.resliceGeom||(t.resliceGeom=yu.newInstance()),t.resliceGeom.getPoints().setData(e,3),t.resliceGeom.getPolys().setData(c,1);const p=Ds.newInstance({numberOfComponents:3,values:d,name:"Normals"});t.resliceGeom.getPointData().setNormals(p)}else{t.outlineFilter.setInputData(n),t.cutter.setInputConnection(t.outlineFilter.getOutputPort()),t.cutter.setCutFunction(s),t.lineToSurfaceFilter.setInputConnection(t.cutter.getOutputPort()),t.lineToSurfaceFilter.update(),t.resliceGeom||(t.resliceGeom=yu.newInstance());const e=t.lineToSurfaceFilter.getOutputData();t.resliceGeom.getPoints().setData(e.getPoints().getData(),3),t.resliceGeom.getPolys().setData(e.getPolys().getData(),1),t.resliceGeom.getPointData().setNormals(e.getPointData().getNormals());const r=s.getNormal(),o=t.resliceGeom.getNumberOfPoints();za.normalize(r);const a=new Float32Array(3*o);for(let e=0;e<o;++e)a[3*e]=r[0],a[3*e+1]=r[1],a[3*e+2]=r[2];const i=Ds.newInstance({numberOfComponents:3,values:a,name:"Normals"});t.resliceGeom.getPointData().setNormals(i)}else ig("Something went wrong.","A default slice plane should have been created in the beginning of","updateResliceGeometry.");t.resliceGeomUpdateString=e,t.resliceGeom?.modified()}},e.setOpenGLTexture=e=>{e&&(t.openGLTexture=e,t._externalOpenGLTexture=!0)},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const ug={VBOBuildTime:{},VBOBuildString:null,haveSeenDepthRequest:!1,lastHaveSeenDepthRequest:!1,lastIndependentComponents:!1,lastTextureComponents:0,lastSlabThickness:0,lastSlabTrapezoidIntegration:0,lastSlabType:-1,openGLTexture:null,colorTextureString:null,pwfTextureString:null,resliceGeom:null,resliceGeomUpdateString:null,tris:null,colorTexture:null,pwfTexture:null,_externalOpenGLTexture:!1};const dg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ug,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.openGLTexture=null,t.colorTexture=null,t.pwfTexture=null,t.VBOBuildTime={},yt(t.VBOBuildTime),t.tmpMat4=g(new Float64Array(16)),t.outlineFilter=Qf.newInstance(),t.outlineFilter.setGenerateFaces(!0),t.outlineFilter.setGenerateLines(!1),t.cubePolyData=yu.newInstance(),t.cutter=Nf.newInstance(),t.lineToSurfaceFilter=Ef.newInstance(),t.transform=rg.newInstance(),bt(e,t,["openGLTexture"]),cg(e,t)}),"vtkOpenGLImageResliceMapper");rn("vtkImageResliceMapper",dg);var pg={SlicingMode:{NONE:-1,I:0,J:1,K:2,X:3,Y:4,Z:5}};const{vtkErrorMacro:fg}=Kt,{SlicingMode:gg}=pg;function mg(e,t,n){if(t){const r=e.getIndependentComponents();return`${t.getMTime()}-${r}-${n}`}return"0"}function hg(e){const t=e.split("\n"),n=[];for(let e=0;e<t.length;++e){const r=t[e].trim();r.length>0&&n.push(r)}return n}function vg(e,t){function n(n){t.openGLTexture.releaseGraphicsResources(n),[t._colorTransferFunc,t._pwFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLImageMapper"),e.buildPass=r=>{if(r){t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const r=t._openGLRenderWindow;t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow);const o=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(o.getActiveCamera()),t.renderable.isA("vtkImageMapper")&&t.renderable.getSliceAtFocalPoint()&&t.renderable.setSliceFromCamera(o.getActiveCamera())}},e.translucentPass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.zBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.opaqueZBufferPass=t=>e.zBufferPass(t),e.opaquePass=t=>{t&&e.render()},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()==Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result,a=gd.substitute(a,"//VTK::TCoord::Impl","tcoordVCVSOutput = tcoordMC;").result,a=gd.substitute(a,"//VTK::TCoord::Dec","attribute vec2 tcoordMC; varying vec2 tcoordVCVSOutput;").result;const s=t.openGLTexture.getComponents(),l=o.getProperty().getIndependentComponents();let c=["varying vec2 tcoordVCVSOutput;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;","uniform sampler2D texture1;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform sampler2D labelOutlineTexture1;","uniform float opacity;","uniform float outlineOpacity;"];if(l){for(let e=1;e<s;e++)c=c.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(s){case 1:c=c.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:c=c.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:c=c.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:fg("Unsupported number of independent coordinates.")}}if(i=gd.substitute(i,"//VTK::TCoord::Dec",c).result,!0===o.getProperty().getUseLabelOutline()&&(i=gd.substitute(i,"//VTK::LabelOutline::Dec",["uniform int outlineThickness;","uniform float vpWidth;","uniform float vpHeight;","uniform float vpOffsetX;","uniform float vpOffsetY;","uniform mat4 PCWCMatrix;","uniform mat4 vWCtoIDX;","uniform ivec3 imageDimensions;"]).result,i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result,i=gd.substitute(i,"//VTK::LabelOutlineHelperFunction",["#ifdef vtkImageLabelOutlineOn","vec3 fragCoordToIndexSpace(vec4 fragCoord) {","  vec4 pcPos = vec4(","    (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,","    (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,","    (fragCoord.z - 0.5) * 2.0,","    1.0);","","  vec4 worldCoord = PCWCMatrix * pcPos;","  vec4 vertex = (worldCoord/worldCoord.w);","","  vec3 index = (vWCtoIDX * vertex).xyz;","","  // half voxel fix for labelmapOutline","  return (index + vec3(0.5)) / vec3(imageDimensions);","}","#endif"]).result),l){const e=["r","g","b","a"];let t=["vec4 tvalue = texture2D(texture1, tcoordVCVSOutput);"];for(let n=0;n<s;n++)t=t.concat([`vec3 tcolor${n} = mix${n} * texture2D(colorTexture1, vec2(tvalue.${e[n]} * cscale${n} + cshift${n}, height${n})).rgb;`,`float compWeight${n} = mix${n} * texture2D(pwfTexture1, vec2(tvalue.${e[n]} * pwfscale${n} + pwfshift${n}, height${n})).r;`]);switch(s){case 1:t=t.concat(["gl_FragData[0] = vec4(tcolor0.rgb, opacity);"]);break;case 2:t=t.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:t=t.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:fg("Unsupported number of independent coordinates.")}i=gd.substitute(i,"//VTK::TCoord::Impl",t).result}else switch(s){case 1:i=gd.substitute(i,"//VTK::TCoord::Impl",[...hg("\n                #ifdef vtkImageLabelOutlineOn\n                  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord);\n                  float centerValue = texture2D(texture1, centerPosIS.xy).r;\n                  bool pixelOnBorder = false;\n                  vec3 tColor = texture2D(colorTexture1, vec2(centerValue * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(centerValue * pwfscale0 + pwfshift0, 0.5)).r;\n                  float opacityToUse = scalarOpacity * opacity;\n                  int segmentIndex = int(centerValue * 255.0);\n                  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n                  float textureValue = texture2D(labelOutlineTexture1, vec2(textureCoordinate, 0.5)).r;\n                  int actualThickness = int(textureValue * 255.0);\n\n                  if (segmentIndex == 0){\n                    gl_FragData[0] = vec4(0.0, 1.0, 1.0, 0.0);\n                    return;\n                  }\n\n                  for (int i = -actualThickness; i <= actualThickness; i++) {\n                    for (int j = -actualThickness; j <= actualThickness; j++) {\n                      if (i == 0 || j == 0) {\n                        continue;\n                      }\n                      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n                        gl_FragCoord.y + float(j),\n                        gl_FragCoord.z, gl_FragCoord.w);\n                      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n                      float value = texture2D(texture1, neighborPosIS.xy).r;\n                      if (value != centerValue) {\n                        pixelOnBorder = true;\n                        break;\n                      }\n                    }\n                    if (pixelOnBorder == true) {\n                      break;\n                    }\n                  }\n                  if (pixelOnBorder == true) {\n                    gl_FragData[0] = vec4(tColor, outlineOpacity);\n                  }\n                  else {\n                    gl_FragData[0] = vec4(tColor, opacityToUse);\n                  }\n                #else\n                  float intensity = texture2D(texture1, tcoordVCVSOutput).r;\n                  vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;\n                  float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;\n                  gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);\n                #endif\n                ")]).result;break;case 2:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = texture2D(texture1, tcoordVCVSOutput);","float intensity = tcolor.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tcolor.g + pwfshift0);"]).result;break;case 3:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]).result;break;default:i=gd.substitute(i,"//VTK::TCoord::Impl",["vec4 tcolor = cscale0*texture2D(texture1, tcoordVCVSOutput.st) + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"]).result}t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.openGLTexture.getComponents(),a=r.getProperty().getIndependentComponents();let i=!1;return(!t.currentRenderPass&&t.lastRenderPassShaderReplacement||t.currentRenderPass&&t.currentRenderPass.getShaderReplacement()!==t.lastRenderPassShaderReplacement)&&(i=!0),!(!i&&t.lastHaveSeenDepthRequest===t.haveSeenDepthRequest&&0!==e.getProgram()?.getHandle()&&t.lastTextureComponents===o&&t.lastIndependentComponents===a||(t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,0))},e.updateShaders=(n,r,o)=>{if(t.lastBoundBO=n,e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{n.getCABO().getElementCount()&&(t.VBOBuildTime>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(n.getProgram().isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"vertexMC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||fg("Error setting vertexMC in shader VAO.")),n.getProgram().isAttributeUsed("tcoordMC")&&n.getCABO().getTCoordOffset()&&(n.getVAO().addAttributeArray(n.getProgram(),n.getCABO(),"tcoordMC",n.getCABO().getTCoordOffset(),n.getCABO().getStride(),t.context.FLOAT,n.getCABO().getTCoordComponents(),t.context.FALSE)||fg("Error setting tcoordMC in shader VAO.")),n.getAttributeUpdateTime().modified());const a=t.openGLTexture.getTextureUnit();n.getProgram().setUniformi("texture1",a);const i=t.openGLTexture.getComponents(),s=o.getProperty().getIndependentComponents();if(s)for(let e=0;e<i;e++)n.getProgram().setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));const l=t.openGLTexture.getShiftAndScale();for(let e=0;e<i;e++){let t=o.getProperty().getColorWindow(),r=o.getProperty().getColorLevel();const a=s?e:0,i=o.getProperty().getRGBTransferFunction(a);if(i&&o.getProperty().getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],r=.5*(e[1]+e[0])}const c=l.scale/t,u=(l.shift-r)/t+.5;n.getProgram().setUniformf(`cshift${e}`,u),n.getProgram().setUniformf(`cscale${e}`,c)}for(let e=0;e<i;e++){let t=1,r=0;const a=s?e:0,i=o.getProperty().getPiecewiseFunction(a);if(i){const e=i.getRange(),n=e[1]-e[0],o=.5*(e[0]+e[1]);t=l.scale/n,r=(l.shift-o)/n+.5}n.getProgram().setUniformf(`pwfshift${e}`,r),n.getProgram().setUniformf(`pwfscale${e}`,t)}if(t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),n.getProgram().isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);n.getProgram().setUniformf("coffset",t.offset),n.getProgram().isUniformUsed("cfactor")&&n.getProgram().setUniformf("cfactor",t.factor)}const c=t.colorTexture.getTextureUnit();n.getProgram().setUniformi("colorTexture1",c);const u=t.pwfTexture.getTextureUnit();n.getProgram().setUniformi("pwfTexture1",u);const p=t.labelOutlineThicknessTexture.getTextureUnit();if(n.getProgram().setUniformi("labelOutlineTexture1",p),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(et("OpenGL has a limit of 6 clipping planes"),e=6);const r=n.getCABO().getCoordShiftAndScaleEnabled()?n.getCABO().getInverseShiftAndScaleMatrix():null,a=r?d(t.imagematinv,o.getMatrix()):o.getMatrix();r&&(m(a,a),T(a,a,r),m(a,a)),m(t.imagemat,t.currentInput.getIndexToWorld()),T(t.imagematinv,a,t.imagemat);const i=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)i.push(e[t])}n.getProgram().setUniformi("numClipPlanes",e),n.getProgram().setUniform4fv("clipPlanes",i)}if(!0===o.getProperty().getUseLabelOutline()){const e=o.getProperty().getLabelOutlineOpacity();n.getProgram().setUniformf("outlineOpacity",e)}},e.setCameraShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=t.openGLImageSlice.getKeyMatrices(),s=t.currentInput,l=s.getIndexToWorld();T(t.imagemat,i.mcwc,l);const c=t.openGLCamera.getKeyMatrices(r);if(T(t.imagemat,c.wcpc,t.imagemat),n.getCABO().getCoordShiftAndScaleEnabled()){const e=n.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,e)}if(a.setUniformMatrix("MCPCMatrix",t.imagemat),!0===o.getProperty().getUseLabelOutline()){const n=s.getWorldToIndex(),o=s.getDimensions();a.setUniform3i("imageDimensions",o[0],o[1],1),a.setUniformMatrix("vWCtoIDX",n);const i=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,i.wcpc),t.openGLCamera.getKeyMatrices(r),a.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const l=e.getRenderTargetSize();a.setUniformf("vpWidth",l[0]),a.setUniformf("vpHeight",l[1]);const c=e.getRenderTargetOffset();a.setUniformf("vpOffsetX",c[0]/l[0]),a.setUniformf("vpOffsetY",c[1]/l[1])}},e.setPropertyShaderParameters=(e,t,n)=>{const r=e.getProgram(),o=n.getProperty().getOpacity();r.setUniformf("opacity",o)},e.renderPieceStart=(n,r)=>{e.updateBufferObjects(n,r),t.lastBoundBO=null},e.renderPieceDraw=(n,r)=>{const o=t.context;t.openGLTexture.activate(),t.colorTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.openGLTexture.deactivate(),t.colorTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getCurrentImage(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):fg("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.openGLTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle()||!t.pwfTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=a.getDataType(),s=a.getNumberOfComponents(),l=r.getProperty(),c=l.getInterpolationType(),u=l.getIndependentComponents(),d=u?s:1,p=u?2*d:1,f=l.getRGBTransferFunction(),g=mg(l,f,d),m=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(m?.oglObject?.getHandle()&&m?.hash===g)t.colorTexture=m.oglObject;else{t.colorTexture=Ud.newInstance({resizable:!0}),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(n*p*3);if(c===og.NEAREST?(t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST)):(t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR)),f){const e=new Float32Array(3*n);for(let t=0;t<d;t++){const o=l.getRGBTransferFunction(t),a=o.getRange();if(o.getTable(a[0],a[1],n,e,1),u)for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o],r[t*n*6+o+3*n]=255*e[o];else for(let o=0;o<3*n;o++)r[t*n*6+o]=255*e[o]}t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(n,p,3,ys.UNSIGNED_CHAR,r)}else{for(let e=0;e<3*n;++e)r[e]=255*e/(3*(n-1)),r[e+1]=255*e/(3*(n-1)),r[e+2]=255*e/(3*(n-1));t.colorTexture.create2DFromRaw(n,1,3,ys.UNSIGNED_CHAR,r)}f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,g),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}const h=l.getPiecewiseFunction(),v=mg(l,h,d),y=t._openGLRenderWindow.getGraphicsResourceForObject(h);if(y?.oglObject?.getHandle()&&y?.hash===v)t.pwfTexture=y.oglObject;else{const n=1024,r=n*p,o=new Uint8ClampedArray(r);if(t.pwfTexture=Ud.newInstance({resizable:!0}),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow),c===og.NEAREST?(t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR)),h){const e=new Float32Array(r),o=new Float32Array(n);for(let t=0;t<d;++t){const r=l.getPiecewiseFunction(t);if(null===r)e.fill(1);else{const a=r.getRange();if(r.getTable(a[0],a[1],n,o,1),u)for(let r=0;r<n;r++)e[t*n*2+r]=o[r],e[t*n*2+r+n]=o[r];else for(let r=0;r<n;r++)e[t*n*2+r]=o[r]}}t.pwfTexture.resetFormatAndType(),t.pwfTexture.create2DFromRaw(n,p,1,ys.FLOAT,e)}else o.fill(255),t.pwfTexture.create2DFromRaw(n,1,1,ys.UNSIGNED_CHAR,o);h&&(t._openGLRenderWindow.setGraphicsResourceForObject(h,t.pwfTexture,v),h!==t._pwFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(h,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._pwFunc,e)),t._pwFunc=h)}e.updatelabelOutlineThicknessTexture(r);const{ijkMode:T}=t.renderable.getClosestIJKAxis();let b=t.renderable.getSlice();T!==t.renderable.getSlicingMode()&&(b=t.renderable.getSliceAtPosition(b));const x=t.renderable.isA("vtkImageArrayMapper")?t.renderable.getSubSlice():Math.round(b),C=o.getExtent();let S;T===gg.I&&(S=x-C[0]),T===gg.J&&(S=x-C[2]),T!==gg.K&&T!==gg.NONE||(S=x-C[4]);const A=`${b}A${o.getMTime()}A${a.getMTime()}B${e.getMTime()}C${t.renderable.getSlicingMode()}D${r.getProperty().getInterpolationType()}`;if(t.VBOBuildString!==A){const e=o.getDimensions();t.openGLTexture||(t.openGLTexture=Ud.newInstance({resizable:!0})),t.openGLTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),c===og.NEAREST?(new Set([1,3,4]).has(s)&&i===ys.UNSIGNED_CHAR&&!u?(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.NEAREST)):t.openGLTexture.setMinificationFilter(Id.NEAREST),t.openGLTexture.setMagnificationFilter(Id.NEAREST)):(4!==s||i!==ys.UNSIGNED_CHAR||u?t.openGLTexture.setMinificationFilter(Id.LINEAR):(t.openGLTexture.setGenerateMipmap(!0),t.openGLTexture.setMinificationFilter(Id.LINEAR_MIPMAP_LINEAR)),t.openGLTexture.setMagnificationFilter(Id.LINEAR)),t.openGLTexture.setWrapS(Ad.CLAMP_TO_EDGE),t.openGLTexture.setWrapT(Ad.CLAMP_TO_EDGE);const n=e[0]*e[1]*s,r=new Float32Array(12),l=new Float32Array(8);for(let e=0;e<4;e++)l[2*e]=e%2?1:0,l[2*e+1]=e>1?1:0;const d=[gg.X,gg.Y,gg.Z].includes(t.renderable.getSlicingMode())?b:x,p=o.getSpatialExtent(),f=a.getData();let g=null;if(T===gg.I){g=new f.constructor(e[2]*e[1]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[1];r++){let o=(S+r*e[0]+n*e[0]*e[1])*s;t=(n*e[1]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[0]=e[1],e[1]=e[2],r[0]=d,r[1]=p[2],r[2]=p[4],r[3]=d,r[4]=p[3],r[5]=p[4],r[6]=d,r[7]=p[2],r[8]=p[5],r[9]=d,r[10]=p[3],r[11]=p[5]}else if(T===gg.J){g=new f.constructor(e[2]*e[0]*s);let t=0;for(let n=0;n<e[2];n++)for(let r=0;r<e[0];r++){let o=(r+S*e[0]+n*e[0]*e[1])*s;t=(n*e[0]+r)*s;const a=o+s;for(;o<a;)g[t++]=f[o++]}e[1]=e[2],r[0]=p[0],r[1]=d,r[2]=p[4],r[3]=p[1],r[4]=d,r[5]=p[4],r[6]=p[0],r[7]=d,r[8]=p[5],r[9]=p[1],r[10]=d,r[11]=p[5]}else T===gg.K||T===gg.NONE?(g=f.subarray(S*n,(S+1)*n),r[0]=p[0],r[1]=p[2],r[2]=d,r[3]=p[1],r[4]=p[2],r[5]=d,r[6]=p[0],r[7]=p[3],r[8]=d,r[9]=p[1],r[10]=p[3],r[11]=d):fg("Reformat slicing not yet supported.");t.openGLTexture.resetFormatAndType(),t.openGLTexture.create2DFilterableFromRaw(e[0],e[1],s,a.getDataType(),g,t.renderable.getPreferSizeOverAccuracy?.()),t.openGLTexture.activate(),t.openGLTexture.sendParameters(),t.openGLTexture.deactivate();const m=Ds.newInstance({numberOfComponents:3,values:r});m.setName("points");const h=Ds.newInstance({numberOfComponents:2,values:l});h.setName("tcoords");const v=new Uint16Array(8);v[0]=3,v[1]=0,v[2]=1,v[3]=3,v[4]=3,v[5]=0,v[6]=3,v[7]=2;const y=Ds.newInstance({numberOfComponents:1,values:v});t.tris.getCABO().createVBO(y,"polys",rs.SURFACE,{points:m,tcoords:h,cellOffset:0}),t.VBOBuildTime.modified(),t.VBOBuildString=A}},e.updatelabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture=Ud.newInstance({resizable:!1}),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.delete=Vt((()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete)}const yg={VBOBuildTime:0,VBOBuildString:null,openGLTexture:null,tris:null,imagemat:null,imagematinv:null,colorTexture:null,pwfTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0};const Tg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,yg,n),Zt.extend(e,t,n),Hd(e,t,n),jd(e,t,n),t.tris=Sd.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),At(e,t,[]),t.VBOBuildTime={},yt(t.VBOBuildTime),vg(e,t)}),"vtkOpenGLImageMapper");rn("vtkAbstractImageMapper",Tg);const bg=0,xg=1,Cg=2,{vtkErrorMacro:Sg}=jt;function Ag(e,t,n){if(t.apply(e)){const t=e.getIndependentComponents();return`${e.getMTime()}-${t}-${n}`}return"0"}function Ig(e,t){t.classHierarchy.push("vtkOpenGLImageCPRMapper"),e.buildPass=n=>{n&&(t.currentRenderPass=null,t.openGLImageSlice=e.getFirstAncestorOfType("vtkOpenGLImageSlice"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow"),t.context=t._openGLRenderWindow.getContext(),t.openGLCamera=t._openGLRenderer.getViewNodeFor(t._openGLRenderer.getRenderable().getActiveCamera()),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.volumeTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.pwfTexture.setOpenGLRenderWindow(t._openGLRenderWindow))},e.opaquePass=(n,r)=>{n&&(t.currentRenderPass=r,e.render())},e.opaqueZBufferPass=n=>{n&&(t.haveSeenDepthRequest=!0,t.renderDepth=!0,e.render(),t.renderDepth=!1)},e.getCoincidentParameters=(e,n)=>t.renderable.getResolveCoincidentTopology()===Vl.PolygonOffset?t.renderable.getCoincidentTopologyPolygonOffsetParameters():null,e.render=()=>{const n=t.openGLImageSlice.getRenderable(),r=t._openGLRenderer.getRenderable();e.renderPiece(r,n)},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),e.invokeEvent({type:"EndEvent"}),t.renderable.preRenderCheck()&&(t.currentImageDataInput=t.renderable.getInputData(0),t.currentCenterlineInput=t.renderable.getOrientedCenterline(),e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r))},e.renderPieceStart=(t,n)=>{e.updateBufferObjects(t,n)},e.renderPieceDraw=(n,r)=>{const o=t.context;t.volumeTexture.activate(),t.colorTexture.activate(),t.pwfTexture.activate(),t.tris.getCABO().getElementCount()&&(e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release()),t.volumeTexture.deactivate(),t.colorTexture.deactivate(),t.pwfTexture.deactivate()},e.renderPieceFinish=(e,t)=>{},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>{const o=t.VBOBuildTime.getMTime();return o<e.getMTime()||o<t.renderable.getMTime()||o<r.getMTime()||o<t.currentImageDataInput.getMTime()||o<t.currentCenterlineInput.getMTime()},e.buildBufferObjects=(e,n)=>{const r=t.currentImageDataInput,o=t.currentCenterlineInput;n.getProperty().getInterpolationType()===og.NEAREST?(t.volumeTexture.setMinificationFilter(Id.NEAREST),t.volumeTexture.setMagnificationFilter(Id.NEAREST),t.colorTexture.setMinificationFilter(Id.NEAREST),t.colorTexture.setMagnificationFilter(Id.NEAREST),t.pwfTexture.setMinificationFilter(Id.NEAREST),t.pwfTexture.setMagnificationFilter(Id.NEAREST)):(t.volumeTexture.setMinificationFilter(Id.LINEAR),t.volumeTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.pwfTexture.setMinificationFilter(Id.LINEAR),t.pwfTexture.setMagnificationFilter(Id.LINEAR));const a=r.getMTime();if(t.volumeTextureTime!==a){const e=r.getDimensions(),n=r.getPointData().getScalars();if(!n)return;t.volumeTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.volumeTexture.releaseGraphicsResources(t._openGLRenderWindow),t.volumeTexture.resetFormatAndType(),t.volumeTexture.create3DFilterableFromRaw(e[0],e[1],e[2],n.getNumberOfComponents(),n.getDataType(),n.getData(),t.renderable.getPreferSizeOverAccuracy()),t.volumeTextureTime=a}const i=r.getPointData()&&r.getPointData().getScalars();if(!i)return;const s=i.getNumberOfComponents(),l=n.getProperty(),c=l.getIndependentComponents(),u=c?s:1,d=c?2*u:1,p=Ag(l,l.getRGBTransferFunction,u);if(t.colorTextureString!==p){const e=1024,n=new Uint8ClampedArray(e*d*3);let r=l.getRGBTransferFunction();if(r){const o=new Float32Array(3*e);for(let t=0;t<u;t++){r=l.getRGBTransferFunction(t);const a=r.getRange();if(r.getTable(a[0],a[1],e,o,1),c)for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r],n[t*e*6+r+3*e]=255*o[r];else for(let r=0;r<3*e;r++)n[t*e*6+r]=255*o[r]}t.colorTexture.releaseGraphicsResources(t._openGLRenderWindow),t.colorTexture.resetFormatAndType(),t.colorTexture.create2DFromRaw(e,d,3,ys.UNSIGNED_CHAR,n)}else{for(let t=0;t<3*e;++t)n[t]=255*t/(3*(e-1)),n[t+1]=255*t/(3*(e-1)),n[t+2]=255*t/(3*(e-1));t.colorTexture.create2DFromRaw(e,1,3,ys.UNSIGNED_CHAR,n)}t.colorTextureString=p}const f=Ag(l,l.getPiecewiseFunction,u);if(t.pwfTextureString!==f){const e=1024,n=e*d,r=new Uint8ClampedArray(n);let o=l.getPiecewiseFunction();if(t.pwfTexture.releaseGraphicsResources(t._openGLRenderWindow),t.pwfTexture.resetFormatAndType(),o){const r=new Float32Array(n),a=new Float32Array(e);for(let t=0;t<u;++t)if(o=l.getPiecewiseFunction(t),null===o)r.fill(1);else{const n=o.getRange();if(o.getTable(n[0],n[1],e,a,1),c)for(let n=0;n<e;n++)r[t*e*2+n]=a[n],r[t*e*2+n+e]=a[n];else for(let n=0;n<e;n++)r[t*e*2+n]=a[n]}t.pwfTexture.create2DFromRaw(e,d,1,ys.FLOAT,r)}else r.fill(255),t.pwfTexture.create2DFromRaw(e,1,1,ys.UNSIGNED_CHAR,r);t.pwfTextureString=f}if(t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<o.getMTime()){const e=o.getNumberOfPoints(),n=e<=1?0:e-1,r=o.getDistancesToFirstPoint(),a=t.renderable.getHeight(),i=4*n,s=new Float32Array(3*i),l=t.renderable.getWidth();for(let e=0,t=0;e<n;++e)s.set([0,a-r[e],0],t),t+=3,s.set([l,a-r[e],0],t),t+=3,s.set([l,a-r[e+1],0],t),t+=3,s.set([0,a-r[e+1],0],t),t+=3;const c=Ds.newInstance({numberOfComponents:3,values:s});c.setName("points");const u=new Uint16Array(5*n);for(let e=0,t=0,r=0;e<n;++e)u.set([4,r+3,r+2,r+1,r],t),t+=5,r+=4;const d=Ds.newInstance({numberOfComponents:1,values:u}),p=o.getPoints(),f=new Float32Array(3*i),g=new Array(3),m=new Array(3);for(let e=0,t=0;e<n;++e)p.getPoint(e,g),p.getPoint(e+1,m),f.set(g,t),t+=3,f.set(g,t),t+=3,f.set(m,t),t+=3,f.set(m,t),t+=3;const h=Ds.newInstance({numberOfComponents:3,values:f,name:"centerlinePosition"}),v=new Float32Array(i);for(let e=0,t=0;e<n;++e)v.set([0,1,3,2],t),t+=4;const y=[h,Ds.newInstance({numberOfComponents:1,values:v,name:"quadIndex"})];if(!t.renderable.getUseUniformOrientation()){const e=t.renderable.getOrientedCenterline().getOrientations()??[],r=new Float32Array(4*i),o=new Float32Array(4*i);for(let t=0;t<n;++t){const n=e[t],a=e[t+1];for(let e=0;e<4;++e){const i=4*(e+4*t);r.set(n,i),o.set(a,i)}}const a=Ds.newInstance({numberOfComponents:4,values:r,name:"centerlineTopOrientation"}),s=Ds.newInstance({numberOfComponents:4,values:o,name:"centerlineBotOrientation"});y.push(a,s)}t.tris.getCABO().createVBO(d,"polys",rs.SURFACE,{points:c,customAttributes:y}),t.VBOBuildTime.modified()}},e.getNeedToRebuildShaders=(e,n,r)=>{const o=t.volumeTexture.getComponents(),a=r.getProperty().getIndependentComponents(),i=!!t.renderable.getCenterPoint(),s=t.renderable.getUseUniformOrientation(),l=t.renderable.isProjectionEnabled()&&t.renderable.getProjectionMode();return(0===e.getProgram()||t.lastUseCenterPoint!==i||t.lastUseUniformOrientation!==s||t.lastProjectionMode!==l||t.lastHaveSeenDepthRequest!==t.haveSeenDepthRequest||t.lastTextureComponents!==o||t.lastIndependentComponents!==a)&&(t.lastUseCenterPoint=i,t.lastUseUniformOrientation=s,t.lastProjectionMode=l,t.lastHaveSeenDepthRequest=t.haveSeenDepthRequest,t.lastTextureComponents=o,t.lastIndependentComponents=a,!0)},e.buildShaders=(t,n,r)=>{e.getShaderTemplate(t,n,r),e.replaceShaderValues(t,n,r)},e.replaceShaderValues=(n,r,o)=>{let a=n.Vertex,i=n.Fragment;const s=["vec3 applyQuaternionToVec(vec4 q, vec3 v) {","  float uvx = q.y * v.z - q.z * v.y;","  float uvy = q.z * v.x - q.x * v.z;","  float uvz = q.x * v.y - q.y * v.x;","  float uuvx = q.y * uvz - q.z * uvy;","  float uuvy = q.z * uvx - q.x * uvz;","  float uuvz = q.x * uvy - q.y * uvx;","  float w2 = q.w * 2.0;","  uvx *= w2;","  uvy *= w2;","  uvz *= w2;","  uuvx *= 2.0;","  uuvy *= 2.0;","  uuvz *= 2.0;","  return vec3(v.x + uvx + uuvx, v.y + uvy + uuvy, v.z + uvz + uuvz);","}"];a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 MCPCMatrix;"]).result,a=gd.substitute(a,"//VTK::PositionVC::Impl",["  gl_Position = MCPCMatrix * vertexMC;"]).result;const l=["attribute vec3 centerlinePosition;","attribute float quadIndex;","uniform float width;","out vec2 quadOffsetVSOutput;","out vec3 centerlinePosVSOutput;"],c=t.renderable.isProjectionEnabled(),u=t.renderable.getUseUniformOrientation();u?(l.push("out vec3 samplingDirVSOutput;","uniform vec4 centerlineOrientation;","uniform vec3 tangentDirection;",...s),c&&l.push("out vec3 projectionDirVSOutput;","uniform vec3 bitangentDirection;")):l.push("out vec4 centerlineTopOrientationVSOutput;","out vec4 centerlineBotOrientationVSOutput;","attribute vec4 centerlineTopOrientation;","attribute vec4 centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Dec",l).result;const d=["quadOffsetVSOutput = vec2(width * (mod(quadIndex, 2.0) == 0.0 ? -0.5 : 0.5), quadIndex > 1.0 ? 0.0 : 1.0);","centerlinePosVSOutput = centerlinePosition;"];u?(d.push("samplingDirVSOutput = applyQuaternionToVec(centerlineOrientation, tangentDirection);"),c&&d.push("projectionDirVSOutput = applyQuaternionToVec(centerlineOrientation, bitangentDirection);")):d.push("centerlineTopOrientationVSOutput = centerlineTopOrientation;","centerlineBotOrientationVSOutput = centerlineBotOrientation;"),a=gd.substitute(a,"//VTK::Color::Impl",d).result;const p=t.volumeTexture.getComponents(),f=o.getProperty().getIndependentComponents();let g=["uniform mat4 MCTCMatrix; // Model coordinates to texture coordinates","in vec2 quadOffsetVSOutput;","in vec3 centerlinePosVSOutput;","uniform highp sampler3D volumeTexture;","uniform sampler2D colorTexture1;","uniform sampler2D pwfTexture1;","uniform float opacity;","uniform vec4 backgroundColor;","uniform float cshift0;","uniform float cscale0;","uniform float pwfshift0;","uniform float pwfscale0;"];c&&g.push("uniform vec3 volumeSizeMC;","uniform int projectionSlabNumberOfSamples;","uniform float projectionConstantOffset;","uniform float projectionStepLength;"),u?(g.push("in vec3 samplingDirVSOutput;"),c&&g.push("in vec3 projectionDirVSOutput;")):(g.push("uniform vec3 tangentDirection;","in vec4 centerlineTopOrientationVSOutput;","in vec4 centerlineBotOrientationVSOutput;",...s),c&&g.push("uniform vec3 bitangentDirection;"));const m=t.renderable.getCenterPoint();if(m&&g.push("uniform vec3 globalCenterPoint;"),f){for(let e=1;e<p;e++)g=g.concat([`uniform float cshift${e};`,`uniform float cscale${e};`,`uniform float pwfshift${e};`,`uniform float pwfscale${e};`]);switch(p){case 1:g=g.concat(["uniform float mix0;","#define height0 0.5"]);break;case 2:g=g.concat(["uniform float mix0;","uniform float mix1;","#define height0 0.25","#define height1 0.75"]);break;case 3:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","#define height0 0.17","#define height1 0.5","#define height2 0.83"]);break;case 4:g=g.concat(["uniform float mix0;","uniform float mix1;","uniform float mix2;","uniform float mix3;","#define height0 0.125","#define height1 0.375","#define height2 0.625","#define height3 0.875"]);break;default:Sg("Unsupported number of independent coordinates.")}}i=gd.substitute(i,"//VTK::TCoord::Dec",g).result;let h=[];if(u?(h.push("vec3 samplingDirection = samplingDirVSOutput;"),c&&h.push("vec3 projectionDirection = projectionDirVSOutput;")):(h.push("vec4 q0 = centerlineBotOrientationVSOutput;","vec4 q1 = centerlineTopOrientationVSOutput;","float qCosAngle = dot(q0, q1);","vec4 interpolatedOrientation;","if (qCosAngle > 0.999 || qCosAngle < -0.999) {","  // Use LERP instead of SLERP when the two quaternions are close or opposite","  interpolatedOrientation = normalize(mix(q0, q1, quadOffsetVSOutput.y));","} else {","  float omega = acos(qCosAngle);","  interpolatedOrientation = normalize(sin((1.0 - quadOffsetVSOutput.y) * omega) * q0 + sin(quadOffsetVSOutput.y * omega) * q1);","}","vec3 samplingDirection = applyQuaternionToVec(interpolatedOrientation, tangentDirection);"),c&&h.push("vec3 projectionDirection = applyQuaternionToVec(interpolatedOrientation, bitangentDirection);")),m?h.push("float baseOffset = dot(samplingDirection, globalCenterPoint - centerlinePosVSOutput);","float horizontalOffset = quadOffsetVSOutput.x + baseOffset;"):h.push("float horizontalOffset = quadOffsetVSOutput.x;"),h.push("vec3 volumePosMC = centerlinePosVSOutput + horizontalOffset * samplingDirection;","vec3 volumePosTC = (MCTCMatrix * vec4(volumePosMC, 1.0)).xyz;","if (any(lessThan(volumePosTC, vec3(0.0))) || any(greaterThan(volumePosTC, vec3(1.0))))","{","  // set the background color and exit","  gl_FragData[0] = backgroundColor;","  return;","}"),c){const e=t.renderable.getProjectionMode();switch(e===xg?h.push("const vec4 initialProjectionTextureValue = vec4(1.0);"):h.push("const vec4 initialProjectionTextureValue = vec4(0.0);"),h.push("vec3 projectionScaledDirection = projectionDirection / volumeSizeMC;","vec3 projectionStep = projectionStepLength * projectionScaledDirection;","vec3 projectionStartPosition = volumePosTC + projectionConstantOffset * projectionScaledDirection;","vec4 tvalue = initialProjectionTextureValue;","for (int projectionSampleIdx = 0; projectionSampleIdx < projectionSlabNumberOfSamples; ++projectionSampleIdx) {","  vec3 projectionSamplePosition = projectionStartPosition + float(projectionSampleIdx) * projectionStep;","  vec4 sampledTextureValue = texture(volumeTexture, projectionSamplePosition);"),e){case bg:h.push("  tvalue = max(tvalue, sampledTextureValue);");break;case xg:h.push("  tvalue = min(tvalue, sampledTextureValue);");break;default:h.push("  tvalue = tvalue + sampledTextureValue;")}h.push("}"),e===Cg&&h.push("tvalue = tvalue / float(projectionSlabNumberOfSamples);")}else h.push("vec4 tvalue = texture(volumeTexture, volumePosTC);");if(f){const e=["r","g","b","a"];for(let t=0;t<p;++t)h=h.concat([`vec3 tcolor${t} = mix${t} * texture2D(colorTexture1, vec2(tvalue.${e[t]} * cscale${t} + cshift${t}, height${t})).rgb;`,`float compWeight${t} = mix${t} * texture2D(pwfTexture1, vec2(tvalue.${e[t]} * pwfscale${t} + pwfshift${t}, height${t})).r;`]);switch(p){case 1:h=h.concat(["gl_FragData[0] = vec4(tcolor0.rgb, compWeight0 * opacity);"]);break;case 2:h=h.concat(["float weightSum = compWeight0 + compWeight1;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum))), opacity);"]);break;case 3:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum))), opacity);"]);break;case 4:h=h.concat(["float weightSum = compWeight0 + compWeight1 + compWeight2 + compWeight3;","gl_FragData[0] = vec4(vec3((tcolor0.rgb * (compWeight0 / weightSum)) + (tcolor1.rgb * (compWeight1 / weightSum)) + (tcolor2.rgb * (compWeight2 / weightSum)) + (tcolor3.rgb * (compWeight3 / weightSum))), opacity);"]);break;default:Sg("Unsupported number of independent coordinates.")}}else switch(p){case 1:h=h.concat(["// Dependent components","float intensity = tvalue.r;","vec3 tcolor = texture2D(colorTexture1, vec2(intensity * cscale0 + cshift0, 0.5)).rgb;","float scalarOpacity = texture2D(pwfTexture1, vec2(intensity * pwfscale0 + pwfshift0, 0.5)).r;","gl_FragData[0] = vec4(tcolor, scalarOpacity * opacity);"]);break;case 2:h=h.concat(["float intensity = tvalue.r*cscale0 + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(intensity, 0.5)).rgb, pwfscale0*tvalue.g + pwfshift0);"]);break;case 3:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, opacity);"]);break;default:h=h.concat(["vec4 tcolor = cscale0*tvalue + cshift0;","gl_FragData[0] = vec4(texture2D(colorTexture1, vec2(tcolor.r,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.g,0.5)).r,","  texture2D(colorTexture1, vec2(tcolor.b,0.5)).r, tcolor.a);"])}i=gd.substitute(i,"//VTK::TCoord::Impl",h).result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec","uniform int depthRequest;").result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float iz = floor(gl_FragCoord.z*65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),n.Vertex=a,n.Fragment=i,e.replaceShaderClip(n,r,o),e.replaceShaderCoincidentOffset(n,r,o)},e.replaceShaderClip=(e,n,r)=>{let o=e.Vertex,a=e.Fragment;if(t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6),o=gd.substitute(o,"//VTK::Clip::Dec",["uniform int numClipPlanes;","uniform vec4 clipPlanes[6];","varying float clipDistancesVSOutput[6];"]).result,o=gd.substitute(o,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    clipDistancesVSOutput[planeNum] = dot(clipPlanes[planeNum], vertexMC);","    }"]).result,a=gd.substitute(a,"//VTK::Clip::Dec",["uniform int numClipPlanes;","varying float clipDistancesVSOutput[6];"]).result,a=gd.substitute(a,"//VTK::Clip::Impl",["for (int planeNum = 0; planeNum < 6; planeNum++)","    {","    if (planeNum >= numClipPlanes)","        {","        break;","        }","    if (clipDistancesVSOutput[planeNum] < 0.0) discard;","    }"]).result}e.Vertex=o,e.Fragment=a},e.getShaderTemplate=(e,t,n)=>{e.Vertex=zd,e.Fragment=Wd,e.Geometry=""},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram(),i=n.getCABO();i.getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexMC")&&(n.getVAO().addAttributeArray(a,i,"vertexMC",i.getVertexOffset(),i.getStride(),t.context.FLOAT,3,t.context.FALSE)||Sg("Error setting vertexMC in shader VAO.")),n.getCABO().getCustomData().forEach((e=>{e&&a.isAttributeUsed(e.name)&&!n.getVAO().addAttributeArray(a,i,e.name,e.offset,i.getStride(),t.context.FLOAT,e.components,t.context.FALSE)&&Sg(`Error setting ${e.name} in shader VAO.`)})),n.getAttributeUpdateTime().modified());const s=t.volumeTexture.getTextureUnit();if(a.setUniformi("volumeTexture",s),a.setUniformf("width",t.renderable.getWidth()),n.getProgram().setUniform4fv("backgroundColor",t.renderable.getBackgroundColor()),a.isUniformUsed("tangentDirection")){const e=t.renderable.getTangentDirection();n.getProgram().setUniform3fArray("tangentDirection",e)}if(a.isUniformUsed("bitangentDirection")){const e=t.renderable.getBitangentDirection();n.getProgram().setUniform3fArray("bitangentDirection",e)}if(a.isUniformUsed("centerlineOrientation")){const e=t.renderable.getUniformOrientation();n.getProgram().setUniform4fv("centerlineOrientation",e)}if(a.isUniformUsed("globalCenterPoint")){const e=t.renderable.getCenterPoint();a.setUniform3fArray("globalCenterPoint",e)}if(t.renderable.isProjectionEnabled()){const e=t.currentImageDataInput,n=e.getSpacing(),r=e.getDimensions(),o=t.renderable.getProjectionSlabThickness(),i=t.renderable.getProjectionSlabNumberOfSamples(),s=function(e,t,n){return e[0]=t[0]*n[0],e[1]=t[1]*n[1],e[2]=t[2]*n[2],e}([],n,r);a.setUniform3fArray("volumeSizeMC",s),a.setUniformi("projectionSlabNumberOfSamples",i);const l=-.5*o;a.setUniformf("projectionConstantOffset",l);const c=o/(i-1);a.setUniformf("projectionStepLength",c)}const l=t.currentImageDataInput,c=l.getWorldToIndex(),u=P(new Float32Array(16),Rn([],l.getDimensions())),p=oe(u,u,c);if(a.setUniformMatrix("MCTCMatrix",p),t.haveSeenDepthRequest&&n.getProgram().setUniformi("depthRequest",t.renderDepth?1:0),t.renderable.getNumberOfClippingPlanes()){let e=t.renderable.getNumberOfClippingPlanes();e>6&&(jt.vtkErrorMacro("OpenGL has a limit of 6 clipping planes"),e=6);const n=i.getCoordShiftAndScaleEnabled()?i.getInverseShiftAndScaleMatrix():null,r=n?d(t.imagematinv,o.getMatrix()):o.getMatrix();n&&(m(r,r),T(r,r,n),m(r,r)),m(t.imagemat,t.currentImageDataInput.getIndexToWorld()),T(t.imagematinv,r,t.imagemat);const s=[];for(let n=0;n<e;n++){const e=[];t.renderable.getClippingPlaneInDataCoords(t.imagematinv,n,e);for(let t=0;t<4;t++)s.push(e[t])}a.setUniformi("numClipPlanes",e),a.setUniform4fv("clipPlanes",s)}if(a.isUniformUsed("coffset")){const t=e.getCoincidentParameters(r,o);a.setUniformf("coffset",t.offset),a.isUniformUsed("cfactor")&&a.setUniformf("cfactor",t.factor)}},e.setCameraShaderParameters=(e,n,r)=>{const o=t.openGLImageSlice.getKeyMatrices().mcwc,a=t.openGLCamera.getKeyMatrices(n).wcpc;if(T(t.imagemat,a,o),e.getCABO().getCoordShiftAndScaleEnabled()){const n=e.getCABO().getInverseShiftAndScaleMatrix();T(t.imagemat,t.imagemat,n)}e.getProgram().setUniformMatrix("MCPCMatrix",t.imagemat)},e.setPropertyShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=r.getProperty(),i=a.getOpacity();o.setUniformf("opacity",i);const s=t.volumeTexture.getComponents(),l=a.getIndependentComponents();if(l)for(let e=0;e<s;++e)o.setUniformf(`mix${e}`,a.getComponentWeight(e));const c=t.volumeTexture.getVolumeInfo();for(let e=0;e<s;e++){let t=a.getColorWindow(),n=a.getColorLevel();const r=l?e:0,i=a.getRGBTransferFunction(r);if(i&&a.getUseLookupTableScalarRange()){const e=i.getRange();t=e[1]-e[0],n=.5*(e[1]+e[0])}const s=c.scale[e]/t,u=(c.offset[e]-n)/t+.5;o.setUniformf(`cshift${e}`,u),o.setUniformf(`cscale${e}`,s)}const u=t.colorTexture.getTextureUnit();o.setUniformi("colorTexture1",u);for(let e=0;e<s;e++){let t=1,n=0;const r=l?e:0,i=a.getPiecewiseFunction(r);if(i){const r=i.getRange(),o=r[1]-r[0],a=.5*(r[0]+r[1]);t=c.scale[e]/o,n=(c.offset[e]-a)/o+.5}o.setUniformf(`pwfshift${e}`,n),o.setUniformf(`pwfscale${e}`,t)}const d=t.pwfTexture.getTextureUnit();o.setUniformi("pwfTexture1",d)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o)}}const wg={currentRenderPass:null,volumeTexture:null,volumeTextureTime:0,colorTexture:null,colorTextureString:null,pwfTexture:null,pwfTextureString:null,tris:null,lastHaveSeenDepthRequest:!1,haveSeenDepthRequest:!1,lastTextureComponents:0,lastIndependentComponents:0,imagemat:null,imagematinv:null};const Pg=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,wg,n),Zt.extend(e,t,n),Hd(e,t,n),jt.algo(e,t,2,0),t.tris=Sd.newInstance(),t.volumeTexture=Ud.newInstance(),t.colorTexture=Ud.newInstance(),t.pwfTexture=Ud.newInstance(),t.imagemat=g(new Float64Array(16)),t.imagematinv=g(new Float64Array(16)),t.VBOBuildTime={},jt.obj(t.VBOBuildTime,{mtime:0}),Ig(e,t)}),"vtkOpenGLImageCPRMapper");function Og(e,t){t.classHierarchy.push("vtkOpenGLImageSlice"),e.buildPass=n=>{if(t.renderable&&t.renderable.getVisibility()&&n){if(!t.renderable)return;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes()}},e.traverseZBufferPass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseOpaqueZBufferPass=t=>e.traverseOpaquePass(t),e.traverseOpaquePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&t.renderable.getIsOpaque()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.traverseTranslucentPass=n=>{!t.renderable||!t.renderable.getNestedVisibility()||t.renderable.getIsOpaque()||t._openGLRenderer.getSelector()&&!t.renderable.getNestedPickable()||(e.apply(n,!0),t.children.forEach((e=>{e.traverse(n)})),e.apply(n,!1))},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;t.renderable.getIsOpaque()?n.incrementOpaqueActorCount():n.incrementTranslucentActorCount()}},e.zBufferPass=(t,n)=>e.opaquePass(t,n),e.opaqueZBufferPass=(t,n)=>e.opaquePass(t,n),e.opaquePass=(e,n)=>{e&&t.context.depthMask(!0)},e.translucentPass=(e,n)=>{t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(d(t.keyMatrices.mcwc,t.renderable.getMatrix()),m(t.keyMatrices.mcwc,t.keyMatrices.mcwc),t.keyMatrixTime.modified()),t.keyMatrices)}rn("vtkImageCPRMapper",Pg);const Rg={context:null,keyMatrixTime:null,keyMatrices:null};const Mg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Rg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.keyMatrices={mcwc:g(new Float64Array(16))},At(e,t,["context"]),Og(e,t)}),"vtkOpenGLImageSlice");function Eg(e,t){t.classHierarchy.push("vtkOpenGLVolume"),e.buildPass=n=>{t.renderable&&t.renderable.getVisibility()&&n&&(t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t.context=t._openGLRenderWindow.getContext(),e.prepareNodes(),e.addMissingNode(t.renderable.getMapper()),e.removeUnusedNodes())},e.queryPass=(e,n)=>{if(e){if(!t.renderable||!t.renderable.getVisibility())return;n.incrementVolumeCount()}},e.traverseVolumePass=n=>{t.renderable&&t.renderable.getNestedVisibility()&&(!t._openGLRenderer.getSelector()||t.renderable.getNestedPickable())&&(e.apply(n,!0),t.children[0].traverse(n),e.apply(n,!1))},e.volumePass=e=>{t.renderable&&t.renderable.getVisibility()&&t.context.depthMask(!e)},e.getKeyMatrices=()=>(t.renderable.getMTime()>t.keyMatrixTime.getMTime()&&(t.renderable.computeMatrix(),d(t.MCWCMatrix,t.renderable.getMatrix()),m(t.MCWCMatrix,t.MCWCMatrix),t.renderable.getIsIdentity()?pe(t.normalMatrix):(se(t.normalMatrix,t.MCWCMatrix),ge(t.normalMatrix,t.normalMatrix),fe(t.normalMatrix,t.normalMatrix)),t.keyMatrixTime.modified()),{mcwc:t.MCWCMatrix,normalMatrix:t.normalMatrix})}rn("vtkImageSlice",Mg);const Dg={};const Vg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dg,n),Zt.extend(e,t,n),t.keyMatrixTime={},yt(t.keyMatrixTime,{mtime:0}),t.normalMatrix=new Float64Array(9),t.MCWCMatrix=new Float64Array(16),At(e,t,["context"]),Eg(e,t)}),"vtkOpenGLVolume");rn("vtkVolume",Vg);const Lg={NEAREST:0,LINEAR:1,FAST_LINEAR:2},Bg={FRACTIONAL:0,PROPORTIONAL:1},Ng={CUSTOM:0,ADDITIVE:1,COLORIZE:2};var _g={InterpolationType:Lg,OpacityMode:Bg,ColorMixPreset:Ng};const Fg={COMPOSITE_BLEND:0,MAXIMUM_INTENSITY_BLEND:1,MINIMUM_INTENSITY_BLEND:2,AVERAGE_INTENSITY_BLEND:3,ADDITIVE_INTENSITY_BLEND:4,RADON_TRANSFORM_BLEND:5,LABELMAP_EDGE_PROJECTION_BLEND:6};var kg={BlendMode:Fg,FilterMode:{OFF:0,NORMALIZED:1,RAW:2}};const{vtkWarningMacro:Gg,vtkErrorMacro:Ug}=Kt;function zg(e,t,n){return e?`${e.getMTime()}-${t}-${n}`:"0"}function Wg(e,t){function n(n){[t._scalars,t._scalarOpacityFunc,t._colorTransferFunc,t._labelOutlineThicknessArray].forEach((t=>n.unregisterGraphicsResourceUser(t,e)))}t.classHierarchy.push("vtkOpenGLVolumeMapper"),e.buildPass=()=>{t.zBufferTexture=null},e.zBufferPass=(e,n)=>{if(e){const e=n.getZBufferTexture();e!==t.zBufferTexture&&(t.zBufferTexture=e)}},e.opaqueZBufferPass=(t,n)=>e.zBufferPass(t,n),e.volumePass=(r,o)=>{if(r){const r=t._openGLRenderWindow;t._openGLRenderWindow=e.getLastAncestorOfType("vtkOpenGLRenderWindow"),r&&!r.isDeleted()&&r!==t._openGLRenderWindow&&n(r),t.context=t._openGLRenderWindow.getContext(),t.tris.setOpenGLRenderWindow(t._openGLRenderWindow),t.jitterTexture.setOpenGLRenderWindow(t._openGLRenderWindow),t.framebuffer.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLVolume=e.getFirstAncestorOfType("vtkOpenGLVolume");const o=t.openGLVolume.getRenderable();t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer");const a=t._openGLRenderer.getRenderable();t.openGLCamera=t._openGLRenderer.getViewNodeFor(a.getActiveCamera()),e.renderPiece(a,o)}},e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkPolyDataVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\n\nvarying vec3 vertexVCVSOutput;\nuniform mat4 PCVCMatrix;\n\nuniform float dcxmin;\nuniform float dcxmax;\nuniform float dcymin;\nuniform float dcymax;\n\nvoid main()\n{\n  // dcsmall is the device coords reduced to the\n  // x y area covered by the volume\n  vec4 dcsmall = vec4(\n    dcxmin + 0.5 * (vertexDC.x + 1.0) * (dcxmax - dcxmin),\n    dcymin + 0.5 * (vertexDC.y + 1.0) * (dcymax - dcymin),\n    vertexDC.z,\n    vertexDC.w);\n  vec4 vcpos = PCVCMatrix * dcsmall;\n  vertexVCVSOutput = vcpos.xyz/vcpos.w;\n  gl_Position = dcsmall;\n}\n",e.Fragment="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkVolumeFS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// Template for the volume mappers fragment shader\n\n// the output of this shader\n//VTK::Output::Dec\n\nvarying vec3 vertexVCVSOutput;\n\n// first declare the settings from the mapper\n// that impact the code paths in here\n\n// always set vtkNumComponents 1,2,3,4\n//VTK::NumComponents\n\n// possibly define vtkTrilinearOn\n//VTK::TrilinearOn\n\n// possibly define UseIndependentComponents\n//VTK::IndependentComponentsOn\n\n// possibly define vtkCustomComponentsColorMix\n//VTK::CustomComponentsColorMixOn\n\n// possibly define any \"proportional\" components\n//VTK::vtkProportionalComponents\n\n// possibly define any components that are forced to nearest interpolation\n//VTK::vtkForceNearestComponents\n\n// Define the blend mode to use\n#define vtkBlendMode //VTK::BlendMode\n\n// Possibly define vtkImageLabelOutlineOn\n//VTK::ImageLabelOutlineOn\n\n// Possibly define vtkLabelEdgeProjectionOn\n//VTK::LabelEdgeProjectionOn\n\n\n#ifdef vtkImageLabelOutlineOn\n  uniform float outlineOpacity;\n  uniform float vpWidth;\n  uniform float vpHeight;\n  uniform float vpOffsetX;\n  uniform float vpOffsetY;\n  uniform mat4 PCWCMatrix;\n  uniform mat4 vWCtoIDX;\n\n  const int MAX_SEGMENT_INDEX = 256; // Define as per expected maximum\n  // bool seenSegmentsByOriginalPos[MAX_SEGMENT_INDEX];\n  #define MAX_SEGMENTS 256\n  #define UINT_SIZE 32\n  #define BITMASK_SIZE ((MAX_SEGMENTS + UINT_SIZE - 1) / UINT_SIZE)\n\n  uint bitmask[BITMASK_SIZE];\n\n  // Set the corresponding bit in the bitmask\n  void setBit(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    bitmask[index] |= 1u << bitIndex;\n  }\n\n  // Check if a bit is set in the bitmask\n  bool isBitSet(int segmentIndex) {\n    int index = segmentIndex / UINT_SIZE;\n    int bitIndex = segmentIndex % UINT_SIZE;\n    return ((bitmask[index] & (1u << bitIndex)) != 0u);\n  }\n#endif\n\n// define vtkLightComplexity\n//VTK::LightComplexity\n#if vtkLightComplexity > 0\nuniform float vSpecularPower;\nuniform float vAmbient;\nuniform float vDiffuse;\nuniform float vSpecular;\n//VTK::Light::Dec\n#endif\n\n//VTK::VolumeShadowOn\n//VTK::SurfaceShadowOn\n//VTK::localAmbientOcclusionOn\n//VTK::LAO::Dec\n//VTK::VolumeShadow::Dec\n\n// define vtkComputeNormalFromOpacity\n//VTK::vtkComputeNormalFromOpacity\n\n// possibly define vtkGradientOpacityOn\n//VTK::GradientOpacityOn\n#ifdef vtkGradientOpacityOn\nuniform float goscale0;\nuniform float goshift0;\nuniform float gomin0;\nuniform float gomax0;\n#ifdef UseIndependentComponents\n#if vtkNumComponents > 1\nuniform float goscale1;\nuniform float goshift1;\nuniform float gomin1;\nuniform float gomax1;\n#if vtkNumComponents > 2\nuniform float goscale2;\nuniform float goshift2;\nuniform float gomin2;\nuniform float gomax2;\n#if vtkNumComponents > 3\nuniform float goscale3;\nuniform float goshift3;\nuniform float gomin3;\nuniform float gomax3;\n#endif\n#endif\n#endif\n#endif\n#endif\n\n// if you want to see the raw tiled\n// data in webgl1 uncomment the following line\n// #define debugtile\n\n// camera values\nuniform float camThick;\nuniform float camNear;\nuniform float camFar;\nuniform int cameraParallel;\n\n// values describing the volume geometry\nuniform vec3 vOriginVC;\nuniform vec3 vSpacing;\nuniform ivec3 volumeDimensions; // 3d texture dimensions\nuniform vec3 vPlaneNormal0;\nuniform float vPlaneDistance0;\nuniform vec3 vPlaneNormal1;\nuniform float vPlaneDistance1;\nuniform vec3 vPlaneNormal2;\nuniform float vPlaneDistance2;\nuniform vec3 vPlaneNormal3;\nuniform float vPlaneDistance3;\nuniform vec3 vPlaneNormal4;\nuniform float vPlaneDistance4;\nuniform vec3 vPlaneNormal5;\nuniform float vPlaneDistance5;\n\n//VTK::ClipPlane::Dec\n\n// opacity and color textures\nuniform sampler2D otexture;\nuniform float oshift0;\nuniform float oscale0;\nuniform sampler2D ctexture;\nuniform float cshift0;\nuniform float cscale0;\n\n#if vtkNumComponents >= 2\nuniform float oshift1;\nuniform float oscale1;\nuniform float cshift1;\nuniform float cscale1;\n#endif\n#if vtkNumComponents >= 3\nuniform float oshift2;\nuniform float oscale2;\nuniform float cshift2;\nuniform float cscale2;\n#endif\n#if vtkNumComponents >= 4\nuniform float oshift3;\nuniform float oscale3;\nuniform float cshift3;\nuniform float cscale3;\n#endif\n\n// jitter texture\nuniform sampler2D jtexture;\nuniform sampler2D ttexture;\n\n\n// some 3D texture values\nuniform float sampleDistance;\nuniform vec3 vVCToIJK;\nuniform vec3 volumeSpacings; // spacing in the world coorindates\n\n\n// the heights defined below are the locations\n// for the up to four components of the tfuns\n// the tfuns have a height of 2XnumComps pixels so the\n// values are computed to hit the middle of the two rows\n// for that component\n#ifdef UseIndependentComponents\n#if vtkNumComponents == 1\nuniform float mix0;\n#define height0 0.5\n#endif\n#if vtkNumComponents == 2\nuniform float mix0;\nuniform float mix1;\n#define height0 0.25\n#define height1 0.75\n#endif\n#if vtkNumComponents == 3\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\n#define height0 0.17\n#define height1 0.5\n#define height2 0.83\n#endif\n#if vtkNumComponents == 4\nuniform float mix0;\nuniform float mix1;\nuniform float mix2;\nuniform float mix3;\n#define height0 0.125\n#define height1 0.375\n#define height2 0.625\n#define height3 0.875\n#endif\n#endif\n\nuniform vec4 ipScalarRangeMin;\nuniform vec4 ipScalarRangeMax;\n\n// declaration for intermixed geometry\n//VTK::ZBuffer::Dec\n\n//=======================================================================\n// global and custom variables (a temporary section before photorealistics rendering module is complete)\nvec3 rayDirVC;\nfloat sampleDistanceISVS;\nfloat sampleDistanceIS;\n\n#define SQRT3    1.7321\n#define INV4PI   0.0796\n#define EPSILON  0.001\n#define PI       3.1415\n#define PI2      9.8696\n\n//=======================================================================\n// Webgl2 specific version of functions\n#if __VERSION__ == 300\n\nuniform highp sampler3D texture1;\n\nvec4 getTextureValue(vec3 pos)\n{\n  vec4 tmp = texture(texture1, pos);\n\n  #if defined(vtkComponent0ForceNearest) || \\\n      defined(vtkComponent1ForceNearest) || \\\n      defined(vtkComponent2ForceNearest) || \\\n      defined(vtkComponent3ForceNearest)\n    vec3 nearestPos = (floor(pos * vec3(volumeDimensions)) + 0.5) / vec3(volumeDimensions);\n    vec4 nearestValue = texture(texture1, nearestPos);\n    #ifdef vtkComponent0ForceNearest\n      tmp[0] = nearestValue[0];\n    #endif\n    #ifdef vtkComponent1ForceNearest\n      tmp[1] = nearestValue[1];\n    #endif\n    #ifdef vtkComponent2ForceNearest\n      tmp[2] = nearestValue[2];\n    #endif\n    #ifdef vtkComponent3ForceNearest\n      tmp[3] = nearestValue[3];\n    #endif\n  #endif\n\n  #ifndef UseIndependentComponents\n    #if vtkNumComponents == 1\n      tmp.a = tmp.r;\n    #endif\n    #if vtkNumComponents == 2\n      tmp.a = tmp.g;\n    #endif\n    #if vtkNumComponents == 3\n      tmp.a = length(tmp.rgb);\n    #endif\n  #endif\n\n  return tmp;\n}\n\n//=======================================================================\n// WebGL1 specific version of functions\n#else\n\nuniform sampler2D texture1;\n\nuniform float texWidth;\nuniform float texHeight;\nuniform int xreps;\nuniform int xstride;\nuniform int ystride;\n\n// if computing trilinear values from multiple z slices\n#ifdef vtkTrilinearOn\nvec4 getTextureValue(vec3 ijk)\n{\n  float zoff = 1.0/float(volumeDimensions.z);\n  vec4 val1 = getOneTextureValue(ijk);\n  vec4 val2 = getOneTextureValue(vec3(ijk.xy, ijk.z + zoff));\n\n  float indexZ = float(volumeDimensions)*ijk.z;\n  float zmix =  indexZ - floor(indexZ);\n\n  return mix(val1, val2, zmix);\n}\n\nvec4 getOneTextureValue(vec3 ijk)\n#else // nearest or fast linear\nvec4 getTextureValue(vec3 ijk)\n#endif\n{\n  vec3 tdims = vec3(volumeDimensions);\n\n#ifdef debugtile\n  vec2 tpos = vec2(ijk.x, ijk.y);\n  vec4 tmp = texture2D(texture1, tpos);\n  tmp.a = 1.0;\n\n#else\n  int z = int(ijk.z * tdims.z);\n  int yz = z / xreps;\n  int xz = z - yz*xreps;\n\n  int tileWidth = volumeDimensions.x/xstride;\n  int tileHeight = volumeDimensions.y/ystride;\n\n  xz *= tileWidth;\n  yz *= tileHeight;\n\n  float ni = float(xz) + (ijk.x*float(tileWidth));\n  float nj = float(yz) + (ijk.y*float(tileHeight));\n\n  vec2 tpos = vec2(ni/texWidth, nj/texHeight);\n\n  vec4 tmp = texture2D(texture1, tpos);\n\n#if vtkNumComponents == 1\n  tmp.a = tmp.r;\n#endif\n#if vtkNumComponents == 2\n  tmp.g = tmp.a;\n#endif\n#if vtkNumComponents == 3\n  tmp.a = length(tmp.rgb);\n#endif\n#endif\n\n  return tmp;\n}\n\n// End of Webgl1 specific code\n//=======================================================================\n#endif\n\n//=======================================================================\n// transformation between VC and IS space\n\n// convert vector position from idx to vc\n#if (vtkLightComplexity > 0) || (defined vtkClippingPlanesOn)\nvec3 IStoVC(vec3 posIS){\n  vec3 posVC = posIS / vVCToIJK;\n  return posVC.x * vPlaneNormal0 +\n         posVC.y * vPlaneNormal2 +\n         posVC.z * vPlaneNormal4 +\n         vOriginVC;\n}\n\n// convert vector position from vc to idx\nvec3 VCtoIS(vec3 posVC){\n  posVC = posVC - vOriginVC;\n  posVC = vec3(\n    dot(posVC, vPlaneNormal0),\n    dot(posVC, vPlaneNormal2),\n    dot(posVC, vPlaneNormal4));\n  return posVC * vVCToIJK;\n}\n#endif\n\n//Rotate vector to view coordinate\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\nvoid rotateToViewCoord(inout vec3 dirIS){\n  dirIS.xyz =\n    dirIS.x * vPlaneNormal0 +\n    dirIS.y * vPlaneNormal2 +\n    dirIS.z * vPlaneNormal4;\n}\n\n//Rotate vector to idx coordinate\nvec3 rotateToIDX(vec3 dirVC){\n  vec3 dirIS;\n  dirIS.xyz = vec3(\n    dot(dirVC, vPlaneNormal0),\n    dot(dirVC, vPlaneNormal2),\n    dot(dirVC, vPlaneNormal4));\n  return dirIS;\n}\n#endif\n\n//=======================================================================\n// Given a normal compute the gradient opacity factors\nfloat computeGradientOpacityFactor(\n  float normalMag, float goscale, float goshift, float gomin, float gomax)\n{\n  return clamp(normalMag * goscale + goshift, gomin, gomax);\n}\n\n//=======================================================================\n// compute the normal and gradient magnitude for a position, uses forward difference\n#if (vtkLightComplexity > 0) || (defined vtkGradientOpacityOn)\n  #ifdef vtkClippingPlanesOn\n    void adjustClippedVoxelValues(vec3 pos, vec3 texPos[3], inout vec3 g1)\n    {\n      vec3 g1VC[3];\n      for (int i = 0; i < 3; ++i)\n      {\n        g1VC[i] = IStoVC(texPos[i]);\n      }\n      vec3 posVC = IStoVC(pos);\n      for (int i = 0; i < clip_numPlanes; ++i)\n      {\n        for (int j = 0; j < 3; ++j)\n        {\n          if(dot(vec3(vClipPlaneOrigins[i] - g1VC[j].xyz), vClipPlaneNormals[i]) > 0.0)\n          {\n            g1[j] = 0.0;\n          }\n        }\n      }\n    }\n  #endif\n\n  #ifdef vtkComputeNormalFromOpacity\n    vec4 computeDensityNormal(vec3 opacityUCoords[2], float opactityTextureHeight, float gradientOpacity) {\n      vec3 opacityG1, opacityG2;\n      opacityG1.x = texture2D(otexture, vec2(opacityUCoords[0].x, opactityTextureHeight)).r;\n      opacityG1.y = texture2D(otexture, vec2(opacityUCoords[0].y, opactityTextureHeight)).r;\n      opacityG1.z = texture2D(otexture, vec2(opacityUCoords[0].z, opactityTextureHeight)).r;\n      opacityG2.x = texture2D(otexture, vec2(opacityUCoords[1].x, opactityTextureHeight)).r;\n      opacityG2.y = texture2D(otexture, vec2(opacityUCoords[1].y, opactityTextureHeight)).r;\n      opacityG2.z = texture2D(otexture, vec2(opacityUCoords[1].z, opactityTextureHeight)).r;\n      opacityG1.xyz *= gradientOpacity;\n      opacityG2.xyz *= gradientOpacity;\n\n      vec4 opacityG = vec4(opacityG1 - opacityG2, 1.0f);\n      // divide by spacing\n      opacityG.xyz /= vSpacing;\n      opacityG.w = length(opacityG.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(opacityG.xyz);\n      if (!all(equal(opacityG.xyz, vec3(0.0)))) {\n        return vec4(normalize(opacityG.xyz),opacityG.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n\n    vec4 computeNormalForDensity(vec3 pos, vec3 tstep, out vec3 scalarInterp[2], const int opacityComponent)\n    {\n      vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n      vec3 yvec = vec3(0.0, tstep.y, 0.0);\n      vec3 zvec = vec3(0.0, 0.0, tstep.z);\n      vec3 texPosPVec[3];\n      texPosPVec[0] = pos + xvec;\n      texPosPVec[1] = pos + yvec;\n      texPosPVec[2] = pos + zvec;\n      vec3 texPosNVec[3];\n      texPosNVec[0] = pos - xvec;\n      texPosNVec[1] = pos - yvec;\n      texPosNVec[2] = pos - zvec;\n      vec3 g1, g2;\n\n      scalarInterp[0].x = getTextureValue(texPosPVec[0])[opacityComponent];\n      scalarInterp[0].y = getTextureValue(texPosPVec[1])[opacityComponent];\n      scalarInterp[0].z = getTextureValue(texPosPVec[2])[opacityComponent];\n      scalarInterp[1].x = getTextureValue(texPosNVec[0])[opacityComponent];\n      scalarInterp[1].y = getTextureValue(texPosNVec[1])[opacityComponent];\n      scalarInterp[1].z = getTextureValue(texPosNVec[2])[opacityComponent];\n\n      #ifdef vtkClippingPlanesOn\n        adjustClippedVoxelValues(pos, texPosPVec, scalarInterp[0]);\n        adjustClippedVoxelValues(pos, texPosNVec, scalarInterp[1]);\n      #endif\n      vec4 result;\n      result.x = scalarInterp[0].x - scalarInterp[1].x;\n      result.y = scalarInterp[0].y - scalarInterp[1].y;\n      result.z = scalarInterp[0].z - scalarInterp[1].z;\n      // divide by spacing\n      result.xyz /= vSpacing;\n      result.w = length(result.xyz);\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      if (length(result.xyz) > 0.0) {\n        return vec4(normalize(result.xyz),result.w);\n      } else {\n        return vec4(0.0);\n      }\n    }\n  #endif\n\n  // only works with dependent components\n  vec4 computeNormal(vec3 pos, vec3 tstep)\n  {\n    vec3 xvec = vec3(tstep.x, 0.0, 0.0);\n    vec3 yvec = vec3(0.0, tstep.y, 0.0);\n    vec3 zvec = vec3(0.0, 0.0, tstep.z);\n    vec3 texPosPVec[3];\n    texPosPVec[0] = pos + xvec;\n    texPosPVec[1] = pos + yvec;\n    texPosPVec[2] = pos + zvec;\n    vec3 texPosNVec[3];\n    texPosNVec[0] = pos - xvec;\n    texPosNVec[1] = pos - yvec;\n    texPosNVec[2] = pos - zvec;\n    vec3 g1, g2;\n    g1.x = getTextureValue(texPosPVec[0]).a;\n    g1.y = getTextureValue(texPosPVec[1]).a;\n    g1.z = getTextureValue(texPosPVec[2]).a;\n    g2.x = getTextureValue(texPosNVec[0]).a;\n    g2.y = getTextureValue(texPosNVec[1]).a;\n    g2.z = getTextureValue(texPosNVec[2]).a;\n    #ifdef vtkClippingPlanesOn\n      adjustClippedVoxelValues(pos, texPosPVec, g1);\n      adjustClippedVoxelValues(pos, texPosNVec, g2);\n    #endif\n    vec4 result;\n    result = vec4(g1 - g2, -1.0);\n    // divide by spacing\n    result.xyz /= vSpacing;\n    result.w = length(result.xyz);\n    if (result.w > 0.0){\n      // rotate to View Coords\n      rotateToViewCoord(result.xyz);\n      return vec4(normalize(result.xyz),result.w);\n    } else {\n      return vec4(0.0);\n    }\n  }\n#endif\n\n\n#ifdef vtkImageLabelOutlineOn\n  vec4 fragCoordToPCPos(vec4 fragCoord) {\n    return vec4(\n      (fragCoord.x / vpWidth - vpOffsetX - 0.5) * 2.0,\n      (fragCoord.y / vpHeight - vpOffsetY - 0.5) * 2.0,\n      (fragCoord.z - 0.5) * 2.0,\n      1.0);\n  }\n\n  vec4 pcPosToWorldCoord(vec4 pcPos) {\n    return PCWCMatrix * pcPos;\n  }\n\n  vec3 fragCoordToIndexSpace(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    vec4 vertex = (worldCoord / worldCoord.w);\n\n    vec3 index = (vWCtoIDX * vertex).xyz;\n\n    // half voxel fix for labelmapOutline\n    return (index + vec3(0.5)) / vec3(volumeDimensions);\n  }\n\n  vec3 fragCoordToWorld(vec4 fragCoord) {\n    vec4 pcPos = fragCoordToPCPos(fragCoord);\n    vec4 worldCoord = pcPosToWorldCoord(pcPos);\n    return worldCoord.xyz;\n  }\n#endif\n\n//=======================================================================\n// compute the normals and gradient magnitudes for a position\n// for independent components\nmat4 computeMat4Normal(vec3 pos, vec4 tValue, vec3 tstep)\n{\n  mat4 result;\n  vec4 distX = getTextureValue(pos + vec3(tstep.x, 0.0, 0.0)) - tValue;\n  vec4 distY = getTextureValue(pos + vec3(0.0, tstep.y, 0.0)) - tValue;\n  vec4 distZ = getTextureValue(pos + vec3(0.0, 0.0, tstep.z)) - tValue;\n\n  // divide by spacing\n  distX /= vSpacing.x;\n  distY /= vSpacing.y;\n  distZ /= vSpacing.z;\n\n  mat3 rot;\n  rot[0] = vPlaneNormal0;\n  rot[1] = vPlaneNormal2;\n  rot[2] = vPlaneNormal4;\n\n#if !defined(vtkComponent0Proportional)\n  result[0].xyz = vec3(distX.r, distY.r, distZ.r);\n  result[0].a = length(result[0].xyz);\n  result[0].xyz *= rot;\n  if (result[0].w > 0.0)\n  {\n    result[0].xyz /= result[0].w;\n  }\n#endif\n\n// optionally compute the 2nd component\n#if vtkNumComponents >= 2 && !defined(vtkComponent1Proportional)\n  result[1].xyz = vec3(distX.g, distY.g, distZ.g);\n  result[1].a = length(result[1].xyz);\n  result[1].xyz *= rot;\n  if (result[1].w > 0.0)\n  {\n    result[1].xyz /= result[1].w;\n  }\n#endif\n\n// optionally compute the 3rd component\n#if vtkNumComponents >= 3 && !defined(vtkComponent2Proportional)\n  result[2].xyz = vec3(distX.b, distY.b, distZ.b);\n  result[2].a = length(result[2].xyz);\n  result[2].xyz *= rot;\n  if (result[2].w > 0.0)\n  {\n    result[2].xyz /= result[2].w;\n  }\n#endif\n\n// optionally compute the 4th component\n#if vtkNumComponents >= 4 && !defined(vtkComponent3Proportional)\n  result[3].xyz = vec3(distX.a, distY.a, distZ.a);\n  result[3].a = length(result[3].xyz);\n  result[3].xyz *= rot;\n  if (result[3].w > 0.0)\n  {\n    result[3].xyz /= result[3].w;\n  }\n#endif\n\n  return result;\n}\n\n//=======================================================================\n// global shadow - secondary ray\n#if defined(VolumeShadowOn) || defined(localAmbientOcclusionOn)\nfloat random()\n{\n  float rand = fract(sin(dot(gl_FragCoord.xy,vec2(12.9898,78.233)))*43758.5453123);\n  float jitter=texture2D(jtexture,gl_FragCoord.xy/32.).r;\n  uint pcg_state = floatBitsToUint(jitter);\n  uint state = pcg_state;\n  pcg_state = pcg_state * uint(747796405) + uint(2891336453);\n  uint word = ((state >> ((state >> uint(28)) + uint(4))) ^ state) * uint(277803737);\n  return (float((((word >> uint(22)) ^ word) >> 1 ))/float(2147483647) + rand)/2.0;\n}\n#endif\n\n#ifdef VolumeShadowOn\n// henyey greenstein phase function\nfloat phase_function(float cos_angle)\n{\n  // divide by 2.0 instead of 4pi to increase intensity\n  return ((1.0-anisotropy2)/pow(1.0+anisotropy2-2.0*anisotropy*cos_angle, 1.5))/2.0;\n}\n\n// Computes the intersection between a ray and a box\nstruct Hit\n{\n  float tmin;\n  float tmax;\n};\n\nstruct Ray\n{\n  vec3 origin;\n  vec3 dir;\n  vec3 invDir;\n};\n\nbool BBoxIntersect(vec3 boundMin, vec3 boundMax, const Ray r, out Hit hit)\n{\n  vec3 tbot = r.invDir * (boundMin - r.origin);\n  vec3 ttop = r.invDir * (boundMax - r.origin);\n  vec3 tmin = min(ttop, tbot);\n  vec3 tmax = max(ttop, tbot);\n  vec2 t = max(tmin.xx, tmin.yz);\n  float t0 = max(t.x, t.y);\n  t = min(tmax.xx, tmax.yz);\n  float t1 = min(t.x, t.y);\n  hit.tmin = t0;\n  hit.tmax = t1;\n  return t1 > max(t0,0.0);\n}\n\n// As BBoxIntersect requires the inverse of the ray coords,\n// this function is used to avoid numerical issues\nvoid safe_0_vector(inout Ray ray)\n{\n  if(abs(ray.dir.x) < EPSILON) ray.dir.x = sign(ray.dir.x) * EPSILON;\n  if(abs(ray.dir.y) < EPSILON) ray.dir.y = sign(ray.dir.y) * EPSILON;\n  if(abs(ray.dir.z) < EPSILON) ray.dir.z = sign(ray.dir.z) * EPSILON;\n}\n\nfloat volume_shadow(vec3 posIS, vec3 lightDirNormIS)\n{\n  float shadow = 1.0;\n  float opacity = 0.0;\n\n  // modify sample distance with a random number between 1.5 and 3.0\n  float sampleDistanceISVS_jitter = sampleDistanceISVS * mix(1.5, 3.0, random());\n  float opacityPrev = texture2D(otexture, vec2(getTextureValue(posIS).r * oscale0 + oshift0, 0.5)).r;\n\n  // in case the first sample near surface has a very tiled light ray, we need to offset start position\n  posIS += sampleDistanceISVS_jitter * lightDirNormIS;\n\n  // compute the start and end points for the ray\n  Ray ray;\n  Hit hit;\n  ray.origin = posIS;\n  ray.dir = lightDirNormIS;\n  safe_0_vector(ray);\n  ray.invDir = 1.0/ray.dir;\n\n  if(!BBoxIntersect(vec3(0.0),vec3(1.0), ray, hit))\n  {\n    return 1.0;\n  }\n  float maxdist = hit.tmax;\n\n  // interpolate shadow ray length between: 1 unit of sample distance in IS to SQRT3, based on globalIlluminationReach\n  float maxgi = mix(sampleDistanceISVS_jitter,SQRT3,giReach);\n  maxdist = min(maxdist,maxgi);\n  if(maxdist < EPSILON) {\n    return 1.0;\n  }\n\n  float current_dist = 0.0;\n  float current_step = length(sampleDistanceISVS_jitter * lightDirNormIS);\n  float clamped_step = 0.0;\n\n  vec4 scalar = vec4(0.0);\n  while(current_dist < maxdist)\n  {\n#ifdef vtkClippingPlanesOn\n    vec3 posVC = IStoVC(posIS);\n    for (int i = 0; i < clip_numPlanes; ++i)\n    {\n      if (dot(vec3(vClipPlaneOrigins[i] - posVC), vClipPlaneNormals[i]) > 0.0)\n      {\n        current_dist = maxdist;\n      }\n    }\n#endif\n    scalar = getTextureValue(posIS);\n    opacity = texture2D(otexture, vec2(scalar.r * oscale0 + oshift0, 0.5)).r;\n    #if defined(vtkGradientOpacityOn) && !defined(UseIndependentComponents)\n      vec4 normal = computeNormal(posIS, vec3(1.0/vec3(volumeDimensions)));\n      opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n    #endif\n    shadow *= 1.0 - opacity;\n\n    // optimization: early termination\n    if (shadow < EPSILON){\n      return 0.0;\n    }\n\n    clamped_step = min(maxdist - current_dist, current_step);\n    posIS += clamped_step * lightDirNormIS;\n    current_dist += current_step;\n  }\n\n  return shadow;\n}\n\nvec3 applyShadowRay(vec3 tColor, vec3 posIS, vec3 viewDirectionVC)\n{\n  vec3 vertLight = vec3(0.0);\n  vec3 secondary_contrib = vec3(0.0);\n  // here we assume only positional light, no effect of cones\n  for (int i = 0; i < lightNum; i++)\n  {\n    #if(vtkLightComplexity==3)\n      if (lightPositional[i] == 1){\n        vertLight = lightPositionVC[i] - IStoVC(posIS);\n      }else{\n        vertLight = - lightDirectionVC[i];\n      }\n    #else\n      vertLight = - lightDirectionVC[i];\n    #endif\n    // here we assume achromatic light, only intensity\n    float dDotL = dot(viewDirectionVC, normalize(vertLight));\n    // isotropic scatter returns 0.5 instead of 1/4pi to increase intensity\n    float phase_attenuation = 0.5;\n    if (abs(anisotropy) > EPSILON){\n      phase_attenuation = phase_function(dDotL);\n    }\n    float vol_shadow = volume_shadow(posIS, normalize(rotateToIDX(vertLight)));\n    secondary_contrib += tColor * vDiffuse * lightColor[i] * vol_shadow * phase_attenuation;\n    secondary_contrib += tColor * vAmbient;\n  }\n  return secondary_contrib;\n}\n#endif\n\n//=======================================================================\n// local ambient occlusion\n#ifdef localAmbientOcclusionOn\nvec3 sample_direction_uniform(int i)\n{\n  float rand = random() * 0.5;\n  float theta = PI2 * (kernelSample[i][0] + rand);\n  float phi = acos(2.0 * (kernelSample[i][1] + rand) -1.0) / 2.5;\n  return normalize(vec3(cos(theta)*sin(phi), sin(theta)*sin(phi), cos(phi)));\n}\n\n// return a matrix that transform startDir into z axis; startDir should be normalized\nmat3 zBaseRotationalMatrix(vec3 startDir){\n  vec3 axis = cross(startDir, vec3(0.0,0.0,1.0));\n  float cosA = startDir.z;\n  float k = 1.0 / (1.0 + cosA);\n  mat3 matrix = mat3((axis.x * axis.x * k) + cosA, (axis.y * axis.x * k) - axis.z, (axis.z * axis.x * k) + axis.y,\n              (axis.x * axis.y * k) + axis.z, (axis.y * axis.y * k) + cosA, (axis.z * axis.y * k) - axis.x,\n              (axis.x * axis.z * k) - axis.y, (axis.y * axis.z * k) + axis.x, (axis.z * axis.z * k) + cosA);\n  return matrix;\n}\n\nfloat computeLAO(vec3 posIS, float op, vec3 lightDir, vec4 normal){\n  // apply LAO only at selected locations, otherwise return full brightness\n  if (normal.w > 0.0 && op > 0.05){\n    float total_transmittance = 0.0;\n    mat3 inverseRotateBasis = inverse(zBaseRotationalMatrix(normalize(-normal.xyz)));\n    vec3 currPos, randomDirStep;\n    float weight, transmittance, opacity;\n    for (int i = 0; i < kernelSize; i++)\n    {\n      randomDirStep = inverseRotateBasis * sample_direction_uniform(i) * sampleDistanceIS;\n      weight = 1.0 - dot(normalize(lightDir), normalize(randomDirStep));\n      currPos = posIS;\n      transmittance = 1.0;\n      for (int j = 0; j < kernelRadius ; j++){\n        currPos += randomDirStep;\n        // check if it's at clipping plane, if so return full brightness\n        if (all(greaterThan(currPos, vec3(EPSILON))) && all(lessThan(currPos,vec3(1.0-EPSILON)))){\n          opacity = texture2D(otexture, vec2(getTextureValue(currPos).r * oscale0 + oshift0, 0.5)).r;\n          #ifdef vtkGradientOpacityOn\n             opacity *= computeGradientOpacityFactor(normal.w, goscale0, goshift0, gomin0, gomax0);\n          #endif\n          transmittance *= 1.0 - opacity;\n        }\n        else{\n          break;\n        }\n      }\n      total_transmittance += transmittance / float(kernelRadius) * weight;\n\n      // early termination if fully translucent\n      if (total_transmittance > 1.0 - EPSILON){\n        return 1.0;\n      }\n    }\n    // average transmittance and reduce variance\n    return clamp(total_transmittance / float(kernelSize), 0.3, 1.0);\n  } else {\n    return 1.0;\n  }\n}\n#endif\n\n//=======================================================================\n// surface light contribution\n#if vtkLightComplexity > 0\n  void applyLighting(inout vec3 tColor, vec4 normal)\n  {\n    vec3 diffuse = vec3(0.0, 0.0, 0.0);\n    vec3 specular = vec3(0.0, 0.0, 0.0);\n    float df, sf = 0.0;\n    for (int i = 0; i < lightNum; i++){\n        df = abs(dot(normal.rgb, -lightDirectionVC[i]));\n        diffuse += df * lightColor[i];\n        sf = pow( abs(dot(lightHalfAngleVC[i],normal.rgb)), vSpecularPower);\n        specular += sf * lightColor[i];\n    }\n    tColor.rgb = tColor.rgb*(diffuse*vDiffuse + vAmbient) + specular*vSpecular;\n  }\n  #ifdef SurfaceShadowOn\n  #if vtkLightComplexity < 3\n    vec3 applyLightingDirectional(vec3 posIS, vec4 tColor, vec4 normal)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float ndotL,vdotR;\n        vertLightDirection = lightDirectionVC[i];\n        ndotL = dot(normal.xyz, vertLightDirection);\n        if (ndotL < 0.0 && twoSidedLighting)\n        {\n          ndotL = -ndotL;\n        }\n        if (ndotL > 0.0)\n        {\n          diffuse += ndotL * lightColor[i];\n          //specular\n          vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n          if (vdotR > 0.0)\n          {\n            specular += pow(vdotR, vSpecularPower) * lightColor[i];\n          }\n        }\n        #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n        #endif\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #else\n    vec3 applyLightingPositional(vec3 posIS, vec4 tColor, vec4 normal, vec3 posVC)\n    {\n      // everything in VC\n      vec3 diffuse = vec3(0.0);\n      vec3 specular = vec3(0.0);\n      #ifdef localAmbientOcclusionOn\n        vec3 ambient = vec3(0.0);\n      #endif\n      vec3 vertLightDirection;\n      for (int i = 0; i < lightNum; i++){\n        float distance,attenuation,ndotL,vdotR;\n        vec3 lightDir;\n        if (lightPositional[i] == 1){\n          lightDir = lightDirectionVC[i];\n          vertLightDirection = posVC - lightPositionVC[i];\n          distance = length(vertLightDirection);\n          vertLightDirection = normalize(vertLightDirection);\n          attenuation = 1.0 / (lightAttenuation[i].x\n                              + lightAttenuation[i].y * distance\n                              + lightAttenuation[i].z * distance * distance);\n          // per OpenGL standard cone angle is 90 or less for a spot light\n          if (lightConeAngle[i] <= 90.0){\n            float coneDot = dot(vertLightDirection, lightDir);\n            if (coneDot >= cos(radians(lightConeAngle[i]))){  // if inside cone\n              attenuation = attenuation * pow(coneDot, lightExponent[i]);\n            }\n            else {\n              attenuation = 0.0;\n            }\n          }\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * attenuation * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * attenuation * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        } else {\n          vertLightDirection = lightDirectionVC[i];\n          ndotL = dot(normal.xyz, vertLightDirection);\n          if (ndotL < 0.0 && twoSidedLighting)\n          {\n            ndotL = -ndotL;\n          }\n          if (ndotL > 0.0)\n          {\n            diffuse += ndotL * lightColor[i];\n            //specular\n            vdotR = dot(-rayDirVC, normalize(2.0 * ndotL * -normal.xyz + vertLightDirection));\n            if (vdotR > 0.0)\n            {\n              specular += pow(vdotR, vSpecularPower) * lightColor[i];\n            }\n          }\n          #ifdef localAmbientOcclusionOn\n            ambient += computeLAO(posIS, tColor.a, vertLightDirection, normal);\n          #endif\n        }\n      }\n      #ifdef localAmbientOcclusionOn\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient * ambient) + specular*vSpecular;\n      #else\n        return tColor.rgb * (diffuse * vDiffuse + vAmbient) + specular*vSpecular;\n      #endif\n    }\n  #endif\n  #endif\n#endif\n\n// LAO of surface shadows and volume shadows only work with dependent components\nvec3 applyAllLightning(vec3 tColor, float alpha, vec3 posIS, vec4 normalLight) {\n  #if vtkLightComplexity > 0\n    // surface shadows if needed\n    #ifdef SurfaceShadowOn\n      #if vtkLightComplexity < 3\n        vec3 tColorS = applyLightingDirectional(posIS, vec4(tColor, alpha), normalLight);\n      #else\n        vec3 tColorS = applyLightingPositional(posIS, vec4(tColor, alpha), normalLight, IStoVC(posIS));\n      #endif\n    #endif\n\n    // volume shadows if needed\n    #ifdef VolumeShadowOn\n      vec3 tColorVS = applyShadowRay(tColor, posIS, rayDirVC);\n    #endif\n\n    // merge\n    #ifdef VolumeShadowOn\n      #ifdef SurfaceShadowOn\n        // surface shadows + volumetric shadows\n        float vol_coef = volumetricScatteringBlending * (1.0 - alpha / 2.0) * (1.0 - atan(normalLight.w) * INV4PI);\n        tColor = (1.0-vol_coef) * tColorS + vol_coef * tColorVS;\n      #else\n        // volumetric shadows only\n        tColor = tColorVS;\n      #endif\n    #else\n      #ifdef SurfaceShadowOn\n        // surface shadows only\n        tColor = tColorS;\n      #else\n        // no shadows\n        applyLighting(tColor, normal3);\n      #endif\n    #endif\n  #endif\n  return tColor;\n}\n\n  \nvec4 getColorForValue(vec4 tValue, vec3 posIS, vec3 tstep)\n{\n\n// If labeloutline and not the edge labelmap, since in the edge labelmap blend\n// we need the underlying data to sample through\n#if defined(vtkImageLabelOutlineOn) && !defined(vtkLabelEdgeProjectionOn)\n  vec3 centerPosIS = fragCoordToIndexSpace(gl_FragCoord); // pos in texture space\n  vec4 centerValue = getTextureValue(centerPosIS);\n  bool pixelOnBorder = false;\n  vec4 tColor = texture2D(ctexture, vec2(centerValue.r * cscale0 + cshift0, 0.5));\n\n  // Get alpha of segment from opacity function.\n  tColor.a = texture2D(otexture, vec2(centerValue.r * oscale0 + oshift0, 0.5)).r;\n\n  int segmentIndex = int(centerValue.r * 255.0);\n  \n  // Use texture sampling for outlineThickness\n  float textureCoordinate = float(segmentIndex - 1) / 1024.0;\n  float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n  int actualThickness = int(textureValue * 255.0);\n\n\n  // If it is the background (segment index 0), we should quickly bail out. \n  // Previously, this was determined by tColor.a, which was incorrect as it\n  // prevented the outline from appearing when the fill is 0.\n  if (segmentIndex == 0){\n    return vec4(0, 0, 0, 0);\n  }\n\n  // Only perform outline check on fragments rendering voxels that aren't invisible.\n  // Saves a bunch of needless checks on the background.\n  // TODO define epsilon when building shader?\n  for (int i = -actualThickness; i <= actualThickness; i++) {\n    for (int j = -actualThickness; j <= actualThickness; j++) {\n      if (i == 0 || j == 0) {\n        continue;\n      }\n\n      vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i),\n        gl_FragCoord.y + float(j),\n        gl_FragCoord.z, gl_FragCoord.w);\n\n      vec3 neighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n      vec4 value = getTextureValue(neighborPosIS);\n\n      // If any of my neighbours are not the same value as I\n      // am, this means I am on the border of the segment.\n      // We can break the loops\n      if (any(notEqual(value, centerValue))) {\n        pixelOnBorder = true;\n        break;\n      }\n    }\n\n    if (pixelOnBorder == true) {\n      break;\n    }\n  }\n\n  // If I am on the border, I am displayed at full opacity\n  if (pixelOnBorder == true) {\n    tColor.a = outlineOpacity;\n  }\n\n  return tColor;\n\n#else\n  // compute the normal and gradient magnitude if needed\n  // We compute it as a vec4 if possible otherwise a mat4\n\n  #ifdef UseIndependentComponents\n\n    // sample textures\n    vec3 tColor0 = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, height0)).r;\n\n    #if vtkNumComponents > 1\n      vec3 tColor1 = texture2D(ctexture, vec2(tValue.g * cscale1 + cshift1, height1)).rgb;\n      float pwfValue1 = texture2D(otexture, vec2(tValue.g * oscale1 + oshift1, height1)).r;\n\n      #if vtkNumComponents > 2\n        vec3 tColor2 = texture2D(ctexture, vec2(tValue.b * cscale2 + cshift2, height2)).rgb;\n        float pwfValue2 = texture2D(otexture, vec2(tValue.b * oscale2 + oshift2, height2)).r;\n\n        #if vtkNumComponents > 3\n          vec3 tColor3 = texture2D(ctexture, vec2(tValue.a * cscale3 + cshift3, height3)).rgb;\n          float pwfValue3 = texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, height3)).r;\n        #endif\n      #endif\n    #endif\n\n    #if !defined(vtkCustomComponentsColorMix)\n      // default path for component color mix\n\n      // compute the normal vectors as needed\n      #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n      #endif\n\n      // compute gradient opacity factors as needed\n      vec4 goFactor = vec4(1.0, 1.0 ,1.0 ,1.0);\n      #if defined(vtkGradientOpacityOn)\n        #if !defined(vtkComponent0Proportional)\n          goFactor.x =\n            computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n        #endif\n        #if vtkNumComponents > 1\n          #if !defined(vtkComponent1Proportional)\n            goFactor.y =\n              computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          #endif\n          #if vtkNumComponents > 2\n            #if !defined(vtkComponent2Proportional)\n              goFactor.z =\n                computeGradientOpacityFactor(normalMat[2].a, goscale2, goshift2, gomin2, gomax2);\n            #endif\n            #if vtkNumComponents > 3\n              #if !defined(vtkComponent3Proportional)\n                goFactor.w =\n                  computeGradientOpacityFactor(normalMat[3].a, goscale3, goshift3, gomin3, gomax3);\n              #endif\n            #endif\n          #endif\n        #endif\n      #endif\n\n      // process color and opacity for each component\n      #if !defined(vtkComponent0Proportional)\n        float alpha = goFactor.x*mix0*pwfValue0;\n        #if vtkLightComplexity > 0\n          applyLighting(tColor0, normalMat[0]);\n        #endif\n      #else\n        tColor0 *= pwfValue0;\n        float alpha = mix(pwfValue0, 1.0, (1.0 - mix0));\n      #endif\n\n      #if vtkNumComponents > 1\n        #if !defined(vtkComponent1Proportional)\n          alpha += goFactor.y*mix1*pwfValue1;\n          #if vtkLightComplexity > 0\n            applyLighting(tColor1, normalMat[1]);\n          #endif\n        #else\n          tColor1 *= pwfValue1;\n          alpha *= mix(pwfValue1, 1.0, (1.0 - mix1));\n        #endif\n\n        #if vtkNumComponents > 2\n          #if !defined(vtkComponent2Proportional)\n            alpha += goFactor.z*mix2*pwfValue2;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor2, normalMat[2]);\n            #endif\n          #else\n            tColor2 *= pwfValue2;\n            alpha *= mix(pwfValue2, 1.0, (1.0 - mix2));\n          #endif\n        #endif\n\n        #if vtkNumComponents > 3\n          #if !defined(vtkComponent3Proportional)\n            alpha += goFactor.w*mix3*pwfValue3;\n            #if vtkLightComplexity > 0\n              applyLighting(tColor3, normalMat[3]);\n            #endif\n          #else\n            tColor3 *= pwfValue3;\n            alpha *= mix(pwfValue3, 1.0, (1.0 - mix3));\n          #endif\n        #endif\n      #endif\n\n      // perform final independent blend\n      vec3 tColor = mix0 * tColor0;\n      #if vtkNumComponents > 1\n        tColor += mix1 * tColor1;\n        #if vtkNumComponents > 2\n          tColor += mix2 * tColor2;\n          #if vtkNumComponents > 3\n            tColor += mix3 * tColor3;\n          #endif\n        #endif\n      #endif\n\n      return vec4(tColor, alpha);\n    #else\n      /*\n       * Mix the color information from all the independent components to get a single rgba output\n       * Gradient opactity factors and normals are not computed\n       *\n       * You can compute these using:\n       * - computeMat4Normal: always available, compute normal only for non proportional components, used by default independent component mix\n       * - computeDensityNormal & computeNormalForDensity: available if ((LightComplexity > 0) || GradientOpacityOn) && ComputeNormalFromOpacity),\n       *                                                   used by dependent component color mix, see code for Additive preset in OpenGl/VolumeMapper\n       * - computeGradientOpacityFactor: always available, used in a lot of places\n       *\n       * Using applyAllLightning() is advised for shading but some features don't work well with it (volume shadows, LAO)\n       * mix0, mix1, ... are defined for each component that is used and correspond to the componentWeight\n       */\n      //VTK::CustomComponentsColorMix::Impl\n    #endif\n  #else\n    // dependent components\n\n    // compute normal if needed\n    #if (vtkLightComplexity > 0) || defined(vtkGradientOpacityOn)\n      // use component 3 of the opacity texture as getTextureValue() sets alpha to the opacity value\n      #ifdef vtkComputeNormalFromOpacity\n        vec3 scalarInterp[2];\n        vec4 normal0 = computeNormalForDensity(posIS, tstep, scalarInterp, 3);\n      #else\n        vec4 normal0 = computeNormal(posIS, tstep);\n      #endif\n    #endif\n\n    // compute gradient opacity factor enabled\n    #if defined(vtkGradientOpacityOn)\n      float gradientOpacity = computeGradientOpacityFactor(normal0.a, goscale0, goshift0, gomin0, gomax0);\n    #else\n      const float gradientOpacity = 1.0;\n    #endif\n\n    // get color and opacity\n    #if vtkNumComponents == 1\n      vec3 tColor = texture2D(ctexture, vec2(tValue.r * cscale0 + cshift0, 0.5)).rgb;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      if (alpha < EPSILON){\n        return vec4(0.0);\n      }\n    #endif\n    #if vtkNumComponents == 2\n      vec3 tColor = vec3(tValue.r * cscale0 + cshift0);\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale1 + oshift1, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 3\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale0 + oshift0, 0.5)).r;\n    #endif\n    #if vtkNumComponents == 4\n      vec3 tColor;\n      tColor.r = tValue.r * cscale0 + cshift0;\n      tColor.g = tValue.g * cscale1 + cshift1;\n      tColor.b = tValue.b * cscale2 + cshift2;\n      float alpha = gradientOpacity*texture2D(otexture, vec2(tValue.a * oscale3 + oshift3, 0.5)).r;\n    #endif\n\n    // lighting\n    #if (vtkLightComplexity > 0)\n      #ifdef vtkComputeNormalFromOpacity\n        vec4 normalLight;\n        if (!all(equal(normal0, vec4(0.0)))) {\n          scalarInterp[0] = scalarInterp[0] * oscale0 + oshift0;\n          scalarInterp[1] = scalarInterp[1] * oscale0 + oshift0;\n          normalLight = computeDensityNormal(scalarInterp, 0.5, gradientOpacity);\n          if (all(equal(normalLight, vec4(0.0)))) {\n            normalLight = normal0;\n          }\n        }\n      #else\n        vec4 normalLight = normal0;\n      #endif\n      tColor = applyAllLightning(tColor, alpha, posIS, normalLight);\n    #endif\n\n    return vec4(tColor, alpha);\n  #endif // dependent\n#endif\n}\n\nbool valueWithinScalarRange(vec4 val, vec4 min, vec4 max) {\n  bool withinRange = false;\n  #if vtkNumComponents == 1\n    if (val.r >= min.r && val.r <= max.r) {\n      withinRange = true;\n    }\n  #else\n    #ifdef UseIndependentComponents\n      #if vtkNumComponents == 2\n        if (val.r >= min.r && val.r <= max.r &&\n            val.g >= min.g && val.g <= max.g) {\n          withinRange = true;\n        }\n      #else\n        if (all(greaterThanEqual(val, ipScalarRangeMin)) &&\n            all(lessThanEqual(val, ipScalarRangeMax))) {\n          withinRange = true;\n        }\n      #endif\n    #endif\n  #endif\n  return withinRange;\n}\n\n#if vtkBlendMode == 6 \nbool checkOnEdgeForNeighbor(int i, int j, int s, vec3 stepIS) {\n    vec4 neighborPixelCoord = vec4(gl_FragCoord.x + float(i), gl_FragCoord.y + float(j), gl_FragCoord.z, gl_FragCoord.w);\n    vec3 originalNeighborPosIS = fragCoordToIndexSpace(neighborPixelCoord);\n\n    bool justSawIt = false;\n\n    vec3 neighborPosIS = originalNeighborPosIS;\n\n    float stepsTraveled = 0.0;\n\n\n    // float neighborValue;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS += stepIS;\n    }\n\n    if (justSawIt){\n      return false;\n    }\n\n   \n    neighborPosIS = originalNeighborPosIS;\n    for (int k = 0; k < //VTK::MaximumSamplesValue /2 ; ++k) {\n        ivec3 texCoord = ivec3(neighborPosIS * vec3(volumeDimensions));\n        vec4 texValue = texelFetch(texture1, texCoord, 0);\n\n        if (int(texValue.g) == s) {\n            justSawIt = true;\n            break;\n        }\n        neighborPosIS -= stepIS;\n    }\n\n\n    if (!justSawIt) {\n        // onedge\n        vec3 tColorSegment = texture2D(ctexture, vec2(float(s) * cscale1 + cshift1, height1)).rgb;\n        float pwfValueSegment = texture2D(otexture, vec2(float(s) * oscale1 + oshift1, height1)).r;\n        gl_FragData[0] = vec4(tColorSegment, pwfValueSegment);\n        return true;\n    }\n\n    // not on edge\n    return false;\n}\n\n#endif\n\n\n//=======================================================================\n// Apply the specified blend mode operation along the ray's path.\n//\nvoid applyBlend(vec3 posIS, vec3 endIS, vec3 tdims)\n{\n  vec3 tstep = 1.0/tdims;\n\n  // start slightly inside and apply some jitter\n  vec3 delta = endIS - posIS;\n  vec3 stepIS = normalize(delta)*sampleDistanceIS;\n  float raySteps = length(delta)/sampleDistanceIS;\n\n  // Initialize arrays to false\n  // avoid 0.0 jitter\n  float jitter = 0.01 + 0.99*texture2D(jtexture, gl_FragCoord.xy/32.0).r;\n  float stepsTraveled = jitter;\n\n  // local vars for the loop\n  vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\n  vec4 tValue;\n  vec4 tColor;\n\n  // if we have less than one step then pick the middle point\n  // as our value\n  // if (raySteps <= 1.0)\n  // {\n  //   posIS = (posIS + endIS)*0.5;\n  // }\n\n  // Perform initial step at the volume boundary\n  // compute the scalar\n  tValue = getTextureValue(posIS);\n  \n  #if vtkBlendMode == 6 \n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n    vec3 maxPosIS = posIS; // Store the position of the max value\n    int segmentIndex = int(value.g);\n    bool originalPosHasSeenNonZero = false;\n\n    uint bitmask = 0u;\n\n    if (segmentIndex != 0) {\n      // Tried using the segment index in an boolean array but reading \n      // from the array by dynamic indexing was horrondously slow\n      // so use bit masking instead and assign 1 to the bit corresponding to the segment index\n      // and later check if the bit is set via bit operations\n      setBit(segmentIndex);\n    }\n    \n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n      segmentIndex = int(tValue.g);\n\n      if (segmentIndex != 0) {\n        originalPosHasSeenNonZero = true;\n        setBit(segmentIndex);\n      }\n\n      if (tValue.r > value.r) {\n        value =  tValue; // Update the max value\n        maxPosIS = posIS; // Update the position where max occurred\n      }\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n\n    if (tValue.r > value.r) {\n      value = tValue; // Update the max value\n      maxPosIS = posIS; // Update the position where max occurred\n    }  \n\n    // If we have not seen any non-zero segments, we can return early\n    // and grab color from the actual center value first component (image)\n    if (!originalPosHasSeenNonZero) {\n      gl_FragData[0] = getColorForValue(value, maxPosIS, tstep);\n      return;\n    }\n\n    // probably we can make this configurable but for now we will use the same\n    // sample distance as the original sample distance\n    float neighborSampleDistanceIS = sampleDistanceIS;\n\n    vec3 neighborRayStepsIS = stepIS;\n    float neighborRaySteps = raySteps;\n    bool shouldLookInAllNeighbors = false;\n\n    float minVoxelSpacing = min(volumeSpacings[0], min(volumeSpacings[1], volumeSpacings[2]));\n    vec4 base = vec4(gl_FragCoord.x, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec4 baseXPlus = vec4(gl_FragCoord.x + 1.0, gl_FragCoord.y, gl_FragCoord.z, gl_FragCoord.w);\n    vec4 baseYPlus = vec4(gl_FragCoord.x, gl_FragCoord.y + 1.0, gl_FragCoord.z, gl_FragCoord.w);\n\n    vec3 baseWorld = fragCoordToWorld(base);\n    vec3 baseXPlusWorld = fragCoordToWorld(baseXPlus);\n    vec3 baseYPlusWorld = fragCoordToWorld(baseYPlus);\n\n    float XPlusDiff = length(baseXPlusWorld - baseWorld);\n    float YPlusDiff = length(baseYPlusWorld - baseWorld);\n\n    float minFragSpacingWorld = min(XPlusDiff, YPlusDiff);\n\n    for (int s = 1; s < MAX_SEGMENT_INDEX; s++) {\n      // bail out quickly if the segment index has not \n      // been seen by the center segment\n      if (!isBitSet(s)) {\n       continue;\n      }\n\n      // Use texture sampling for outlineThickness so that we can have \n      // per segment thickness\n      float textureCoordinate = float(s - 1) / 1024.0;\n      float textureValue = texture2D(ttexture, vec2(textureCoordinate, 0.5)).r;\n\n      int actualThickness = int(textureValue * 255.0);\n\n      // check the extreme points in the neighborhood since there is a better\n      // chance of finding the edge there, so that we can bail out \n      // faster if we find the edge\n      bool onEdge =\n          checkOnEdgeForNeighbor(-actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(actualThickness, -actualThickness, s, stepIS) ||\n          checkOnEdgeForNeighbor(-actualThickness, +actualThickness, s, stepIS);\n\n      if (onEdge) {\n        return;\n      }\n\n      // since the next step is computationally expensive, we need to perform\n      // some optimizations to avoid it if possible. One of the optimizations\n      // is to check the whether the minimum of the voxel spacing is greater than \n      // the 2 * the thickness of the outline segment. If that is the case\n      // then we can safely skip the next step since we can be sure that the\n      // the previous 4 checks on the extreme points would caught the entirety \n      // of the all the fragments inside. i.e., this happens when we zoom out, \n      if (minVoxelSpacing > (2.0 * float(actualThickness) - 1.0) * minFragSpacingWorld) {\n        continue;\n      }\n      \n      // Loop through the rest, skipping the processed extremes and the center\n      for (int i = -actualThickness; i <= actualThickness; i++) {\n            for (int j = -actualThickness; j <= actualThickness; j++) {\n                if (i == 0 && j == 0) continue; // Skip the center\n                if (abs(i) == actualThickness && abs(j) == actualThickness) continue; // Skip corners\n                if (checkOnEdgeForNeighbor(i, j, s, stepIS )) {\n                    return;\n                }\n          }\n      }\n    }\n\n    vec3 tColor0 = texture2D(ctexture, vec2(value.r * cscale0 + cshift0, height0)).rgb;\n    float pwfValue0 = texture2D(otexture, vec2(value.r * oscale0 + oshift0, height0)).r;\n    gl_FragData[0] = vec4(tColor0, pwfValue0);\n  #endif\n  #if vtkBlendMode == 0 // COMPOSITE_BLEND\n    // now map through opacity and color\n    tColor = getColorForValue(tValue, posIS, tstep);\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps);\n      gl_FragData[0] = tColor;\n      return;\n    }\n\n    tColor.a = 1.0 - pow(1.0 - tColor.a, jitter);\n    color = vec4(tColor.rgb*tColor.a, tColor.a);\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n\n      float mix = (1.0 - color.a);\n\n      // this line should not be needed but nvidia seems to not handle\n      // the break correctly on windows/chrome 58 angle\n      //mix = mix * sign(max(raySteps - stepsTraveled - 1.0, 0.0));\n\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n      stepsTraveled++;\n      posIS += stepIS;\n      if (color.a > 0.99) { color.a = 1.0; break; }\n    }\n\n    if (color.a < 0.99 && (raySteps - stepsTraveled) > 0.0)\n    {\n      posIS = endIS;\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // now map through opacity and color\n      tColor = getColorForValue(tValue, posIS, tstep);\n      tColor.a = 1.0 - pow(1.0 - tColor.a, raySteps - stepsTraveled);\n\n      float mix = (1.0 - color.a);\n      color = color + vec4(tColor.rgb*tColor.a, tColor.a)*mix;\n    }\n\n    gl_FragData[0] = vec4(color.rgb/color.a, color.a);\n  #endif\n  #if vtkBlendMode == 1 || vtkBlendMode == 2\n    // MAXIMUM_INTENSITY_BLEND || MINIMUM_INTENSITY_BLEND\n    // Find maximum/minimum intensity along the ray.\n\n    // Define the operation we will use (min or max)\n    #if vtkBlendMode == 1\n    #define OP max\n    #else\n    #define OP min\n    #endif\n\n    // If the clipping range is shorter than the sample distance\n    // we can skip the sampling loop along the ray.\n    if (raySteps <= 1.0)\n    {\n      gl_FragData[0] = getColorForValue(tValue, posIS, tstep);\n      return;\n    }\n\n    vec4 value = tValue;\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // Update the maximum value if necessary\n      value = OP(tValue, value);\n\n      // Otherwise, continue along the ray\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n    tValue = getTextureValue(posIS);\n    value = OP(tValue, value);\n\n    // Now map through opacity and color\n    gl_FragData[0] = getColorForValue(value, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 3 || vtkBlendMode == 4 //AVERAGE_INTENSITY_BLEND || ADDITIVE_BLEND\n    vec4 sum = vec4(0.);\n\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n    }\n\n    if (raySteps <= 1.0) {\n      gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    // Sample along the ray until MaximumSamplesValue,\n    // ending slightly inside the total distance\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      // If we have reached the last step, break\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar\n      tValue = getTextureValue(posIS);\n\n      // One can control the scalar range by setting the AverageIPScalarRange to disregard scalar values, not in the range of interest, from the average computation.\n      // Notes:\n      // - We are comparing all values in the texture to see if any of them\n      //   are outside of the scalar range. In the future we might want to allow\n      //   scalar ranges for each component.\n      if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n        // Sum the values across each step in the path\n        sum += tValue;\n      }\n      stepsTraveled++;\n      posIS += stepIS;\n    }\n\n    // Perform the last step along the ray using the\n    // residual distance\n    posIS = endIS;\n\n    // compute the scalar\n    tValue = getTextureValue(posIS);\n\n    // One can control the scalar range by setting the IPScalarRange to disregard scalar values, not in the range of interest, from the average computation\n    if (valueWithinScalarRange(tValue, ipScalarRangeMin, ipScalarRangeMax)) {\n      sum += tValue;\n\n      stepsTraveled++;\n    }\n\n    #if vtkBlendMode == 3 // Average\n      sum /= vec4(stepsTraveled, stepsTraveled, stepsTraveled, 1.0);\n    #endif\n\n    gl_FragData[0] = getColorForValue(sum, posIS, tstep);\n  #endif\n  #if vtkBlendMode == 5 // RADON\n    float normalizedRayIntensity = 1.0;\n\n    // handle very thin volumes\n    if (raySteps <= 1.0)\n    {\n      tValue = getTextureValue(posIS);\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n      gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity, 0.5));\n      return;\n    }\n\n    posIS += (jitter*stepIS);\n\n    for (int i = 0; i < //VTK::MaximumSamplesValue ; ++i)\n    {\n      if (stepsTraveled + 1.0 >= raySteps) { break; }\n\n      // compute the scalar value\n      tValue = getTextureValue(posIS);\n\n      // Convert scalar value to normalizedRayIntensity coefficient and accumulate normalizedRayIntensity\n      normalizedRayIntensity = normalizedRayIntensity - sampleDistance*texture2D(otexture, vec2(tValue.r * oscale0 + oshift0, 0.5)).r;\n\n      posIS += stepIS;\n      stepsTraveled++;\n    }\n\n    // map normalizedRayIntensity to color\n    gl_FragData[0] = texture2D(ctexture, vec2(normalizedRayIntensity , 0.5));\n\n  #endif\n}\n\n//=======================================================================\n// Compute a new start and end point for a given ray based\n// on the provided bounded clipping plane (aka a rectangle)\nvoid getRayPointIntersectionBounds(\n  vec3 rayPos, vec3 rayDir,\n  vec3 planeDir, float planeDist,\n  inout vec2 tbounds, vec3 vPlaneX, vec3 vPlaneY,\n  float vSize1, float vSize2)\n{\n  float result = dot(rayDir, planeDir);\n  if (abs(result) < 1e-6)\n  {\n    return;\n  }\n  result = -1.0 * (dot(rayPos, planeDir) + planeDist) / result;\n  vec3 xposVC = rayPos + rayDir*result;\n  vec3 vxpos = xposVC - vOriginVC;\n  vec2 vpos = vec2(\n    dot(vxpos, vPlaneX),\n    dot(vxpos, vPlaneY));\n\n  // on some apple nvidia systems this does not work\n  // if (vpos.x < 0.0 || vpos.x > vSize1 ||\n  //     vpos.y < 0.0 || vpos.y > vSize2)\n  // even just\n  // if (vpos.x < 0.0 || vpos.y < 0.0)\n  // fails\n  // so instead we compute a value that represents in and out\n  //and then compute the return using this value\n  float xcheck = max(0.0, vpos.x * (vpos.x - vSize1)); //  0 means in bounds\n  float check = sign(max(xcheck, vpos.y * (vpos.y - vSize2))); //  0 means in bounds, 1 = out\n\n  tbounds = mix(\n   vec2(min(tbounds.x, result), max(tbounds.y, result)), // in value\n   tbounds, // out value\n   check);  // 0 in 1 out\n}\n\n//=======================================================================\n// given a\n// - ray direction (rayDir)\n// - starting point (vertexVCVSOutput)\n// - bounding planes of the volume\n// - optionally depth buffer values\n// - far clipping plane\n// compute the start/end distances of the ray we need to cast\nvec2 computeRayDistances(vec3 rayDir, vec3 tdims)\n{\n  vec2 dists = vec2(100.0*camFar, -1.0);\n\n  vec3 vSize = vSpacing*tdims;\n\n  // all this is in View Coordinates\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal0, vPlaneDistance0, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal1, vPlaneDistance1, dists, vPlaneNormal2, vPlaneNormal4,\n    vSize.y, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal2, vPlaneDistance2, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal3, vPlaneDistance3, dists, vPlaneNormal0, vPlaneNormal4,\n    vSize.x, vSize.z);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal4, vPlaneDistance4, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n  getRayPointIntersectionBounds(vertexVCVSOutput, rayDir,\n    vPlaneNormal5, vPlaneDistance5, dists, vPlaneNormal0, vPlaneNormal2,\n    vSize.x, vSize.y);\n\n  //VTK::ClipPlane::Impl\n\n  // do not go behind front clipping plane\n  dists.x = max(0.0,dists.x);\n\n  // do not go PAST far clipping plane\n  float farDist = -camThick/rayDir.z;\n  dists.y = min(farDist,dists.y);\n\n  // Do not go past the zbuffer value if set\n  // This is used for intermixing opaque geometry\n  //VTK::ZBuffer::Impl\n\n  return dists;\n}\n\n//=======================================================================\n// Compute the index space starting position (pos) and end\n// position\n//\nvoid computeIndexSpaceValues(out vec3 pos, out vec3 endPos, vec3 rayDir, vec2 dists)\n{\n  // compute starting and ending values in volume space\n  pos = vertexVCVSOutput + dists.x*rayDir;\n  pos = pos - vOriginVC;\n  // convert to volume basis and origin\n  pos = vec3(\n    dot(pos, vPlaneNormal0),\n    dot(pos, vPlaneNormal2),\n    dot(pos, vPlaneNormal4));\n\n  endPos = vertexVCVSOutput + dists.y*rayDir;\n  endPos = endPos - vOriginVC;\n  endPos = vec3(\n    dot(endPos, vPlaneNormal0),\n    dot(endPos, vPlaneNormal2),\n    dot(endPos, vPlaneNormal4));\n\n  float delta = length(endPos - pos);\n\n  pos *= vVCToIJK;\n  endPos *= vVCToIJK;\n\n  float delta2 = length(endPos - pos);\n  sampleDistanceIS = sampleDistance*delta2/delta;\n  #ifdef VolumeShadowOn\n    sampleDistanceISVS = sampleDistanceIS * volumeShadowSamplingDistFactor;\n  #endif\n}\n\nvoid main()\n{\n\n  if (cameraParallel == 1)\n  {\n    // Camera is parallel, so the rayDir is just the direction of the camera.\n    rayDirVC = vec3(0.0, 0.0, -1.0);\n  } else {\n    // camera is at 0,0,0 so rayDir for perspective is just the vc coord\n    rayDirVC = normalize(vertexVCVSOutput);\n  }\n\n  vec3 tdims = vec3(volumeDimensions);\n\n  // compute the start and end points for the ray\n  vec2 rayStartEndDistancesVC = computeRayDistances(rayDirVC, tdims);\n\n  // do we need to composite? aka does the ray have any length\n  // If not, bail out early\n  if (rayStartEndDistancesVC.y <= rayStartEndDistancesVC.x)\n  {\n    discard;\n  }\n\n  // IS = Index Space\n  vec3 posIS;\n  vec3 endIS;\n  computeIndexSpaceValues(posIS, endIS, rayDirVC, rayStartEndDistancesVC);\n\n  // Perform the blending operation along the ray\n  applyBlend(posIS, endIS, tdims);\n}\n",e.Geometry=""},e.useIndependentComponents=e=>{const n=e.getIndependentComponents(),r=t.currentInput?.getPointData()?.getScalars()?.getNumberOfComponents(),o=e.getColorMixPreset();return n&&r>=2||!!o},e.replaceShaderValues=(n,r,o)=>{const a=o.getProperty();let i=n.Fragment;a.getInterpolationType()===Lg.LINEAR&&(i=gd.substitute(i,"//VTK::TrilinearOn","#define vtkTrilinearOn").result),!0===e.isLabelmapOutlineRequired(o)&&(i=gd.substitute(i,"//VTK::ImageLabelOutlineOn","#define vtkImageLabelOutlineOn").result),t.renderable.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND&&(i=gd.substitute(i,"//VTK::LabelEdgeProjectionOn","#define vtkLabelEdgeProjectionOn").result);const s=t.scalarTexture.getComponents();i=gd.substitute(i,"//VTK::NumComponents",`#define vtkNumComponents ${s}`).result;const l=e.useIndependentComponents(a);l&&(i=gd.substitute(i,"//VTK::IndependentComponentsOn","#define UseIndependentComponents").result);const c=[],u=[];for(let e=0;e<s;e++)a.getOpacityMode(e)===Bg.PROPORTIONAL&&c.push(`#define vtkComponent${e}Proportional`),a.getForceNearestInterpolation(e)&&u.push(`#define vtkComponent${e}ForceNearest`);i=gd.substitute(i,"//VTK::vtkProportionalComponents",c.join("\n")).result,i=gd.substitute(i,"//VTK::vtkForceNearestComponents",u.join("\n")).result;const d=function(e){switch(e){case Ng.CUSTOM:return"//VTK::CustomColorMix";case Ng.ADDITIVE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n\n          vec3 scalarInterp1[2];\n          vec4 normalLight1 = computeNormalForDensity(posIS, tstep, scalarInterp1, 1);\n          scalarInterp1[0] = scalarInterp1[0] * oscale1 + oshift1;\n          scalarInterp1[1] = scalarInterp1[1] * oscale1 + oshift1;\n          normalLight1 = computeDensityNormal(scalarInterp1, height1, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n          vec4 normalLight1 = normalMat[1];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        float opacity1 = pwfValue1;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n          float gof1 = computeGradientOpacityFactor(normalMat[1].a, goscale1, goshift1, gomin1, gomax1);\n          opacity1 *= gof1;\n        #endif\n        float opacitySum = opacity0 + opacity1;\n        if (opacitySum <= 0.0) {\n          return vec4(0.0);\n        }\n\n        // mix the colors and opacities\n        tColor0 = applyAllLightning(tColor0, opacity0, posIS, normalLight0);\n        tColor1 = applyAllLightning(tColor1, opacity1, posIS, normalLight1);\n        vec3 mixedColor = (opacity0 * tColor0 + opacity1 * tColor1) / opacitySum;\n        return vec4(mixedColor, min(1.0, opacitySum));\n";case Ng.COLORIZE:return"\n        // compute normals\n        mat4 normalMat = computeMat4Normal(posIS, tValue, tstep);\n        #if (vtkLightComplexity > 0) && defined(vtkComputeNormalFromOpacity)\n          vec3 scalarInterp0[2];\n          vec4 normalLight0 = computeNormalForDensity(posIS, tstep, scalarInterp0, 0);\n          scalarInterp0[0] = scalarInterp0[0] * oscale0 + oshift0;\n          scalarInterp0[1] = scalarInterp0[1] * oscale0 + oshift0;\n          normalLight0 = computeDensityNormal(scalarInterp0, height0, 1.0);\n        #else\n          vec4 normalLight0 = normalMat[0];\n        #endif\n\n        // compute opacities\n        float opacity0 = pwfValue0;\n        #ifdef vtkGradientOpacityOn\n          float gof0 = computeGradientOpacityFactor(normalMat[0].a, goscale0, goshift0, gomin0, gomax0);\n          opacity0 *= gof0;\n        #endif\n\n        // mix the colors and opacities\n        vec3 color = tColor0 * mix(vec3(1.0), tColor1, pwfValue1);\n        color = applyAllLightning(color, opacity0, posIS, normalLight0);\n        return vec4(color, opacity0);\n";default:return null}}(a.getColorMixPreset());d&&(i=gd.substitute(i,"//VTK::CustomComponentsColorMixOn","#define vtkCustomComponentsColorMix").result,i=gd.substitute(i,"//VTK::CustomComponentsColorMix::Impl",d).result);const p=t.currentInput.getSpatialExtent(),f=t.currentInput.getSpacing(),g=new Float64Array(3);In(g,(p[1]-p[0])*f[0],(p[3]-p[2])*f[1],(p[5]-p[4])*f[2]);const m=Sn(g)/e.getCurrentSampleDistance(r);i=gd.substitute(i,"//VTK::MaximumSamplesValue",`${Math.ceil(m)}`).result,i=gd.substitute(i,"//VTK::LightComplexity",`#define vtkLightComplexity ${t.lightComplexity}`).result,t.lightComplexity>0&&(t.renderable.getVolumetricScatteringBlending()>0&&(i=gd.substitute(i,"//VTK::VolumeShadowOn","#define VolumeShadowOn").result),t.renderable.getVolumetricScatteringBlending()<1&&(i=gd.substitute(i,"//VTK::SurfaceShadowOn","#define SurfaceShadowOn").result),t.renderable.getLocalAmbientOcclusion()&&a.getAmbient()>0&&(i=gd.substitute(i,"//VTK::localAmbientOcclusionOn","#define localAmbientOcclusionOn").result));const h=l?s:1;t.gopacity=!1;for(let e=0;!t.gopacity&&e<h;++e)t.gopacity||=a.getUseGradientOpacity(e);t.gopacity&&(i=gd.substitute(i,"//VTK::GradientOpacityOn","#define vtkGradientOpacityOn").result),t.renderable.getComputeNormalFromOpacity()&&(i=gd.substitute(i,"//VTK::vtkComputeNormalFromOpacity","#define vtkComputeNormalFromOpacity").result),null!==t.zBufferTexture&&(i=gd.substitute(i,"//VTK::ZBuffer::Dec",["uniform sampler2D zBufferTexture;","uniform float vpZWidth;","uniform float vpZHeight;"]).result,i=gd.substitute(i,"//VTK::ZBuffer::Impl",["vec4 depthVec = texture2D(zBufferTexture, vec2(gl_FragCoord.x / vpZWidth, gl_FragCoord.y/vpZHeight));","float zdepth = (depthVec.r*256.0 + depthVec.g)/257.0;","zdepth = zdepth * 2.0 - 1.0;","if (cameraParallel == 0) {","zdepth = -2.0 * camFar * camNear / (zdepth*(camFar-camNear)-(camFar+camNear)) - camNear;}","else {","zdepth = (zdepth + 1.0) * 0.5 * (camFar - camNear);}\n","zdepth = -zdepth/rayDir.z;","dists.y = min(zdepth,dists.y);"]).result),i=gd.substitute(i,"//VTK::BlendMode",`${t.renderable.getBlendMode()}`).result,n.Fragment=i,e.replaceShaderLight(n,r,o),e.replaceShaderClippingPlane(n,r,o)},e.replaceShaderLight=(e,n,r)=>{if(0===t.lightComplexity)return;let o=e.Fragment,a=0;n.getLights().forEach((e=>{e.getSwitch()&&(a+=1)})),o=gd.substitute(o,"//VTK::Light::Dec",["uniform int lightNum;","uniform bool twoSidedLighting;",`uniform vec3 lightColor[${a}];`,`uniform vec3 lightDirectionVC[${a}]; // normalized`,`uniform vec3 lightHalfAngleVC[${a}];`,"//VTK::Light::Dec"],!1).result,3===t.lightComplexity&&(o=gd.substitute(o,"//VTK::Light::Dec",[`uniform vec3 lightPositionVC[${a}];`,`uniform vec3 lightAttenuation[${a}];`,`uniform float lightConeAngle[${a}];`,`uniform float lightExponent[${a}];`,`uniform int lightPositional[${a}];`],!1).result),t.renderable.getVolumetricScatteringBlending()>0&&(o=gd.substitute(o,"//VTK::VolumeShadow::Dec",["uniform float volumetricScatteringBlending;","uniform float giReach;","uniform float volumeShadowSamplingDistFactor;","uniform float anisotropy;","uniform float anisotropy2;"],!1).result),t.renderable.getLocalAmbientOcclusion()&&r.getProperty().getAmbient()>0&&(o=gd.substitute(o,"//VTK::LAO::Dec",["uniform int kernelRadius;",`uniform vec2 kernelSample[${t.renderable.getLAOKernelRadius()}];`,"uniform int kernelSize;"],!1).result),e.Fragment=o},e.replaceShaderClippingPlane=(e,n,r)=>{let o=e.Fragment;if(t.renderable.getClippingPlanes().length>0){const e=t.renderable.getClippingPlanes().length;o=gd.substitute(o,"//VTK::ClipPlane::Dec",["uniform vec3 vClipPlaneNormals[6];","uniform float vClipPlaneDistances[6];","uniform vec3 vClipPlaneOrigins[6];","uniform int clip_numPlanes;","//VTK::ClipPlane::Dec","#define vtkClippingPlanesOn"],!1).result,o=gd.substitute(o,"//VTK::ClipPlane::Impl",[`for(int i = 0; i < ${e}; i++) {`,"  float rayDirRatio = dot(rayDir, vClipPlaneNormals[i]);","  float equationResult = dot(vertexVCVSOutput, vClipPlaneNormals[i]) + vClipPlaneDistances[i];","  if (rayDirRatio == 0.0)","  {","    if (equationResult < 0.0) dists.x = dists.y;","    continue;","  }","  float result = -1.0 * equationResult / rayDirRatio;","  if (rayDirRatio < 0.0) dists.y = min(dists.y, result);","  else dists.x = max(dists.x, result);","}","//VTK::ClipPlane::Impl"],!1).result}e.Fragment=o},e.getNeedToRebuildShaders=(n,r,o)=>{const a=o.getProperty();((n,r)=>{let o=0;n.getProperty().getShade()&&t.renderable.getBlendMode()===Fg.COMPOSITE_BLEND&&(o=0,t.numberOfLights=0,r.forEach((e=>{e.getSwitch()>0&&(t.numberOfLights++,0===o&&(o=1)),1===o&&(t.numberOfLights>1||1!==e.getIntensity()||!e.lightTypeIsHeadLight())&&(o=2),o<3&&e.getPositional()&&(o=3)}))),o!==t.lightComplexity&&(t.lightComplexity=o,e.modified())})(o,r.getLights());const i=t.scalarTexture.getComponents(),s=[],l=[];for(let e=0;e<i;e++)s.push(a.getOpacityMode(e)),l.push(a.getForceNearestInterpolation(e));const c=t.currentInput.getSpatialExtent(),u=t.currentInput.getSpacing(),d=new Float64Array(3);In(d,(c[1]-c[0])*u[0],(c[3]-c[2])*u[1],(c[5]-c[4])*u[2]);const p=Sn(d)/e.getCurrentSampleDistance(r),f=!!t.zBufferTexture,g={iComps:a.getIndependentComponents(),colorMixPreset:a.getColorMixPreset(),interpolationType:a.getInterpolationType(),useLabelOutline:e.isLabelmapOutlineRequired(o),numComp:i,maxSamples:p,useGradientOpacity:a.getUseGradientOpacity(0),blendMode:t.renderable.getBlendMode(),hasZBufferTexture:f,opacityModes:s,forceNearestInterps:l};return(0===n.getProgram()?.getHandle()||n.getShaderSourceTime().getMTime()<e.getMTime()||n.getShaderSourceTime().getMTime()<t.renderable.getMTime()||!t.previousState||!ke()(t.previousState,g))&&(t.previousState=g,!0)},e.updateShaders=(n,r,o)=>{if(e.getNeedToRebuildShaders(n,r,o)){const a={Vertex:null,Fragment:null,Geometry:null};e.buildShaders(a,r,o);const i=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(a.Vertex,a.Fragment,a.Geometry);i!==n.getProgram()&&(n.setProgram(i),n.getVAO().releaseGraphicsResources()),n.getShaderSourceTime().modified()}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(n.getProgram());n.getVAO().bind(),e.setMapperShaderParameters(n,r,o),e.setCameraShaderParameters(n,r,o),e.setPropertyShaderParameters(n,r,o),e.getClippingPlaneShaderParameters(n,r,o)},e.setMapperShaderParameters=(n,r,o)=>{const a=n.getProgram();n.getCABO().getElementCount()&&(t.VBOBuildTime.getMTime()>n.getAttributeUpdateTime().getMTime()||n.getShaderSourceTime().getMTime()>n.getAttributeUpdateTime().getMTime())&&(a.isAttributeUsed("vertexDC")&&(n.getVAO().addAttributeArray(a,n.getCABO(),"vertexDC",n.getCABO().getVertexOffset(),n.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in shader VAO.")),n.getAttributeUpdateTime().modified()),a.setUniformi("texture1",t.scalarTexture.getTextureUnit()),a.setUniformf("sampleDistance",e.getCurrentSampleDistance(r));const i=t.scalarTexture.getVolumeInfo(),s=t.renderable.getIpScalarRange(),l=[],c=[];for(let e=0;e<4;e++)l[e]=s[0]*i.dataComputedScale[e]+i.dataComputedOffset[e],c[e]=s[1]*i.dataComputedScale[e]+i.dataComputedOffset[e],l[e]=(l[e]-i.offset[e])/i.scale[e],c[e]=(c[e]-i.offset[e])/i.scale[e];if(a.setUniform4f("ipScalarRangeMin",l[0],l[1],l[2],l[3]),a.setUniform4f("ipScalarRangeMax",c[0],c[1],c[2],c[3]),null!==t.zBufferTexture){a.setUniformi("zBufferTexture",t.zBufferTexture.getTextureUnit());const e=t._useSmallViewport?[t._smallViewportWidth,t._smallViewportHeight]:t._openGLRenderWindow.getFramebufferSize();a.setUniformf("vpZWidth",e[0]),a.setUniformf("vpZHeight",e[1])}},e.setCameraShaderParameters=(n,r,o)=>{const a=t.openGLCamera.getKeyMatrices(r),i=t.openGLVolume.getKeyMatrices();T(t.modelToView,a.wcvc,i.mcwc);const s=n.getProgram(),l=t.openGLCamera.getRenderable(),c=l.getClippingRange();s.setUniformf("camThick",c[1]-c[0]),s.setUniformf("camNear",c[0]),s.setUniformf("camFar",c[1]);const u=t.currentInput.getBounds(),d=t.currentInput.getDimensions(),p=new Float64Array(3),f=new Float64Array(3);let g=1,m=-1,v=1,y=-1;for(let e=0;e<8;++e)In(p,u[e%2],u[2+Math.floor(e/2)%2],u[4+Math.floor(e/4)]),Vn(p,p,t.modelToView),l.getParallelProjection()||(Mn(f,p),On(p,f,-c[0]/p[2])),Vn(p,p,a.vcpc),g=Math.min(p[0],g),m=Math.max(p[0],m),v=Math.min(p[1],v),y=Math.max(p[1],y);s.setUniformf("dcxmin",g),s.setUniformf("dcxmax",m),s.setUniformf("dcymin",v),s.setUniformf("dcymax",y),s.isUniformUsed("cameraParallel")&&s.setUniformi("cameraParallel",l.getParallelProjection());const b=t.currentInput.getSpatialExtent(),x=t.currentInput.getSpacing(),C=new Float64Array(3);In(C,(b[1]-b[0])*x[0],(b[3]-b[2])*x[1],(b[5]-b[4])*x[2]),s.setUniform3f("vSpacing",x[0],x[1],x[2]),In(p,b[0],b[2],b[4]),t.currentInput.indexToWorldVec3(p,p),Vn(p,p,t.modelToView),s.setUniform3f("vOriginVC",p[0],p[1],p[2]);const S=t.currentInput.getIndexToWorld();T(t.idxToView,t.modelToView,S),ve(t.idxNormalMatrix,a.normalMatrix,i.normalMatrix),ve(t.idxNormalMatrix,t.idxNormalMatrix,t.currentInput.getDirectionByReference());const A=Sn(C)/e.getCurrentSampleDistance(r);A>t.renderable.getMaximumSamplesPerRay()&&Gg(`The number of steps required ${Math.ceil(A)} is larger than the\n        specified maximum number of steps ${t.renderable.getMaximumSamplesPerRay()}.\n        Please either change the\n        volumeMapper sampleDistance or its maximum number of samples.`);const I=new Float64Array(3);if(In(I,1,1,1),function(e,t,n){e[0]=t[0]/n[0],e[1]=t[1]/n[1],e[2]=t[2]/n[2]}(I,I,C),s.setUniform3f("vVCToIJK",I[0],I[1],I[2]),s.setUniform3i("volumeDimensions",d[0],d[1],d[2]),s.setUniform3f("volumeSpacings",x[0],x[1],x[2]),!t._openGLRenderWindow.getWebgl2()){const e=t.scalarTexture.getVolumeInfo();s.setUniformf("texWidth",t.scalarTexture.getWidth()),s.setUniformf("texHeight",t.scalarTexture.getHeight()),s.setUniformi("xreps",e.xreps),s.setUniformi("xstride",e.xstride),s.setUniformi("ystride",e.ystride)}const w=new Float64Array(3),P=new Float64Array(3);for(let e=0;e<6;++e){switch(e){case 1:In(w,-1,0,0),In(P,b[0],b[2],b[4]);break;case 2:In(w,0,1,0),In(P,b[1],b[3],b[5]);break;case 3:In(w,0,-1,0),In(P,b[0],b[2],b[4]);break;case 4:In(w,0,0,1),In(P,b[1],b[3],b[5]);break;case 5:In(w,0,0,-1),In(P,b[0],b[2],b[4]);break;default:In(w,1,0,0),In(P,b[1],b[3],b[5])}Ln(w,w,t.idxNormalMatrix),Vn(P,P,t.idxToView);const n=-1*En(P,w);s.setUniform3f(`vPlaneNormal${e}`,w[0],w[1],w[2]),s.setUniformf(`vPlaneDistance${e}`,n)}if(e.isLabelmapOutlineRequired(o)){const n=t.currentInput.getWorldToIndex();s.setUniformMatrix("vWCtoIDX",n);const o=r.getActiveCamera(),[a,i]=o.getClippingRange(),l=o.getDistance();o.setClippingRange(l,l+.1);const c=t.openGLCamera.getKeyMatrices(r);h(t.projectionToWorld,c.wcpc),o.setClippingRange(a,i),t.openGLCamera.getKeyMatrices(r),s.setUniformMatrix("PCWCMatrix",t.projectionToWorld);const u=e.getRenderTargetSize();s.setUniformf("vpWidth",u[0]),s.setUniformf("vpHeight",u[1]);const d=e.getRenderTargetOffset();s.setUniformf("vpOffsetX",d[0]/u[0]),s.setUniformf("vpOffsetY",d[1]/u[1])}if(h(t.projectionToView,a.vcpc),s.setUniformMatrix("PCVCMatrix",t.projectionToView),0===t.lightComplexity)return;let O=0;const R=[],M=[],E=[];if(r.getLights().forEach((e=>{if(e.getSwitch()>0){const t=e.getColor(),n=e.getIntensity();R[0+3*O]=t[0]*n,R[1+3*O]=t[1]*n,R[2+3*O]=t[2]*n;const r=e.getDirection();In(w,r[0],r[1],r[2]),Ln(w,w,a.normalMatrix),Mn(w,w),M[0+3*O]=w[0],M[1+3*O]=w[1],M[2+3*O]=w[2],E[0+3*O]=-.5*w[0],E[1+3*O]=-.5*w[1],E[2+3*O]=-.5*(w[2]-1),O++}})),s.setUniformi("twoSidedLighting",r.getTwoSidedLighting()),s.setUniformi("lightNum",O),s.setUniform3fv("lightColor",R),s.setUniform3fv("lightDirectionVC",M),s.setUniform3fv("lightHalfAngleVC",E),3===t.lightComplexity){O=0;const e=[],n=[],o=[],a=[],i=[];r.getLights().forEach((r=>{if(r.getSwitch()>0){const s=r.getAttenuationValues();n[0+3*O]=s[0],n[1+3*O]=s[1],n[2+3*O]=s[2],a[O]=r.getExponent(),o[O]=r.getConeAngle(),i[O]=r.getPositional();const l=r.getTransformedPosition();Vn(l,l,t.modelToView),e[0+3*O]=l[0],e[1+3*O]=l[1],e[2+3*O]=l[2],O+=1}})),s.setUniform3fv("lightPositionVC",e),s.setUniform3fv("lightAttenuation",n),s.setUniformfv("lightConeAngle",o),s.setUniformfv("lightExponent",a),s.setUniformiv("lightPositional",i)}if(t.renderable.getVolumetricScatteringBlending()>0&&(s.setUniformf("giReach",t.renderable.getGlobalIlluminationReach()),s.setUniformf("volumetricScatteringBlending",t.renderable.getVolumetricScatteringBlending()),s.setUniformf("volumeShadowSamplingDistFactor",t.renderable.getVolumeShadowSamplingDistFactor()),s.setUniformf("anisotropy",t.renderable.getAnisotropy()),s.setUniformf("anisotropy2",t.renderable.getAnisotropy()**2)),t.renderable.getLocalAmbientOcclusion()&&o.getProperty().getAmbient()>0){const e=t.renderable.getLAOKernelSize();s.setUniformi("kernelSize",e);const n=[];for(let t=0;t<e;t++)n[2*t]=.5*Math.random(),n[2*t+1]=.5*Math.random();s.setUniform2fv("kernelSample",n),s.setUniformi("kernelRadius",t.renderable.getLAOKernelRadius())}},e.setPropertyShaderParameters=(n,r,o)=>{const a=n.getProgram();a.setUniformi("ctexture",t.colorTexture.getTextureUnit()),a.setUniformi("otexture",t.opacityTexture.getTextureUnit()),a.setUniformi("jtexture",t.jitterTexture.getTextureUnit()),a.setUniformi("ttexture",t.labelOutlineThicknessTexture.getTextureUnit());const i=t.scalarTexture.getVolumeInfo(),s=o.getProperty(),l=t.scalarTexture.getComponents(),c=e.useIndependentComponents(s);if(c)for(let e=0;e<l;e++)a.setUniformf(`mix${e}`,o.getProperty().getComponentWeight(e));for(let e=0;e<l;e++){const t=c?e:0,n=i.scale[e],r=s.getScalarOpacity(t).getRange(),o=n/(r[1]-r[0]),l=(i.offset[e]-r[0])/(r[1]-r[0]);a.setUniformf(`oshift${e}`,l),a.setUniformf(`oscale${e}`,o);const u=s.getRGBTransferFunction(t).getRange(),d=(i.offset[e]-u[0])/(u[1]-u[0]),p=n/(u[1]-u[0]);a.setUniformf(`cshift${e}`,d),a.setUniformf(`cscale${e}`,p)}if(t.gopacity)if(c)for(let e=0;e<l;++e){const t=i.scale[e];if(s.getUseGradientOpacity(e)){const n=s.getGradientOpacityMinimumOpacity(e),r=s.getGradientOpacityMaximumOpacity(e);a.setUniformf(`gomin${e}`,n),a.setUniformf(`gomax${e}`,r);const o=[s.getGradientOpacityMinimumValue(e),s.getGradientOpacityMaximumValue(e)];a.setUniformf(`goscale${e}`,t*(r-n)/(o[1]-o[0])),a.setUniformf(`goshift${e}`,-o[0]*(r-n)/(o[1]-o[0])+n)}else a.setUniformf(`gomin${e}`,1),a.setUniformf(`gomax${e}`,1),a.setUniformf(`goscale${e}`,0),a.setUniformf(`goshift${e}`,1)}else{const e=i.scale[l-1],t=s.getGradientOpacityMinimumOpacity(0),n=s.getGradientOpacityMaximumOpacity(0);a.setUniformf("gomin0",t),a.setUniformf("gomax0",n);const r=[s.getGradientOpacityMinimumValue(0),s.getGradientOpacityMaximumValue(0)];a.setUniformf("goscale0",e*(n-t)/(r[1]-r[0])),a.setUniformf("goshift0",-r[0]*(n-t)/(r[1]-r[0])+t)}if(!0===e.isLabelmapOutlineRequired(o)){const e=o.getProperty().getLabelOutlineOpacity();a.setUniformf("outlineOpacity",e)}t.lightComplexity>0&&(a.setUniformf("vAmbient",s.getAmbient()),a.setUniformf("vDiffuse",s.getDiffuse()),a.setUniformf("vSpecular",s.getSpecular()),a.setUniformf("vSpecularPower",s.getSpecularPower()))},e.getClippingPlaneShaderParameters=(e,n,r)=>{if(t.renderable.getClippingPlanes().length>0){const r=t.openGLCamera.getKeyMatrices(n),o=[],a=[],i=[],s=t.renderable.getClippingPlanes(),l=s.length;for(let e=0;e<l;++e){const t=s[e].getNormal(),n=s[e].getOrigin();Ln(t,t,r.normalMatrix),Vn(n,n,r.wcvc);const l=-1*En(n,t);o.push(t[0]),o.push(t[1]),o.push(t[2]),a.push(l),i.push(n[0]),i.push(n[1]),i.push(n[2])}const c=e.getProgram();c.setUniform3fv("vClipPlaneNormals",o),c.setUniformfv("vClipPlaneDistances",a),c.setUniform3fv("vClipPlaneOrigins",i),c.setUniformi("clip_numPlanes",l)}},e.delete=Vt((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),(()=>{t._openGLRenderWindow&&n(t._openGLRenderWindow)}),e.delete),e.getRenderTargetSize=()=>{if(t._useSmallViewport)return[t._smallViewportWidth,t._smallViewportHeight];const{usize:e,vsize:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getRenderTargetOffset=()=>{const{lowerLeftU:e,lowerLeftV:n}=t._openGLRenderer.getTiledSizeAndOrigin();return[e,n]},e.getCurrentSampleDistance=e=>{const n=e.getVTKWindow().getInteractor(),r=t.renderable.getSampleDistance();return n.isAnimating()?r*t.renderable.getInteractionSampleDistanceFactor():r},e.renderPieceStart=(n,r)=>{const o=n.getVTKWindow().getInteractor();if(t._lastScale||(t._lastScale=t.renderable.getInitialInteractionScale()),t._useSmallViewport=!1,o.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._animationRateSubscription||(t._animationRateSubscription=o.onAnimationFrameRateUpdate((()=>{if(t.renderable.getAutoAdjustSampleDistances()){const e=o.getRecentAnimationFrameRate(),n=o.getDesiredUpdateRate()/e;(n>1.15||n<.85)&&(t._lastScale*=n),t._lastScale>400&&(t._lastScale=400),t._lastScale<1.5&&(t._lastScale=1.5)}else t._lastScale=t.renderable.getImageSampleDistance()*t.renderable.getImageSampleDistance()}))),t._useSmallViewport){const e=t._openGLRenderWindow.getFramebufferSize(),n=1/Math.sqrt(t._lastScale);if(t._smallViewportWidth=Math.ceil(n*e[0]),t._smallViewportHeight=Math.ceil(n*e[1]),t._smallViewportHeight>e[1]&&(t._smallViewportHeight=e[1]),t._smallViewportWidth>e[0]&&(t._smallViewportWidth=e[0]),t.framebuffer.saveCurrentBindingsAndBuffers(),null===t.framebuffer.getGLFramebuffer())t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer();else{const n=t.framebuffer.getSize();n&&n[0]===e[0]&&n[1]===e[1]||(t.framebuffer.create(e[0],e[1]),t.framebuffer.populateFramebuffer())}t.framebuffer.bind();const r=t.context;r.clearColor(0,0,0,0),r.colorMask(!0,!0,!0,!0),r.clear(r.COLOR_BUFFER_BIT),r.viewport(0,0,t._smallViewportWidth,t._smallViewportHeight),t.fvp=[t._smallViewportWidth/e[0],t._smallViewportHeight/e[1]]}t.context.disable(t.context.DEPTH_TEST),e.updateBufferObjects(n,r),r.getProperty().getInterpolationType()===Lg.NEAREST?(t.scalarTexture.setMinificationFilter(Id.NEAREST),t.scalarTexture.setMagnificationFilter(Id.NEAREST)):(t.scalarTexture.setMinificationFilter(Id.LINEAR),t.scalarTexture.setMagnificationFilter(Id.LINEAR)),null!==t.zBufferTexture&&t.zBufferTexture.activate()},e.renderPieceDraw=(n,r)=>{const o=t.context;t.scalarTexture.activate(),t.opacityTexture.activate(),t.labelOutlineThicknessTexture.activate(),t.colorTexture.activate(),t.jitterTexture.activate(),e.updateShaders(t.tris,n,r),o.drawArrays(o.TRIANGLES,0,t.tris.getCABO().getElementCount()),t.tris.getVAO().release(),t.scalarTexture.deactivate(),t.colorTexture.deactivate(),t.opacityTexture.deactivate(),t.labelOutlineThicknessTexture.deactivate(),t.jitterTexture.deactivate()},e.renderPieceFinish=(e,n)=>{if(null!==t.zBufferTexture&&t.zBufferTexture.deactivate(),t._useSmallViewport){if(t.framebuffer.restorePreviousBindingsAndBuffers(),null===t.copyShader){t.copyShader=t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute vec4 vertexDC;","uniform vec2 tfactor;","varying vec2 tcoord;","void main() { tcoord = vec2(vertexDC.x*0.5 + 0.5, vertexDC.y*0.5 + 0.5) * tfactor; gl_Position = vertexDC; }"].join("\n"),["//VTK::System::Dec","//VTK::Output::Dec","uniform sampler2D texture1;","varying vec2 tcoord;","void main() { gl_FragData[0] = texture2D(texture1,tcoord); }"].join("\n"),"");const e=t.copyShader;t.copyVAO=yd.newInstance(),t.copyVAO.setOpenGLRenderWindow(t._openGLRenderWindow),t.tris.getCABO().bind(),t.copyVAO.addAttributeArray(e,t.tris.getCABO(),"vertexDC",t.tris.getCABO().getVertexOffset(),t.tris.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE)||Ug("Error setting vertexDC in copy shader VAO.")}else t._openGLRenderWindow.getShaderCache().readyShaderProgram(t.copyShader);const e=t._openGLRenderWindow.getFramebufferSize();t.context.viewport(0,0,e[0],e[1]);const n=t.framebuffer.getColorTexture();n.activate(),t.copyShader.setUniformi("texture",n.getTextureUnit()),t.copyShader.setUniform2f("tfactor",t.fvp[0],t.fvp[1]);const r=t.context;r.blendFuncSeparate(r.ONE,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA),t.context.drawArrays(t.context.TRIANGLES,0,t.tris.getCABO().getElementCount()),n.deactivate(),r.blendFuncSeparate(r.SRC_ALPHA,r.ONE_MINUS_SRC_ALPHA,r.ONE,r.ONE_MINUS_SRC_ALPHA)}},e.renderPiece=(n,r)=>{e.invokeEvent({type:"StartEvent"}),t.renderable.update(),t.currentInput=t.renderable.getInputData(),e.invokeEvent({type:"EndEvent"}),t.currentInput?(e.renderPieceStart(n,r),e.renderPieceDraw(n,r),e.renderPieceFinish(n,r)):Ug("No input!")},e.computeBounds=(n,r)=>{e.getInput()?t.bounds=e.getInput().getBounds():Oa(t.Bounds)},e.updateBufferObjects=(t,n)=>{e.getNeedToRebuildBufferObjects(t,n)&&e.buildBufferObjects(t,n)},e.getNeedToRebuildBufferObjects=(n,r)=>t.VBOBuildTime.getMTime()<e.getMTime()||t.VBOBuildTime.getMTime()<r.getMTime()||t.VBOBuildTime.getMTime()<t.renderable.getMTime()||t.VBOBuildTime.getMTime()<r.getProperty().getMTime()||t.VBOBuildTime.getMTime()<t.currentInput.getMTime()||!t.scalarTexture?.getHandle()||!t.colorTexture?.getHandle()||!t.labelOutlineThicknessTexture?.getHandle(),e.buildBufferObjects=(n,r)=>{const o=t.currentInput;if(!o)return;const a=o.getPointData()&&o.getPointData().getScalars();if(!a)return;const i=r.getProperty();if(!t.jitterTexture.getHandle()){const e=new Uint8Array(1024);for(let t=0;t<1024;++t)e[t]=255*Math.random();t.jitterTexture.setMinificationFilter(Id.LINEAR),t.jitterTexture.setMagnificationFilter(Id.LINEAR),t.jitterTexture.create2DFromRaw(32,32,1,ys.UNSIGNED_CHAR,e)}const s=a.getNumberOfComponents(),l=e.useIndependentComponents(i),c=l?s:1,u=i.getScalarOpacity(),d=t._openGLRenderWindow.getGraphicsResourceForObject(u);let p=zg(u,l,c);if(d?.oglObject&&d.hash===p)t.opacityTexture=d.oglObject;else{t.opacityTexture=Ud.newInstance(),t.opacityTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const r=1024,o=2*r*c,a=new Float32Array(o),s=new Float32Array(r);for(let t=0;t<c;++t){const o=i.getScalarOpacity(t),l=e.getCurrentSampleDistance(n)/i.getScalarOpacityUnitDistance(t),c=o.getRange();o.getTable(c[0],c[1],r,s,1);for(let e=0;e<r;++e)a[t*r*2+e]=1-(1-s[e])**l,a[t*r*2+e+r]=a[t*r*2+e]}if(t.opacityTexture.resetFormatAndType(),t.opacityTexture.setMinificationFilter(Id.LINEAR),t.opacityTexture.setMagnificationFilter(Id.LINEAR),t._openGLRenderWindow.getWebgl2()||t.context.getExtension("OES_texture_float")&&t.context.getExtension("OES_texture_float_linear"))t.opacityTexture.create2DFromRaw(r,2*c,1,ys.FLOAT,a);else{const e=new Uint8ClampedArray(o);for(let t=0;t<o;++t)e[t]=255*a[t];t.opacityTexture.create2DFromRaw(r,2*c,1,ys.UNSIGNED_CHAR,e)}u&&(t._openGLRenderWindow.setGraphicsResourceForObject(u,t.opacityTexture,p),u!==t._scalarOpacityFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(u,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalarOpacityFunc,e)),t._scalarOpacityFunc=u)}const f=i.getRGBTransferFunction();p=zg(f,l,c);const g=t._openGLRenderWindow.getGraphicsResourceForObject(f);if(g?.oglObject?.getHandle()&&g?.hash===p)t.colorTexture=g.oglObject;else{t.colorTexture=Ud.newInstance(),t.colorTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,r=new Uint8ClampedArray(2*n*c*3),o=new Float32Array(3*n);for(let e=0;e<c;++e){const t=i.getRGBTransferFunction(e),a=t.getRange();t.getTable(a[0],a[1],n,o,1);for(let t=0;t<3*n;++t)r[e*n*6+t]=255*o[t],r[e*n*6+t+3*n]=255*o[t]}t.colorTexture.resetFormatAndType(),t.colorTexture.setMinificationFilter(Id.LINEAR),t.colorTexture.setMagnificationFilter(Id.LINEAR),t.colorTexture.create2DFromRaw(n,2*c,3,ys.UNSIGNED_CHAR,r),f&&(t._openGLRenderWindow.setGraphicsResourceForObject(f,t.colorTexture,p),f!==t._colorTransferFunc&&(t._openGLRenderWindow.registerGraphicsResourceUser(f,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._colorTransferFunc,e)),t._colorTransferFunc=f)}e.updateLabelOutlineThicknessTexture(r);const m=t._openGLRenderWindow.getGraphicsResourceForObject(a);if(p=`${o.getMTime()}A${a.getMTime()}`,m?.oglObject?.getHandle()&&m?.hash===p)t.scalarTexture=m.oglObject;else{t.scalarTexture=Ud.newInstance(),t.scalarTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=o.getDimensions();t.scalarTexture.setOglNorm16Ext(t.context.getExtension("EXT_texture_norm16")),t.scalarTexture.resetFormatAndType(),t.scalarTexture.create3DFilterableFromDataArray(n[0],n[1],n[2],a,t.renderable.getPreferSizeOverAccuracy()),a&&(t._openGLRenderWindow.setGraphicsResourceForObject(a,t.scalarTexture,p),a!==t._scalars&&(t._openGLRenderWindow.registerGraphicsResourceUser(a,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._scalars,e)),t._scalars=a)}if(!t.tris.getCABO().getElementCount()){const e=new Float32Array(12);for(let t=0;t<4;t++)e[3*t]=t%2*2-1,e[3*t+1]=t>1?1:-1,e[3*t+2]=-1;const n=new Uint16Array(8);n[0]=3,n[1]=0,n[2]=1,n[3]=3,n[4]=3,n[5]=0,n[6]=3,n[7]=2;const r=Ds.newInstance({numberOfComponents:3,values:e});r.setName("points");const o=Ds.newInstance({numberOfComponents:1,values:n});t.tris.getCABO().createVBO(o,"polys",rs.SURFACE,{points:r,cellOffset:0})}t.VBOBuildTime.modified()},e.updateLabelOutlineThicknessTexture=n=>{const r=n.getProperty().getLabelOutlineThickness(),o=t._openGLRenderWindow.getGraphicsResourceForObject(r),a=`${r.join("-")}`;if(o?.oglObject?.getHandle()&&o?.hash===a)t.labelOutlineThicknessTexture=o.oglObject;else{t.labelOutlineThicknessTexture=Ud.newInstance(),t.labelOutlineThicknessTexture.setOpenGLRenderWindow(t._openGLRenderWindow);const n=1024,o=1,i=new Uint8Array(n*o);for(let e=0;e<n;++e){const t=void 0!==r[e]?r[e]:r[0];i[e]=t}t.labelOutlineThicknessTexture.resetFormatAndType(),t.labelOutlineThicknessTexture.setMinificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.setMagnificationFilter(Id.NEAREST),t.labelOutlineThicknessTexture.create2DFromRaw(n,o,1,ys.UNSIGNED_CHAR,i),r&&(t._openGLRenderWindow.setGraphicsResourceForObject(r,t.labelOutlineThicknessTexture,a),r!==t._labelOutlineThicknessArray&&(t._openGLRenderWindow.registerGraphicsResourceUser(r,e),t._openGLRenderWindow.unregisterGraphicsResourceUser(t._labelOutlineThicknessArray,e)),t._labelOutlineThicknessArray=r)}},e.isLabelmapOutlineRequired=e=>{const n=e.getProperty(),r=t.renderable;return n.getUseLabelOutline()||r.getBlendMode()===Fg.LABELMAP_EDGE_PROJECTION_BLEND}}const Hg={context:null,VBOBuildTime:null,scalarTexture:null,opacityTexture:null,opacityTextureString:null,colorTexture:null,colorTextureString:null,jitterTexture:null,labelOutlineThicknessTexture:null,labelOutlineThicknessTextureString:null,tris:null,framebuffer:null,copyShader:null,copyVAO:null,lastXYF:1,targetXYF:1,zBufferTexture:null,lastZBufferTexture:null,lightComplexity:0,fullViewportTime:1,idxToView:null,idxNormalMatrix:null,modelToView:null,projectionToView:null,avgWindowArea:0,avgFrameTime:0};const jg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Hg,n),Zt.extend(e,t,n),jd(e,t,n),t.VBOBuildTime={},yt(t.VBOBuildTime,{mtime:0}),t.tris=Sd.newInstance(),t.jitterTexture=Ud.newInstance(),t.jitterTexture.setWrapS(Ad.REPEAT),t.jitterTexture.setWrapT(Ad.REPEAT),t.framebuffer=Xp.newInstance(),t.idxToView=g(new Float64Array(16)),t.idxNormalMatrix=pe(new Float64Array(9)),t.modelToView=g(new Float64Array(16)),t.projectionToView=g(new Float64Array(16)),t.projectionToWorld=g(new Float64Array(16)),At(e,t,["context"]),Wg(e,t)}),"vtkOpenGLVolumeMapper");rn("vtkVolumeMapper",jg);const{vtkDebugMacro:Kg}=Kt;function $g(e,t){t.classHierarchy.push("vtkOpenGLPixelSpaceCallbackMapper"),e.opaquePass=(n,r)=>{t._openGLRenderer=e.getFirstAncestorOfType("vtkOpenGLRenderer"),t._openGLRenderWindow=t._openGLRenderer.getLastAncestorOfType("vtkOpenGLRenderWindow");const o=t._openGLRenderer.getAspectRatio(),a=t._openGLRenderer?t._openGLRenderer.getRenderable().getActiveCamera():null,i=t._openGLRenderer.getTiledSizeAndOrigin();let s=null;if(t.renderable.getUseZValues()){const e=r.getZBufferTexture(),n=Math.floor(e.getWidth()),o=Math.floor(e.getHeight()),a=t._openGLRenderWindow.getContext();e.bind();const i=r.getFramebuffer();i?i.saveCurrentBindingsAndBuffers():Kg("No framebuffer to save/restore");const l=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,l),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,e.getHandle(),0),a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE&&(s=new Uint8Array(n*o*4),a.viewport(0,0,n,o),a.readPixels(0,0,n,o,a.RGBA,a.UNSIGNED_BYTE,s)),i&&i.restorePreviousBindingsAndBuffers(),a.deleteFramebuffer(l)}t.renderable.invokeCallback(t.renderable.getInputData(),a,o,i,s)},e.queryPass=(e,n)=>{e&&t.renderable.getUseZValues()&&n.requestDepth()}}const qg={};const Xg=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qg,n),Zt.extend(e,t,n),$g(e,t)}),"vtkOpenGLPixelSpaceCallbackMapper");rn("vtkPixelSpaceCallbackMapper",Xg);var Yg="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtktextureObjectVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n\nattribute vec4 vertexDC;\nattribute vec2 tcoordDC;\nvarying vec2 tcoordVC;\n\nvoid main()\n{\n  tcoordVC = tcoordDC;\n  gl_Position = vertexDC;\n}\n";const{Representation:Zg}=ds;function Qg(e,t,n,r){let[o,a]=t;const i=e.getContext(),s=Ud.newInstance({autoParameters:!1,wrapS:r,wrapT:r,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:i.FLOAT,baseLevel:0,maxLevel:0});return s.setOpenGLRenderWindow(e),s.setInternalFormat(i.RGBA32F),s.create2DFromRaw(o,a,4,"Float32Array",null),s.activate(),s.sendParameters(),s.deactivate(),s}function Jg(e,t){return Qg(e,t,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE)}function em(e,t){t.classHierarchy.push("vtkLICPingPongBufferManager"),t._openGLRenderWindow?(t.quad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",Zg.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow),t.context=t._openGLRenderWindow.getContext(),t.licTexture0=Jg(t._openGLRenderWindow,t.size),t.seedTexture0=Jg(t._openGLRenderWindow,t.size),t.licTexture1=Jg(t._openGLRenderWindow,t.size),t.seedTexture1=Jg(t._openGLRenderWindow,t.size),t.eeTexture=t.doEEPass?Qg(t._openGLRenderWindow,t.size,Ud.Filter.NEAREST,Ud.Wrap.CLAMP_TO_EDGE):null,t.imageVectorTexture=t.doVTPass?function(e,t){return Qg(e,t,Ud.Filter.LINEAR,Ud.Wrap.CLAMP_TO_EDGE)}(t._openGLRenderWindow,t.size):null,t.pingTextures[0]=t.licTexture0,t.pingTextures[1]=t.seedTexture0,t.pongTextures[0]=t.licTexture1,t.pongTextures[1]=t.seedTexture1,t.textures[0]=t.pingTextures,t.textures[1]=t.pongTextures,e.swap=()=>{t.readIndex=1-t.readIndex},e.renderQuad=(e,n)=>{const r=t.quad,o=t.context;let a=t.quadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.quadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.getLastLICBuffer=()=>0===t.readIndex?t.licTexture0:t.licTexture1,e.getLastSeedBuffer=()=>0===t.readIndex?t.seedTexture0:t.seedTexture1,e.getLICBuffer=()=>1-t.readIndex==0?t.licTexture0:t.licTexture1,e.getSeedBuffer=()=>1-t.readIndex==0?t.seedTexture0:t.seedTexture1,e.getLICTextureUnit=()=>{const e=t.textures[t.readIndex][0];return e.activate(),e.getTextureUnit()},e.getSeedTextureUnit=()=>{const e=t.textures[t.readIndex][1];return e.activate(),e.getTextureUnit()},e.getNoiseTextureUnit=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return 0===e?(t.noiseTexture.activate(),t.noiseTexture.getTextureUnit()):(t.eeTexture.activate(),t.eeTexture.getTextureUnit())},e.getVectorTextureUnit=()=>(t.vectorTexture.activate(),t.vectorTexture.getTextureUnit()),e.getImageVectorTextureUnit=()=>t.imageVectorTexture?(t.imageVectorTexture.activate(),t.imageVectorTexture.getTextureUnit()):e.getVectorTextureUnit(),e.getMaskVectorTextureUnit=()=>t.maskVectorTexture?(t.maskVectorTexture.activate(),t.maskVectorTexture.getTextureUnit()):e.getImageVectorTextureUnit(),e.clearBuffers=function(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),n.setColorBuffer(t.licTexture0,0),n.setColorBuffer(t.seedTexture0,1),n.setColorBuffer(t.licTexture1,2),n.setColorBuffer(t.seedTexture1,3);const o=[r.COLOR_ATTACHMENT0,r.COLOR_ATTACHMENT1,r.COLOR_ATTACHMENT2,r.COLOR_ATTACHMENT3];e&&(n.removeColorBuffer(4),n.setColorBuffer(t.eeTexture,4),o.push(r.COLOR_ATTACHMENT4)),r.drawBuffers(o),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeColorBuffer(3),e&&n.removeColorBuffer(4),r.drawBuffers([r.NONE])},e.clearBuffer=e=>{const n=t.framebuffer,r=t.context;n.removeColorBuffer(0),n.setColorBuffer(e,0),r.drawBuffers([r.COLOR_ATTACHMENT0]),r.clearColor(0,1,0,0),r.disable(r.SCISSOR_TEST),r.disable(r.BLEND),r.clear(r.COLOR_BUFFER_BIT),n.removeColorBuffer(e,0),r.drawBuffers([r.NONE])},e.activateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.activate():t.vectorTexture.activate(),t.maskVectorTexture&&t.maskVectorTexture.activate()},e.deactivateVectorTextures=()=>{t.imageVectorTexture?t.imageVectorTexture.deactivate():t.vectorTexture.deactivate(),t.maskVectorTexture&&t.maskVectorTexture.deactivate()},e.activateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.activate();break;case 1:t.eeTexture.activate();break;default:console.error("Wrong LIC pass number")}},e.deactivateNoiseTexture=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;switch(e){case 0:t.noiseTexture.deactivate();break;case 1:t.eeTexture.deactivate();break;default:console.error("Wrong LIC pass number")}},e.attachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.textures[1-t.readIndex],r=t.framebuffer,o=t.context;e[0].activate(),e[1].activate(),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(n[0],0),r.setColorBuffer(n[1],1),o.drawBuffers([o.COLOR_ATTACHMENT0,o.COLOR_ATTACHMENT1])},e.detachLICBuffers=()=>{const e=t.textures[t.readIndex],n=t.context,r=t.framebuffer;e[0].deactivate(),e[1].deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE])},e.attachImageVectorBuffer=()=>{const e=t.framebuffer,n=t.context;t.vectorTexture.activate(),e.removeColorBuffer(0),e.setColorBuffer(t.imageVectorTexture,0),n.drawBuffers([n.COLOR_ATTACHMENT0])},e.detachImageVectorBuffer=()=>{const e=t.context,n=t.framebuffer;t.vectorTexture.deactivate(),n.removeColorBuffer(0),e.drawBuffers([e.NONE])},e.attachEEBuffer=()=>{t.textures[t.readIndex][0].activate(),t.framebuffer.removeColorBuffer(0),t.framebuffer.setColorBuffer(t.eeTexture,0);const e=t.context;e.drawBuffers([e.COLOR_ATTACHMENT0])},e.detachEEBuffer=()=>{const e=t.context;t.framebuffer.removeColorBuffer(0),e.drawBuffers([e.NONE]),t.textures[t.readIndex][0].deactivate()},e.detachBuffers=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),e.drawBuffers([e.NONE]);const r=t.textures[t.readIndex],o=t.textures[1-t.readIndex];r[0]&&r[0].deactivate(),r[1]&&r[1].deactivate(),o[0]&&o[0].deactivate(),o[1]&&o[1].deactivate(),t.eeTexture&&t.eeTexture.deactivate(),t.noiseTexture&&t.noiseTexture.deactivate()},e.getWriteIndex=()=>1-t.readIndex,e.detachBuffers()):console.error("Pass renderwindow to ping pong manager")}const tm={vectorTexture:null,maskVectorTexture:null,noiseTexture:null,doEEPass:!1,doVTPass:!1,readIndex:0,quad:null,lastProgramHash:null,framebuffer:null,size:null,pingTextures:[],pongTextures:[],textures:[]};function nm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,tm,n),jt.obj(e,t),jt.get(e,t,["readIndex"]),jt.setGet(e,t,["doEEPass","doVTPass","_openGLRenderWindow","vectorTexture","maskVectorTexture","noiseTexture","framebuffer","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),em(e,t)}var rm={newInstance:jt.newInstance(nm,"vtkLICPingPongBufferManager"),extend:nm};const om=0,am=1,im=2,sm=3,lm=1;function cm(e,t){function n(e,t){e.setUniformi("texLIC",t.getLICTextureUnit()),e.setUniformi("texSeedPts",t.getSeedTextureUnit())}function r(e,t,n){e.attachLICBuffers(),e.renderQuad(t,n),e.detachLICBuffers(),e.swap()}t.classHierarchy.push("vtkLineIntegralConvolution2D"),e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.dumpTextureValues=function(e,n){let[r,o]=n,a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:4;const l=Xp.newInstance(),c=a;let u=null;return l.setOpenGLRenderWindow(i),l.saveCurrentBindingsAndBuffers(),l.create(r,o),l.populateFramebuffer(),l.setColorBuffer(e),u=new Float32Array(r*o*s),c.readPixels(0,0,r,o,4===s?c.RGBA:c.RGB,c.FLOAT,u),l.restorePreviousBindingsAndBuffers(),u},e.getTextureMinMax=function(n,r){let o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:t.context,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t._openGLRenderWindow;const i=e.dumpTextureValues(n,r,o,a,4);let s=Number.MAX_VALUE,l=Number.MIN_VALUE;for(let e=0;e<i.length;e+=4)if(0===i[e+1]){const t=i[e];t<s&&(s=t),t>l&&(l=t)}return{min:s,max:l}},e.getComponentSelectionProgram=e=>{const t="xyzw";return`.${t[e[0]]}${t[e[1]]}`},e.buildShaders=()=>{t.LIC0ShaderProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LIC0.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n/**\nThis shader initializes the convolution for the LIC computation.\n*/\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texMaskVectors;\nuniform sampler2D texNoise;\nuniform sampler2D texLIC;\n\nuniform int   uStepNo;         // in step 0 initialize lic and seeds, else just seeds\nuniform int   uPassNo;         // in pass 1 hpf of pass 0 is convolved.\nuniform float uMaskThreshold;  // if |V| < uMaskThreshold render transparent\nuniform vec2  uNoiseBoundsPt1; // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the *whole* domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value. this\n// depends on the pass number.\nvec2 getNoiseTC(vec2 vectc)\n{\n  // in pass 1 : convert from vector tc to noise tc\n  // in pass 2 : use vector tc\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(vectc);\n    }\n  else\n    {\n    return vectc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the\n// need to convert to noise texture coordinates.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\nvoid main(void)\n{\n  vec2 vectc = tcoordVC.st;\n\n  // lic => (convolution, mask, 0, step count)\n  if (uStepNo == 0)\n    {\n    float maskCriteria = length(texture2D(texMaskVectors, vectc).xyz);\n    float maskFlag;\n    if (maskCriteria <= uMaskThreshold)\n      {\n      maskFlag = 1.0;\n      }\n    else\n      {\n      maskFlag = 0.0;\n      }\n    float noise = getNoise(vectc);\n    LICOutput = vec4(noise, maskFlag, 0.0, 1.0);\n    }\n  else\n    {\n    LICOutput = texture2D(texLIC, vectc);\n    }\n\n  // initial seed\n  SeedOutput = vec4(vectc, 0.0, 1.0);\n}\n");const n=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_VT.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// move vector field to normalized image space\n// pre-processing for vtkLineIntegralConvolution2D\n\n// the output of this shader\n//VTK::Output::Dec\n\n// Fragment shader used by the gaussian blur filter render pass.\n\nuniform sampler2D texVectors; // input texture\nuniform vec2      uTexSize;   // size of texture\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  //VTK::LICComponentSelection::Impl\n  V = V/uTexSize;\n  gl_FragData[0] = vec4(V, 0.0, 1.0);\n}\n","//VTK::LICComponentSelection::Impl",`vec2 V = texture2D(texVectors, tcoordVC.st)${e.getComponentSelectionProgram(t.componentIds)};`).result;t.VTProgram=e.buildAShader(n);const r=gd.substitute("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs1.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D  texVectors;\nuniform sampler2D  texNoise;\nuniform sampler2D  texLIC;\nuniform sampler2D  texSeedPts;\n\nuniform int   uPassNo;          // in pass 1 hpf of pass 0 is convolved.\nuniform float uStepSize;        // step size in parametric space\n\nuniform vec2  uNoiseBoundsPt1;  // tc of upper right pt of noise texture\n\nin vec2 tcoordVC;\n\n//VTK::LICVectorLookup::Impl\n\n// We need to do this manually since CLAMP_TO_BORDER and and borderColor\n// are very poorly supported in webgl\nvec2 clampToBorder(vec2 uv){\n  if(uv.x < 0.0 || uv.x > 1.0 || uv.y < 0.0 || uv.y > 1.0)\n  {\n    return vec2(0.0, 0.0);\n  }\n  return getVector(uv);\n}\n\n// convert from vector coordinate space to noise coordinate space.\n// the noise texture is tiled across the whole domain\nvec2 VectorTCToNoiseTC(vec2 vectc)\n{\n  return vectc/uNoiseBoundsPt1;\n}\n\n// get the texture coordidnate to lookup noise value.\n// in pass 1 repeatedly tile the noise texture across\n// the computational domain.\nvec2 getNoiseTC(vec2 tc)\n{\n  if (uPassNo == 0)\n    {\n    return VectorTCToNoiseTC(tc);\n    }\n  else\n    {\n    return tc;\n    }\n}\n\n// look up noise value at the given location. The location\n// is supplied in vector texture coordinates, hence the need\n// to convert to either noise or lic texture coordinates in\n// pass 1 and 2 respectively.\nfloat getNoise(vec2 vectc)\n{\n  return texture2D(texNoise, getNoiseTC(vectc)).r;\n}\n\n// fourth-order Runge-Kutta streamline integration\n// no bounds checks are made, therefore it's essential\n// to have the entire texture initialized to 0\n// and set clamp to border and have border color 0\n// an integer is set if the step was taken, keeping\n// an accurate step count is necessary to prevent\n// boundary artifacts. Don't count the step if\n// all vector lookups are identically 0. This is\n// a proxy for \"stepped outside valid domain\"\nvec2 rk4(vec2 pt0, float dt, out bool count)\n{\n  count=true;\n  float dtHalf = dt * 0.5;\n  vec2 pt1;\n\n  vec2 v0 = clampToBorder(pt0);\n  pt1 = pt0 + v0 * dtHalf;\n\n  vec2 v1 = clampToBorder(pt1);\n  pt1 = pt0 + v1 * dtHalf;\n\n  vec2 v2 = clampToBorder(pt1);\n  pt1 = pt0 + v2 * dt;\n\n  vec2 v3 = clampToBorder(pt1);\n  vec2 vSum = v0 + v1 + v1 + v2 + v2 + v3;\n\n  if (vSum == vec2(0.0, 0.0))\n    {\n      count = false;\n    }\n\n  pt1 = pt0 + (vSum) * (dt * (1.0/6.0));\n\n return pt1;\n}\n\nvoid main(void)\n{\n  vec2 lictc = tcoordVC.st;\n  vec4 lic = texture2D(texLIC, lictc);\n  vec2 pt0 = texture2D(texSeedPts, lictc).st;\n\n  bool count;\n  vec2 pt1 = rk4(pt0, uStepSize, count);\n\n  if (count)\n    {\n    // accumulate lic step\n    // (lic, mask, 0, step count)\n    float noise = getNoise(pt1);\n    LICOutput = vec4(lic.r + noise, lic.g, 0.0, lic.a + 1.0);\n    SeedOutput = vec4(pt1, 0.0, 1.0);\n    }\n  else\n    {\n    // keep existing values\n    LICOutput = lic;\n    SeedOutput = vec4(pt0, 0.0, 1.0);\n    }\n}\n","//VTK::LICVectorLookup::Impl",function(){const e="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      vec2 V = texture2D( texVectors, vectc ).xy;\n\n      // normalize if |V| not 0\n\n      float lenV = length( V );\n\n      if ( lenV > 1.0e-8 )\n\n        {\n\n        return V/lenV;\n\n        }\n\n      else\n\n        {\n\n        return vec2( 0.0, 0.0 );\n\n        }\n\n      }\n\n    ",t="\n    vec2 getVector( vec2 vectc )\n\n      {\n\n      return texture2D( texVectors, vectc ).xy;\n\n      }\n\n    ";return arguments.length>0&&void 0!==arguments[0]&&!arguments[0]?t:e}(t.normalizeVectors),!0).result;t.LICIShaderProgram=e.buildAShader(r),t.LICNShaderProgram=e.buildAShader(" //VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_LICN.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n/**\nThis shader finalizes the convolution for the LIC computation\napplying the normalization. eg. if box kernel is used the this\nis the number of steps taken.\n*/\n\nuniform sampler2D texLIC;\n\nin vec2 tcoordVC;\n\nvoid main(void)\n{\n  vec4 conv = texture2D(texLIC, tcoordVC.st);\n  conv.r = conv.r/conv.a;\n  // lic => (convolution, mask, 0, 1)\n  LICOutput = vec4(conv.rg , 0.0, 1.0);\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.CEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// gray scale contrast enhance stage implemented via histogram stretching\n// if the min and max are tweaked it can generate out-of-range values\n// these will be clamped in 0 to 1\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\n\nuniform sampler2D texLIC;  // most recent lic pass\nuniform float uMin;        // min gray scale color value\nuniform float uMaxMinDiff; // max-min\n\nin vec2 tcoordVC;\n\nvoid main( void )\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g!=0.0)\n    {\n    LICOutput = lic;\n    }\n  else\n    {\n    float CElic = clamp((lic.r - uMin)/uMaxMinDiff, 0.0, 1.0);\n    LICOutput = vec4(CElic, lic.gb, 1.0);\n    }\n    SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.EEProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// high-pass filter stage employed by vtkLineIntegralConvolution2D\n// between LIC pass 1 and LIC pass 2. filtered LIC pass 1, becomes\n// noise for pass2.\n\n// the output of this shader\nlayout(location = 0) out vec4 EEOutput;\n\nuniform sampler2D texLIC; // most recent lic pass\nuniform float     uDx;    // fragment size\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n// kernel for simple laplace edge enhancement.\n// p=Laplace(p)+p\nfloat K[9] = float[9](\n  -1.0, -1.0, -1.0,\n  -1.0,  9.0, -1.0,\n  -1.0, -1.0, -1.0\n  );\n\n// determine if the fragment was masked\nbool Masked(float val) { return val != 0.0; }\n\nvoid main(void)\n{\n  // tex coord neighbor offsets\n  vec2 fragDx[9] = vec2[9](\n    vec2(-uDx, uDy), vec2(0.0, uDy), vec2(uDx, uDy),\n    vec2(-uDx, 0.0), vec2(0.0, 0.0), vec2(uDx, 0.0),\n    vec2(-uDx,-uDy), vec2(0.0,-uDy), vec2(uDx,-uDy)\n    );\n\n  vec2 lictc = tcoordVC.st;\n\n  // compute the convolution but don't use convovled values if\n  // any masked fragments on the stencil. Fragments outside\n  // the valid domain are masked during initialization, and\n  // texture wrap parameters are clamp to border with border\n  // color that contains masked flag\n  float conv = 0.0;\n  bool dontUse = false;\n  for (int i=0; i<9; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    vec4 lic = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic.g);\n    conv = conv + K[i] * lic.r;\n    }\n\n  if (dontUse)\n    {\n    EEOutput = vec4(texture2D(texLIC, lictc).rg, 0.0, 1.0);\n    }\n  else\n    {\n    conv = clamp(conv, 0.0, 1.0);\n    EEOutput = vec4(conv,texture2D(texLIC, lictc).g, 0.0, 1.0);\n    }\n\n}\n"),t.AAHProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAH.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// horizontal pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDx;    // fragment size\n\nin vec2 tcoordVC;\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDx[3] = vec2[3](vec2(-uDx,0.0), vec2(0.0,0.0), vec2(uDx,0.0));\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDx[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n"),t.AAVProgram=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkLineIntegralConvolution2D_AAV.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// Anti-alias stage in vtkLineIntegralConvolution2D\n// vertical pass of a Gaussian convolution\n\n// the output of this shader\nlayout(location = 0) out vec4 LICOutput;\nlayout(location = 1) out vec4 SeedOutput;\n\nuniform sampler2D texLIC; // input texture\nuniform float     uDy;    // fragment size\n\nin vec2 tcoordVC;\n\n\n// factored 3x3 Gaussian kernel\n// K^T*K = G\nfloat K[3] = float[3](0.141421356, 0.707106781, 0.141421356);\n\n// determine if the fragment was masked\nbool Masked(float val){ return val != 0.0; }\n\nvoid main(void)\n{\n// neighbor offsets\nvec2 fragDy[3] = vec2[3](vec2(0.0,-uDy), vec2(0.0,0.0), vec2(0.0,uDy));\n\n\n  vec2 lictc = tcoordVC.st;\n  vec4 lic[3];\n  bool dontUse = false;\n  float conv = 0.0;\n  for (int i=0; i<3; ++i)\n    {\n    vec2 tc = lictc + fragDy[i];\n    lic[i] = texture2D(texLIC, tc);\n    dontUse = dontUse || Masked(lic[i].g);\n    conv = conv + K[i] * lic[i].r;\n    }\n  // output is (conv, mask, skip, 1)\n  if (dontUse)\n    {\n    LICOutput = vec4(lic[1].rg, 1.0, 1.0);\n    }\n  else\n    {\n    LICOutput = vec4(conv, lic[1].gb, 1.0);\n    }\n  SeedOutput = vec4(0.0, 0.0, 0.0, 0.0);\n}\n")},e.executeLIC=(o,a,i,s,l,c)=>{if(t._openGLRenderWindow=l,t.context=l.getContext(),Object.assign(t,c),o[0]<=0||o[1]<=0)return null;const u=[1/o[0],1/o[1]];let d=t.stepSize*Math.sqrt(u[0]*u[0]+u[1]*u[1]);d<=0&&(d=1e-10);const p=t.context;let f=t.framebuffer;const g=f.getSize();f&&g&&o[0]===g&&o[1]===g||(f=Xp.newInstance(),f.setOpenGLRenderWindow(t._openGLRenderWindow),f.saveCurrentBindingsAndBuffers(),f.create(...o),f.populateFramebuffer(),f.restorePreviousBindingsAndBuffers(),t.framebuffer=f),f.saveCurrentBindingsAndBuffers(),f.bind(),p.viewport(0,0,...o),p.scissor(0,0,...o),t.shadersNeedBuild&&(e.buildShaders(),t.shadersNeedBuild=!1),t.bufs?(t.bufs.setVectorTexture(a),t.bufs.setMaskVectorTexture(i),t.bufs.setNoiseTexture(s)):t.bufs=rm.newInstance({openGLRenderWindow:l,doEEPass:t.enhancedLIC,doVTPass:t.transformVectors,vectorTexture:a,maskVectorTexture:i,noiseTexture:s,framebuffer:f,size:o});const m=[(s.getWidth()+1)/o[0],(s.getHeight()+1)/o[1]],h=1/o[0],v=1/o[1],y=t._openGLRenderWindow.getShaderCache();if(t.transformVectors){const e=t.VTProgram;y.readyShaderProgram(e),t.bufs.attachImageVectorBuffer(),e.setUniform2f("uTexSize",...o),e.setUniformi("texVectors",t.bufs.getVectorTextureUnit()),p.clearColor(0,0,0,0),p.clear(p.COLOR_BUFFER_BIT),t.bufs.renderQuad(o,e),t.bufs.detachImageVectorBuffer()}t.bufs.clearBuffers(t.enhancedLIC),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(0);const{LIC0ShaderProgram:T}=t;y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",0),T.setUniformf("uMaskThreshold",t.maskThreshold),T.setUniform2f("uNoiseBoundsPt1",...m),T.setUniformi("texMaskVectors",t.bufs.getMaskVectorTextureUnit()),T.setUniformi("texLIC",t.bufs.getLICTextureUnit()),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0)),r(t.bufs,o,T);const{LICIShaderProgram:b}=t;y.readyShaderProgram(b),b.setUniformi("uPassNo",0),b.setUniformf("uStepSize",-d),b.setUniform2f("uNoiseBoundsPt1",...m),b.setUniformi("texVectors",t.bufs.getImageVectorTextureUnit()),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(0));for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<t.numberOfSteps;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(0),t.bufs.deactivateVectorTextures();const{LICNShaderProgram:x}=t;if(y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),r(t.bufs,o,x),t.enhancedLIC){t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!1,o),t.bufs.attachEEBuffer();const{EEProgram:a}=t;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDx",h),a.setUniformf("uDy",v),t.bufs.renderQuad(o,a),t.bufs.detachEEBuffer(),t.bufs.detachBuffers(),t.bufs.clearBuffers(!1),t.bufs.activateVectorTextures(),t.bufs.activateNoiseTexture(1),y.readyShaderProgram(T),T.setUniformi("uStepNo",0),T.setUniformi("uPassNo",1),n(T,t.bufs),T.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1)),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformi("uPassNo",1),b.setUniformf("uStepSize",-d),b.setUniformi("texNoise",t.bufs.getNoiseTextureUnit(1));const i=t.numberOfSteps/2;for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);y.readyShaderProgram(T),T.setUniformi("uStepNo",1),n(T,t.bufs),r(t.bufs,o,T),y.readyShaderProgram(b),b.setUniformf("uStepSize",d);for(let e=0;e<i;++e)n(b,t.bufs),r(t.bufs,o,b);t.bufs.deactivateNoiseTexture(1),t.bufs.deactivateVectorTextures(),y.readyShaderProgram(x),x.setUniformi("texLIC",t.bufs.getLICTextureUnit()),x.setUniformi("texSeedPts",t.bufs.getSeedTextureUnit()),r(t.bufs,o,x)}if(t.antiAlias){const e=t.AAHProgram;y.readyShaderProgram(e),e.setUniformi("texLIC",t.bufs.getLICTextureUnit()),e.setUniformf("uDx",h);const a=t.AAVProgram;y.readyShaderProgram(a),a.setUniformi("texLIC",t.bufs.getLICTextureUnit()),a.setUniformf("uDy",v);for(let i=0;i<t.antiAlias;++i)y.readyShaderProgram(e),n(e,t.bufs),r(t.bufs,o,e),y.readyShaderProgram(a),n(a,t.bufs),r(t.bufs,o,a)}return t.enhanceContrast!==am&&t.enhanceContrast!==sm||e.contrastEnhance(!0,o),t.bufs.detachBuffers(),f.restorePreviousBindingsAndBuffers(),t.bufs.getLastLICBuffer()},e.contrastEnhance=(n,o)=>{const a=t._openGLRenderWindow.getShaderCache();let{min:i,max:s}=e.getTextureMinMax(t.bufs.getLastLICBuffer(),o,t.context,t._openGLRenderWindow);(s<=i||s>1||i<0)&&(console.error("Invalid color range: ",i,s),i=0,s=1);let l=s-i;n&&(i+=l*t.lowLICContrastEnhancementFactor,s-=l*t.highLICContrastEnhancementFactor,l=s-i);const{CEProgram:c}=t;a.readyShaderProgram(c),c.setUniformi("texLIC",t.bufs.getLICTextureUnit()),c.setUniformf("uMin",i),c.setUniformf("uMaxMinDiff",l),r(t.bufs,o,c)}}const um={shadersNeedBuild:!0,stepSize:1,numberOfSteps:10,enhancedLIC:!0,enhanceContrast:!1,lowContrastEnhancementFactor:0,highContrastEnhancementFactor:0,antiAlias:0,componentIds:[0,1],normalizeVectors:!0,maskThreshold:0,transformVectors:!0,bufs:null,isComposite:!0};function dm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,um,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","nuberOfSteps","stepSize","normalizeVectors","maskThreshold","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","componentIds","isComposite"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),cm(e,t)}var pm={newInstance:jt.newInstance(dm,"vtkLineIntegralConvolution2D"),extend:dm};function fm(e,t){t.classHierarchy.push("vtkSurfaceLICInterface")}const gm={enableLIC:!1,nuberOfSteps:40,stepSize:.25,transformVectors:!0,normalizeVectors:!0,maskOnSurface:!1,maskThreshold:0,maskColor:[0,0,0],maskIntensity:0,enhancedLIC:!0,enhanceContrast:om,lowLICContrastEnhancementFactor:0,highLICContrastEnhancementFactor:0,lowColorContrastEnhancementFactor:0,highColorContrastEnhancementFactor:0,antiAlias:0,colorMode:0,LICIntensity:1,mapModeBias:0,noiseTextureSize:200,noiseTextureType:lm,noiseGrainSize:8,noiseImpulseProbability:.1,noiseImpulseBackgroundValue:0,noiseGeneratorSeed:0,minNoiseValue:0,maxNoiseValue:1,numberOfNoiseLevels:2,shadersNeedBuilding:!0,reallocateTextures:!0,rebuildNoiseTexture:!1,viewPortScale:1};function mm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,gm,n),jt.obj(e,t),jt.setGet(e,t,["enableLIC","numberOfSteps","stepSize","normalizeVectors","transformVectors","maskOnSurface","maskThreshold","maskColor","maskIntensity","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor","antiAlias","colorMode","LICIntensity","mapModeBias","noiseTextureSize","noiseTextureType","noiseGrainSize","minNoiseValue","maxNoiseValue","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","noiseGeneratorSeed","viewPortScale","rebuildNoiseTexture"]),fm(0,t)}var hm={newInstance:jt.newInstance(mm,"vtkSurfaceLICInterface"),extend:mm};const{Representation:vm}=ds;function ym(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICInterface"),e.renderQuad=(e,n)=>{const r=t.licQuad,o=t.context;let a=t.licQuadVAO;a||(a=yd.newInstance(),a.setOpenGLRenderWindow(t._openGLRenderWindow),t.licQuadVAO=a),t.previousProgramHash!==n.getMd5Hash()&&(a.shaderProgramChanged(),r.getCABO().bind(),a.addAttributeArray(n,r.getCABO(),"vertexDC",r.getCABO().getVertexOffset(),r.getCABO().getStride(),t.context.FLOAT,3,t.context.FALSE),a.addAttributeArray(n,r.getCABO(),"tcoordDC",r.getCABO().getTCoordOffset(),r.getCABO().getStride(),t.context.FLOAT,2,t.context.FALSE),t.previousProgramHash=n.getMd5Hash()),o.drawArrays(o.TRIANGLES,0,r.getCABO().getElementCount()),a.release()},e.generateNoiseTexture=e=>{if(!t.noiseTexture||t.licInterface.getRebuildNoiseTexture()){t.licInterface.setRebuildNoiseTexture(!1),t.noiseTexture&&t.noiseTexture.releaseGraphicsResources(),ho()(t.noiseGeneratorSeed,{global:!0});let n=[];const{noiseTextureType:r,noiseGrainSize:o,numberOfNoiseLevels:a,noiseImpulseProbability:i,noiseImpulseBackgroundValue:s,minNoiseValue:l,maxNoiseValue:c}=t.licInterface.get("noiseTextureType","noiseGrainSize","numberOfNoiseLevels","noiseImpulseProbability","noiseImpulseBackgroundValue","minNoiseValue","maxNoiseValue");n=r===lm?function(e,t,n,r,o,a){const i=Math.max(0,Math.min(1,n)),s=Float32Array.from({length:e*e},(()=>{let e=0;if(1===i||Math.random()>1-i)for(let t=0;t<2048;++t)e+=Math.random();return e}));let l=0,c=2049;s.forEach((e=>{c=1===i?e<c?e:c:e<c&&e>0?e:c,l=e>l?e:l}));let u=l-c;0===u&&(c=0,u=0===l?1:l);const d=t-1,p=0!==d?1/d:0,f=a-o;return s.map((e=>{const n=e<c?e:(e-c)/u,i=Math.floor(n*t);return e>=c?1===t?a:o+(i>d?d:i)*p*f:r}))}(Math.floor(e/o),a,i,s,l,c):function(e,t,n,r){let[o,a]=e;const i=r-n;return Float32Array.from({length:o*a},(()=>{let e=Math.random();return e=Math.floor(e*t)/t,e=e*i+n,e>1?1:e<0?0:e}))}([Math.ceil(e/o),Math.ceil(e/o)],a,l,c);const u=1/o,d=Float32Array.from({length:e*e*4},((t,r)=>{const a=r/4;if(r%4==0){const t=Math.floor(a%e*u),r=Math.floor(a/e*u);return n[r*(e/o)+t]}return r%4==1||r%4==3?1:0})),p=Ud.newInstance({wrapS:Ud.Wrap.REPEAT,wrapT:Ud.Wrap.REPEAT,minificationFilter:Ud.Filter.NEAREST,magnificationFilter:Ud.Filter.NEAREST,generateMipMap:!1,openGLDataType:t.context.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});p.setOpenGLRenderWindow(t._openGLRenderWindow),p.create2DFromRaw(e,e,4,"Float32Array",d),p.activate(),p.sendParameters(),p.deactivate(),t.noiseTexture=p}},e.buildAShader=e=>t._openGLRenderWindow.getShaderCache().readyShaderProgramArray(Yg,e,""),e.allocateTextures=()=>{const n=Ud.Filter.NEAREST,r=Ud.Filter.LINEAR,o=t._openGLRenderWindow;t.geometryImage||(t.geometryImage=e.allocateTexture(o,n)),t.vectorImage||(t.vectorImage=e.allocateTexture(o,r)),t.maskVectorImage||(t.maskVectorImage=e.allocateTexture(o,r)),t.LICImage||(t.LICImage=e.allocateTexture(o,n)),t.RGBColorImage||(t.RGBColorImage=e.allocateTexture(o,n)),t.HSLColorImage||(t.HSLColorImage=e.allocateTexture(o,n)),t.depthTexture||(t.depthTexture=e.allocateDepthTexture(o))},e.allocateTexture=(e,n)=>{const r=t.context,o=Ud.newInstance({wrapS:Ud.Wrap.CLAMP_TO_EDGE,wrapT:Ud.Wrap.CLAMP_TO_EDGE,minificationFilter:n,magnificationFilter:n,generateMipmap:!1,openGLDataType:r.FLOAT,baseLevel:0,maxLevel:0,autoParameters:!1});return o.setOpenGLRenderWindow(e),o.setInternalFormat(r.RGBA32F),o.create2DFromRaw(...t.size,4,"Float32Array",null),o.activate(),o.sendParameters(),o.deactivate(),o},e.allocateDepthTexture=e=>{const n=t.context,r=Ud.newInstance({generateMipmap:!1,openGLDataType:n.FLOAT,autoParameters:!1});return r.setOpenGLRenderWindow(e),r.createDepthFromRaw(...t.size,"Float32Array",null),r.activate(),r.sendParameters(),r.deactivate(),r},e.createFBO=()=>{if(!t.framebuffer){t.licHelper=null;const e=Xp.newInstance();e.setOpenGLRenderWindow(t._openGLRenderWindow),e.saveCurrentBindingsAndBuffers(),e.create(...t.size),e.populateFramebuffer(),t.framebuffer=e,e.restorePreviousBindingsAndBuffers()}},e.completedGeometry=()=>{const e=t.context,n=t.framebuffer;n.removeColorBuffer(0),n.removeColorBuffer(1),n.removeColorBuffer(2),n.removeDepthBuffer(),e.drawBuffers([e.NONE]),n.restorePreviousBindingsAndBuffers()},e.buildAllShaders=()=>{t.shadersNeedBuilding&&(t.licColorPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_fs2.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader combines surface geometry, LIC, and  scalar colors.\n\n// the output of this shader\nlayout(location = 0) out vec4 RGBOutput;\nlayout(location = 1) out vec4 HSLOutput;\n\nuniform sampler2D texVectors;       // vectors, depth\nuniform sampler2D texGeomColors;    // scalar colors + lighting\nuniform sampler2D texLIC;           // image lic\nuniform int       uScalarColorMode; // select between blend, and map shader\nuniform float     uLICIntensity;    // blend shader: blending factor for lic'd colors\nuniform float     uMapBias;         // map shader: adjust the brightness of the result\nuniform float     uMaskIntensity;   // blending factor for mask color\nuniform vec3      uMaskColor;       // color for the masked out fragments\n\nin vec2 tcoordVC;\n\n/**\nConvert from RGB color space into HSL colorspace.\n*/\nvec3 RGBToHSL(vec3 RGB)\n{\n  vec3 HSL = vec3(0.0, 0.0, 0.0);\n\n  float RGBMin = min(min(RGB.r, RGB.g), RGB.b);\n  float RGBMax = max(max(RGB.r, RGB.g), RGB.b);\n  float RGBMaxMinDiff = RGBMax - RGBMin;\n\n  HSL.z = (RGBMax + RGBMin) / 2.0;\n\n  if (RGBMaxMinDiff == 0.0)\n    {\n    // Gray scale\n    HSL.x = 0.0;\n    HSL.y = 0.0;\n    }\n  else\n    {\n    // Color\n    if (HSL.z < 0.5)\n      HSL.y = RGBMaxMinDiff / (RGBMax + RGBMin);\n    else\n      HSL.y = RGBMaxMinDiff / (2.0 - RGBMax - RGBMin);\n\n    float dR\n      = (((RGBMax - RGB.r) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dG\n      = (((RGBMax - RGB.g) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n    float dB\n      = (((RGBMax - RGB.b) / 6.0) + (RGBMaxMinDiff / 2.0)) / RGBMaxMinDiff;\n\n    if (RGB.r == RGBMax)\n      HSL.x = dB - dG;\n    else\n    if (RGB.g == RGBMax)\n      HSL.x = (1.0 / 3.0) + dR - dB;\n    else\n    if (RGB.b == RGBMax)\n      HSL.x = (2.0 / 3.0) + dG - dR;\n\n    if (HSL.x < 0.0)\n      HSL.x += 1.0;\n\n    if (HSL.x > 1.0)\n      HSL.x -= 1.0;\n    }\n\n  return HSL;\n}\n\n/**\nHelper for HSL to RGB conversion.\n*/\nfloat Util(float v1, float v2, float vH)\n{\n  if (vH < 0.0)\n    vH += 1.0;\n\n  if (vH > 1.0)\n     vH -= 1.0;\n\n  if ((6.0 * vH) < 1.0)\n    return (v1 + (v2 - v1) * 6.0 * vH);\n\n  if ((2.0 * vH) < 1.0)\n    return (v2);\n\n  if ((3.0 * vH) < 2.0)\n    return (v1 + (v2 - v1) * ((2.0 / 3.0) - vH) * 6.0);\n\n  return v1;\n}\n\n/**\nConvert from HSL space into RGB space.\n*/\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  if (HSL.y == 0.0)\n    {\n    // Gray\n    RGB.r = HSL.z;\n    RGB.g = HSL.z;\n    RGB.b = HSL.z;\n    }\n  else\n    {\n    // Chromatic\n    float v2;\n    if (HSL.z < 0.5)\n      v2 = HSL.z * (1.0 + HSL.y);\n    else\n      v2 = (HSL.z + HSL.y) - (HSL.y * HSL.z);\n\n    float v1 = 2.0 * HSL.z - v2;\n\n    RGB.r = Util(v1, v2, HSL.x + (1.0 / 3.0));\n    RGB.g = Util(v1, v2, HSL.x);\n    RGB.b = Util(v1, v2, HSL.x - (1.0 / 3.0));\n    }\n\n  return RGB.rgb;\n}\n\nvoid main()\n{\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n\n  // depth is used to determine which fragment belong to us\n  // and we can change\n  float depth = texture2D(texVectors, tcoordVC.st).a;\n\n  vec3 fragColorRGB;\n  float valid;\n  if (depth > 1.0e-3)\n    {\n    // we own it\n    // shade LIC'ed geometry, or apply mask\n    if (lic.g!=0.0)\n      {\n      // it's masked\n      // apply fragment mask\n      fragColorRGB = uMaskIntensity * uMaskColor + (1.0 - uMaskIntensity) * geomColor.rgb;\n      valid = 0.0;\n      }\n    else\n      {\n      if (uScalarColorMode==0)\n        {\n        // blend with scalars\n        fragColorRGB = lic.rrr * uLICIntensity + geomColor.rgb * (1.0 - uLICIntensity);\n        }\n      else\n        {\n        // multiply with scalars\n        fragColorRGB = geomColor.rgb * clamp((uMapBias + lic.r), 0.0, 1.0);\n        }\n      if (lic.b != 0.0)\n        {\n        // didn't have the required guard pixels\n        // don't consider it in min max estimation\n        // for histpgram stretching\n        valid = 0.0;\n        }\n      else\n        {\n        // ok to use in min/max estimates for histogram\n        // stretching\n        valid = 1.0;\n        }\n      }\n    }\n  else\n    {\n    // we don't own it\n    // pass through scalars\n    fragColorRGB = geomColor.rgb;\n    valid = 0.0;\n    }\n\n  // if no further stages this texture is\n  // copied to the screen\n  RGBOutput = vec4(fragColorRGB, geomColor.a);\n\n  // if further stages, move to hsl space for contrast\n  // enhancement. encoding validity saves moving a texture to the cpu\n  vec3 fragColorHSL = RGBToHSL(fragColorRGB);\n  HSLOutput = vec4(fragColorHSL, valid);\n}\n"),t.licCopyPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_DCpy.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// This shader copies fragments and depths to the output buffer\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texDepth;     // z values from vertex shader\nuniform sampler2D texRGBColors; // final rgb LIC colors\n\nin vec2 tcoordVC;\n\nvoid main()\n{\n  gl_FragDepth = texture2D(texDepth, tcoordVC).x;\n  gl_FragData[0] = texture2D(texRGBColors, tcoordVC);\n\n  // since we render a screen aligned quad\n  // we're going to be writing fragments\n  // not touched by the original geometry\n  // it's critical not to modify those\n  // fragments.\n  if (gl_FragDepth == 1.0)\n    {\n    discard;\n    }\n}\n"),t.enhanceContrastPass=e.buildAShader("//VTK::System::Dec\n\n//=========================================================================\n//\n//  Program:   Visualization Toolkit\n//  Module:    vtkSurfaceLICMapper_CE.glsl\n//\n//  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n//  All rights reserved.\n//  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n//\n//     This software is distributed WITHOUT ANY WARRANTY; without even\n//     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n//     PURPOSE.  See the above copyright notice for more information.\n//\n//=========================================================================\n\n// color contrast enhance stage implemented via histogram stretching\n// on lightness channel. if the min and max are tweaked it can generate\n// out-of-range values these will be clamped in 0 to 1\n\n// the output of this shader\n//VTK::Output::Dec\n\nuniform sampler2D texGeomColors; // scalars + lighting\nuniform sampler2D texLIC;        // image lic, mask\nuniform sampler2D texHSLColors;  // hsla colors\n\nuniform float     uLMin;         // min lightness over all fragments\nuniform float     uLMaxMinDiff;  // max - min lightness over all fragments\n\nin vec2 tcoordVC;\n\nvec3 HSLToRGB(vec3 HSL)\n{\n  vec3 RGB;\n  float v;\n  float h = HSL.x;\n  float sl = HSL.y;\n  float l = HSL.z;\n\n  v = (l <= 0.5) ? (l * (1.0 + sl)) : (l + sl - l * sl);\n  if (v <= 0.0) {\n    RGB = vec3(0.0,0.0,0.0);\n  } else {\n    float m;\n    int sextant;\n    float fract, vsf, mid1, mid2;\n\n    m = l + l - v;\n    h *= 6.0;\n    sextant = int(h);\n    fract = h - float(sextant);\n\n    vsf = (v - m) * fract;\n    mid1 = m + vsf;\n    mid2 = v - vsf;\n    switch (sextant) {\n      case 0: RGB.r = v; RGB.g = mid1; RGB.b = m; break;\n      case 1: RGB.r = mid2; RGB.g = v; RGB.b = m; break;\n      case 2: RGB.r = m; RGB.g = v; RGB.b = mid1; break;\n      case 3: RGB.r = m; RGB.g = mid2; RGB.b = v; break;\n      case 4: RGB.r = mid1; RGB.g = m; RGB.b = v; break;\n      case 5: RGB.r = v; RGB.g = m; RGB.b = mid2; break;\n    }\n  }\n  return RGB;\n}\n\nvoid main()\n{\n  // lookup hsl color , mask\n  vec4 fragColor = texture2D(texHSLColors, tcoordVC.st);\n\n  // don't modify masked fragments (masked => lic.g==1)\n  vec4 lic = texture2D(texLIC, tcoordVC.st);\n  if (lic.g==0.0)\n    {\n    // normalize lightness channel\n    fragColor.z = clamp((fragColor.z - uLMin)/uLMaxMinDiff, 0.0, 1.0);\n    }\n\n  // back into rgb space\n  fragColor.rgb = HSLToRGB(fragColor.xyz);\n\n  // add alpha\n  vec4 geomColor = texture2D(texGeomColors, tcoordVC.st);\n  fragColor.a = geomColor.a;\n\n  gl_FragData[0] = fragColor;\n}\n"),t.shadersNeedBuilding=!1)},e.initializeResources=()=>{e.createFBO(),e.generateNoiseTexture(t.licInterface.getNoiseTextureSize()),e.allocateTextures(),e.buildAllShaders(),t.licQuad||(t.licQuad=function(e){const t=Sd.newInstance();t.setOpenGLRenderWindow(e);const n=new Float32Array(12);for(let e=0;e<4;e++)n[3*e]=e%2*2-1,n[3*e+1]=e>1?1:-1,n[3*e+2]=0;const r=new Float32Array([0,0,1,0,0,1,1,1]),o=new Uint16Array(8);o[0]=3,o[1]=0,o[2]=1,o[3]=3,o[4]=3,o[5]=0,o[6]=3,o[7]=2;const a=Ds.newInstance({numberOfComponents:3,values:n});a.setName("points");const i=Ds.newInstance({numberOfComponents:1,values:o}),s=Ds.newInstance({numberOfComponents:2,values:r});return t.getCABO().createVBO(i,"polys",vm.SURFACE,{points:a,cellOffset:0,tcoords:s}),t}(t._openGLRenderWindow)),t.licHelper||(t.licHelper=pm.newInstance())},e.prepareForGeometry=()=>{const e=t.framebuffer;e.saveCurrentBindingsAndBuffers(),e.bind(),t.geometryImage.activate(),t.vectorImage.activate(),t.maskVectorImage.activate(),e.removeColorBuffer(0),e.removeColorBuffer(2),e.removeColorBuffer(3),e.setColorBuffer(t.geometryImage,0),e.setColorBuffer(t.vectorImage,2),e.setColorBuffer(t.maskVectorImage,3),e.setDepthBuffer(t.depthTexture);const n=t.context;n.drawBuffers([n.COLOR_ATTACHMENT0,n.NONE,n.COLOR_ATTACHMENT2,n.COLOR_ATTACHMENT3]),n.viewport(0,0,...t.size),n.scissor(0,0,...t.size),n.disable(n.BLEND),n.disable(n.DEPTH_TEST),n.disable(n.SCISSOR_TEST),n.clearColor(0,0,0,0),n.clear(n.DEPTH_BUFFER_BIT|n.COLOR_BUFFER_BIT)},e.copyToScreen=n=>{t.RGBColorImage.activate(),t.depthTexture.activate(),t.licCopyPass||e.initializeResources();const r=t.licCopyPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(r);const o=t.context;o.viewport(0,0,...n),o.scissor(0,0,...n),o.disable(o.BLEND),o.enable(o.DEPTH_TEST),o.disable(o.SCISSOR_TEST),r.setUniformi("texDepth",t.depthTexture.getTextureUnit()),r.setUniformi("texRGBColors",t.RGBColorImage.getTextureUnit()),e.renderQuad(n,r),t.RGBColorImage.deactivate(),t.depthTexture.deactivate()},e.combineColorsAndLIC=()=>{const n=t.context,r=t.framebuffer;r.saveCurrentBindingsAndBuffers(),r.bind(),r.create(...t.size),r.removeColorBuffer(0),r.removeColorBuffer(1),r.setColorBuffer(t.RGBColorImage,0),r.setColorBuffer(t.HSLColorImage,1),n.drawBuffers([n.COLOR_ATTACHMENT0,n.COLOR_ATTACHMENT1]),n.disable(n.DEPTH_TEST),n.clearColor(0,0,0,0),n.clear(n.COLOR_BUFFER_BIT),t.vectorImage.activate(),t.geometryImage.activate(),t.LICImage.activate(),t.licColorPass||e.initializeResources();const o=t.licColorPass;t._openGLRenderWindow.getShaderCache().readyShaderProgram(o),o.setUniformi("texVectors",t.vectorImage.getTextureUnit()),o.setUniformi("texGeomColors",t.geometryImage.getTextureUnit());const{colorMode:a,LICIntensity:i,mapModeBias:s,maskIntensity:l,maskColor:c,enhanceContrast:u,lowColorContrastEnhancementFactor:d,highColorContrastEnhancementFactor:p}=t.licInterface.get("colorMode","LICIntensity","mapModeBias","maskIntensity","maskColor","enhanceContrast","lowColorContrastEnhancementFactor","highColorContrastEnhancementFactor");if(o.setUniformi("texLIC",t.LICImage.getTextureUnit()),o.setUniformi("uScalarColorMode",a),o.setUniformf("uLICIntensity",i),o.setUniformf("uMapBias",s),o.setUniformf("uMaskIntensity",l),o.setUniform3f("uMaskColor",...c),e.renderQuad(t.size,o),t.vectorImage.deactivate(),t.geometryImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),r.removeColorBuffer(1),n.drawBuffers([n.NONE]),u===im||u===sm){let o=0,a=1,i=a-o;o+=i*d,a-=i*p,i=a-o,r.setColorBuffer(t.RGBColorImage),n.drawBuffers([n.COLOR_ATTACHMENT0]),t.geometryImage.activate(),t.HSLColorImage.activate(),t.LICImage.activate(),t.enhanceContrastPass||e.initializeResources();const{enhanceContrastPass:s}=t;t._openGLRenderWindow.getShaderCache().readyShaderProgram(s),s.setUniformi("texGeomColors",t.geometryImage.getTextureUnit()),s.setUniformi("texHSLColors",t.HSLColorImage.getTextureUnit()),s.setUniformi("texLIC",t.LICImage.getTextureUnit()),s.setUniformf("uLMin",o),s.setUniformf("uLMaxMinDiff",i),e.renderQuad(t.size,s),t.geometryImage.deactivate(),t.HSLColorImage.deactivate(),t.LICImage.deactivate(),r.removeColorBuffer(0),n.drawBuffers([n.NONE])}r.restorePreviousBindingsAndBuffers()},e.applyLIC=()=>{const e=t.licInterface.get("stepSize","numberOfSteps","enhancedLIC","enhanceContrast","lowLICContrastEnhancementFactor","highLICContrastEnhancementFactor","antiAlias","normalizeVectors","maskThreshold","transformVectors"),n=t.licHelper.executeLIC(t.size,t.vectorImage,t.maskVectorImage,t.noiseTexture,t._openGLRenderWindow,e);if(!n)return console.error("Failed to compute image LIC"),void(t.LICImage=null);t.LICImage=n},e.setSize=n=>{Array.isArray(n)&&2===n.length&&(t.size&&t.size[0]===n[0]&&t.size[1]===n[1]||(t.size=n,e.releaseGraphicsResources()))},e.releaseGraphicsResources=()=>{t.geometryImage&&(t.geometryImage.releaseGraphicsResources(),t.geometryImage=null),t.vectorImage&&(t.vectorImage.releaseGraphicsResources(),t.vectorImage=null),t.maskVectorImage&&(t.maskVectorImage.releaseGraphicsResources(),t.maskVectorImage=null),t.LICImage&&(t.LICImage.releaseGraphicsResources(),t.LICImage=null),t.RGBColorImage&&(t.RGBColorImage.releaseGraphicsResources(),t.RGBColorImage=null),t.HSLColorImage&&(t.HSLColorImage.releaseGraphicsResources(),t.HSLColorImage=null),t.depthTexture&&(t.depthTexture.releaseGraphicsResources(),t.depthTexture=null),t.framebuffer&&(t.framebuffer.releaseGraphicsResources(),t.framebuffer=null)}}const Tm={context:null,shadersNeedBuilding:!0,reallocateTextures:!0,size:null,licInterface:null};function bm(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Tm,n),hm.extend(e,t,n),jt.obj(e,t),jt.setGet(e,t,["context","_openGLRenderWindow","reallocateTextures","licInterface","size"]),jt.moveToProtected(e,t,["openGLRenderWindow"]),ym(e,t)}var xm={newInstance:jt.newInstance(bm,"vtkSurfaceLICInterface"),extend:bm};const{vtkErrorMacro:Cm}=Kt;function Sm(e,t){t.classHierarchy.push("vtkOpenGLSurfaceLICMapper");const n={...e};e.getNeedToRebuildShaders=(e,r,o)=>t.rebuildLICShaders||n.getNeedToRebuildShaders(e,r,o),e.replaceShaderValues=(e,r,o)=>{const a=t.lastBoundBO.getReferenceByName("lastLightComplexity");let i=e.Vertex,s=e.Fragment;const l=t.renderable.getInputArrayToProcess(0);if(l&&t.canDrawLIC){s=gd.substitute(s,"//VTK::Output::Dec",["//VTK::Output::Dec","layout(location = 2) out vec4 vectorTexture;","layout(location = 3) out vec4 maskVectorTexture;"]).result;const n=`${l.getName()}MC`;0===a&&t.lastBoundBO.set({lastLightComplexity:1},!0),i=gd.substitute(i,"//VTK::TCoord::Dec",[`attribute vec3 ${n};`,"out vec3 licOutput;","//VTK::TCoord::Dec"]).result,i=gd.substitute(i,"//VTK::TCoord::Impl",[`licOutput = ${n};`,"//VTK::TCoord::Impl"]).result,s=gd.substitute(s,"//VTK::TCoord::Dec",["uniform int uMaskOnSurface;","uniform mat3 normalMatrix;","in vec3 licOutput;","//VTK::TCoord::Dec"]).result,s=gd.substitute(s,"//VTK::TCoord::Impl",["// projected vectors","  vec3 tcoordLIC = normalMatrix * licOutput;","  vec3 normN = normalize(normalVCVSOutput);","  float k = dot(tcoordLIC, normN);","  vec3 projected = (tcoordLIC - k*normN);","  vectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","// vectors for fragment masking","  if (uMaskOnSurface == 0)","    {","    maskVectorTexture = vec4(licOutput, 1.0);","    }","  else","    {","    maskVectorTexture = vec4(projected.x, projected.y, 0.0 , 1.0);","    }","//VTK::TCoord::Impl"],!1).result,e.Vertex=i}t.rebuildLICShaders=!1,e.Fragment=s,n.replaceShaderValues(e,r,o),a>0&&t.lastBoundBO.set({lastLightComplexity:a},!0)},e.setMapperShaderParameters=(e,r,o)=>{n.setMapperShaderParameters(e,r,o),t.canDrawLIC&&e.getProgram().setUniformi("uMaskOnSurface",t.maskOnSurface)},e.getNeedToRebuildBufferObjects=(e,r)=>t.rebuildLICBuffers||n.getNeedToRebuildBufferObjects(e,r),e.buildBufferObjects=(e,r)=>{if(t.canDrawLIC){const e=t.renderable.getInputArrayToProcess(0);e&&e.getNumberOfComponents()>1&&t.renderable.setCustomShaderAttributes([e.getName()])}t.rebuildLICBuffers=!1,n.buildBufferObjects(e,r)},e.pushState=e=>{t.stateCache={[e.BLEND]:e.isEnabled(e.BLEND),[e.DEPTH_TEST]:e.isEnabled(e.DEPTH_TEST),[e.SCISSOR_TEST]:e.isEnabled(e.SCISSOR_TEST),[e.CULL_FACE]:e.isEnabled(e.CULL_FACE)}},e.popState=e=>{const n=n=>t.stateCache[n]?e.enable(n):e.disable(n);n(e.BLEND),n(e.DEPTH_TEST),n(e.SCISSOR_TEST),n(e.CULL_FACE)},e.renderPiece=(r,o)=>{let a=!0;t._openGLRenderWindow.getWebgl2()||(Cm("SurfaceLICMapper Requires WebGL 2"),a=!1),t.context.getExtension("EXT_color_buffer_float")&&t.context.getExtension("OES_texture_float_linear")||(Cm("SurfaceLICMapper requires the EXT_color_buffer_float and OES_texture_float_linear WebGL2 extensions."),a=!1),t.currentInput=t.renderable.getInputData(),t.currentInput||(Cm("No input"),a=!1);let i=t.renderable.getLicInterface();i||(i=hm.newInstance(),t.renderable.setLicInterface(i)),t.openGLLicInterface||(t.openGLLicInterface=xm.newInstance()),i!==t.openGLLicInterface.getLicInterface()&&t.openGLLicInterface.setLicInterface(i);const s=t.renderable.getInputArrayToProcess(0);if(i.getEnableLIC()&&(!s||s.getNumberOfComponents()<2)&&(Cm("No vector input array"),a=!1),i.getEnableLIC()||(a=!1),t.canDrawLIC!==a&&(t.rebuildLICShaders=!0,t.rebuildLICBuffers=!0),t.canDrawLIC=a,!a||!i.getEnableLIC())return void n.renderPiece(r,o);const l=t.context,c=o.getProperty().getBackfaceCulling(),u=o.getProperty().getFrontfaceCulling();c||u?u?(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.FRONT)):(t._openGLRenderWindow.enableCullFace(),l.cullFace(l.BACK)):t._openGLRenderWindow.disableCullFace();const d=t._openGLRenderWindow.getSize(),p=d.map((e=>Math.round(e*i.getViewPortScale())));t.openGLLicInterface.setSize(p),t.openGLLicInterface.setOpenGLRenderWindow(t._openGLRenderWindow),t.openGLLicInterface.setContext(t.context),e.pushState(t.context),t.openGLLicInterface.initializeResources(),t.openGLLicInterface.prepareForGeometry(),e.popState(t.context),n.renderPieceStart(r,o),n.renderPieceDraw(r,o),n.renderPieceFinish(r,o),e.pushState(t.context),t.VBOBuildTime.modified(),t.openGLLicInterface.completedGeometry(),t.context.disable(t.context.CULL_FACE),t.openGLLicInterface.applyLIC(),t.openGLLicInterface.combineColorsAndLIC(),t.openGLLicInterface.copyToScreen(d),e.popState(t.context)}}const Am={canDrawLIC:!1,rebuildLICShaders:!1,rebuildLICBuffers:!1,openGLLicInterface:null};const Im=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Am,n),sp.extend(e,t,n),Sm(e,t),At(e,t,["openGLLicInterface"])}),"vtkOpenGLSurfaceLICMapper");rn("vtkSurfaceLICMapper",Im);const{vtkErrorMacro:wm}=Kt;function Pm(e,t){t.classHierarchy.push("vtkOpenGLSphereMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkSphereMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Spheres\n\nattribute vec4 vertexMC;\nattribute vec2 offsetMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying vec3 centerVCVSOutput;\n\nuniform int cameraParallel;\nuniform float scaleFactor;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  // compute the projected vertex position\n  vec2 scaledOffsetMC = scaleFactor * offsetMC;\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = length(scaledOffsetMC)*0.5;\n\n  // make the triangle face the camera\n  if (cameraParallel == 0)\n    {\n    vec3 dir = normalize(-vertexVCVSOutput.xyz);\n    vec3 base2 = normalize(cross(dir,vec3(1.0,0.0,0.0)));\n    vec3 base1 = cross(base2,dir);\n    vertexVCVSOutput.xyz = vertexVCVSOutput.xyz + scaledOffsetMC.x*base1 + scaledOffsetMC.y*base2;\n    }\n  else\n    {\n    // add in the offset\n    vertexVCVSOutput.xy = vertexVCVSOutput.xy + scaledOffsetMC;\n    }\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec",["varying vec4 vertexVCVSOutput;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Impl",["vec4 vertexVC = vertexVCVSOutput;\n"]).result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform float invertedDepth;\n","uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float b = 2.0*dot(EyePos,EyeDir);\n","  float c = dot(EyePos,EyePos) - 1.0;\n","  float d = b*b - 4.0*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t = (-b - invertedDepth*sqrt(d))*0.5;\n","    normalVCVSOutput = invertedDepth*normalize(EyePos + t*EyeDir);\n","    vertexVC.xyz = normalVCVSOutput*radiusVCVSOutput + centerVCVSOutput;\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{if(e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"offsetMC",12,e.getCABO().getStride(),t.context.FLOAT,2,!1)||wm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isUniformUsed("invertedDepth")&&e.getProgram().setUniformf("invertedDepth",t.invert?-1:1),e.getProgram().isUniformUsed("scaleFactor")){const n=t.currentInput.getPointData();null!=t.renderable.getScaleArray()&&n.hasArray(t.renderable.getScaleArray())?e.getProgram().setUniformf("scaleFactor",t.renderable.getScaleFactor()):e.getProgram().setUniformf("scaleFactor",1)}n.setMapperShaderParameters(e,r,o)},e.setCameraShaderParameters=(e,n,r)=>{const o=e.getProgram(),a=n.getActiveCamera(),i=t.openGLCamera.getKeyMatrices(n);if(o.isUniformUsed("VCPCMatrix")&&o.setUniformMatrix("VCPCMatrix",i.vcpc),o.isUniformUsed("MCVCMatrix"))if(r.getIsIdentity())o.setUniformMatrix("MCVCMatrix",i.wcvc);else{const e=t.openGLActor.getKeyMatrices(),n=new Float64Array(16);T(n,i.wcvc,e.mcwc),o.setUniformMatrix("MCVCMatrix",n)}o.isUniformUsed("cameraParallel")&&e.getProgram().setUniformi("cameraParallel",a.getParallelProjection())},e.getOpenGLMode=(e,n)=>t.context.TRIANGLES,e.buildBufferObjects=(e,n)=>{const r=t.currentInput;if(null===r)return;t.renderable.mapScalars(r,1);const o=t.renderable.getColorMapColors(),a=t.primitives[t.primTypes.Tris].getCABO(),i=r.getPointData(),s=r.getPoints(),l=s.getNumberOfPoints(),c=s.getData();let u=null;null!=t.renderable.getScaleArray()&&i.hasArray(t.renderable.getScaleArray())&&(u=i.getArray(t.renderable.getScaleArray()).getData());let d=null,p=0,f=null;o?(p=o.getNumberOfComponents(),a.setColorOffset(0),a.setColorBOStride(4),d=o.getData(),f=new Uint8Array(3*l*4),a.getColorBO()||a.setColorBO(Ju.newInstance()),a.getColorBO().setOpenGLRenderWindow(t._openGLRenderWindow)):a.getColorBO()&&a.setColorBO(null),a.setColorComponents(p);const g=new Float32Array(5*l*3);a.setStride(20);const m=Math.cos(Oo(30));let h=0,v=0,y=0,T=0;for(let e=0;e<l;++e){let n=t.renderable.getRadius();u&&(n=u[e]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=-2*n*m,g[y++]=-n,d&&(v=e*p,f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=2*n*m,g[y++]=-n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3]),h=3*e,g[y++]=c[h++],g[y++]=c[h++],g[y++]=c[h++],g[y++]=0,g[y++]=2*n,d&&(f[T++]=d[v],f[T++]=d[v+1],f[T++]=d[v+2],f[T++]=d[v+3])}a.setElementCount(y/5),a.upload(g,$u.ARRAY_BUFFER),o&&a.getColorBO().upload(f,$u.ARRAY_BUFFER),t.VBOBuildTime.modified()}}const Om={};const Rm=Dt((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Om,n),sp.extend(e,t,n),Pm(e,t)}),"vtkOpenGLSphereMapper");rn("vtkSphereMapper",Rm);const{vtkErrorMacro:Mm}=Kt;function Em(e,t){t.classHierarchy.push("vtkOpenGLStickMapper");const n={...e};e.getShaderTemplate=(e,t,n)=>{e.Vertex="//VTK::System::Dec\n\n/*=========================================================================\n\n  Program:   Visualization Toolkit\n  Module:    vtkStickMapperVS.glsl\n\n  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen\n  All rights reserved.\n  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n     This software is distributed WITHOUT ANY WARRANTY; without even\n     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n     PURPOSE.  See the above copyright notice for more information.\n\n=========================================================================*/\n// this shader implements imposters in OpenGL for Sticks\n\nattribute vec4 vertexMC;\nattribute vec3 orientMC;\nattribute vec4 offsetMC;\nattribute float radiusMC;\n\n// optional normal declaration\n//VTK::Normal::Dec\n\n//VTK::Picking::Dec\n\n// Texture coordinates\n//VTK::TCoord::Dec\n\nuniform mat3 normalMatrix; // transform model coordinate directions to view coordinates\n\n// material property values\n//VTK::Color::Dec\n\n// clipping plane vars\n//VTK::Clip::Dec\n\n// camera and actor matrix values\n//VTK::Camera::Dec\n\nvarying vec4 vertexVCVSOutput;\nvarying float radiusVCVSOutput;\nvarying float lengthVCVSOutput;\nvarying vec3 centerVCVSOutput;\nvarying vec3 orientVCVSOutput;\n\nuniform int cameraParallel;\n\nvoid main()\n{\n  //VTK::Picking::Impl\n\n  //VTK::Color::Impl\n\n  //VTK::Normal::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::Clip::Impl\n\n  vertexVCVSOutput = MCVCMatrix * vertexMC;\n  centerVCVSOutput = vertexVCVSOutput.xyz;\n  radiusVCVSOutput = radiusMC;\n  lengthVCVSOutput = length(orientMC);\n  orientVCVSOutput = normalMatrix * normalize(orientMC);\n\n  // make sure it is pointing out of the screen\n  if (orientVCVSOutput.z < 0.0)\n    {\n    orientVCVSOutput = -orientVCVSOutput;\n    }\n\n  // make the basis\n  vec3 xbase;\n  vec3 ybase;\n  vec3 dir = vec3(0.0,0.0,1.0);\n  if (cameraParallel == 0)\n    {\n    dir = normalize(-vertexVCVSOutput.xyz);\n    }\n  if (abs(dot(dir,orientVCVSOutput)) == 1.0)\n    {\n    xbase = normalize(cross(vec3(0.0,1.0,0.0),orientVCVSOutput));\n    ybase = cross(xbase,orientVCVSOutput);\n    }\n  else\n    {\n    xbase = normalize(cross(orientVCVSOutput,dir));\n    ybase = cross(orientVCVSOutput,xbase);\n    }\n\n  vec3 offsets = offsetMC.xyz*2.0-1.0;\n  vertexVCVSOutput.xyz = vertexVCVSOutput.xyz +\n    radiusVCVSOutput*offsets.x*xbase +\n    radiusVCVSOutput*offsets.y*ybase +\n    0.5*lengthVCVSOutput*offsets.z*orientVCVSOutput;\n\n  gl_Position = VCPCMatrix * vertexVCVSOutput;\n}\n",e.Fragment=Wd,e.Geometry=""},e.replaceShaderValues=(e,r,o)=>{let a=e.Vertex,i=e.Fragment;a=gd.substitute(a,"//VTK::Camera::Dec",["uniform mat4 VCPCMatrix;\n","uniform mat4 MCVCMatrix;"]).result,i=gd.substitute(i,"//VTK::PositionVC::Dec","varying vec4 vertexVCVSOutput;").result,i=gd.substitute(i,"//VTK::PositionVC::Impl","  vec4 vertexVC = vertexVCVSOutput;\n").result,i=gd.substitute(i,"//VTK::Normal::Dec",["uniform int cameraParallel;\n","varying float radiusVCVSOutput;\n","varying vec3 orientVCVSOutput;\n","varying float lengthVCVSOutput;\n","varying vec3 centerVCVSOutput;\n","uniform mat4 VCPCMatrix;\n"]).result;let s="";t.context.getExtension("EXT_frag_depth")&&(s="  gl_FragDepthEXT = (pos.z / pos.w + 1.0) / 2.0;\n"),t._openGLRenderWindow.getWebgl2()&&(s="gl_FragDepth = (pos.z / pos.w + 1.0) / 2.0;\n"),i=gd.substitute(i,"//VTK::Depth::Impl",["  vec3 EyePos;\n","  vec3 EyeDir;\n","  if (cameraParallel != 0) {\n","    EyePos = vec3(vertexVC.x, vertexVC.y, vertexVC.z + 3.0*radiusVCVSOutput);\n","    EyeDir = vec3(0.0,0.0,-1.0); }\n","  else {\n","    EyeDir = vertexVC.xyz;\n","    EyePos = vec3(0.0,0.0,0.0);\n","    float lengthED = length(EyeDir);\n","    EyeDir = normalize(EyeDir);\n","    if (lengthED > radiusVCVSOutput*3.0) {\n","      EyePos = vertexVC.xyz - EyeDir*3.0*radiusVCVSOutput; }\n","    }\n","  EyePos = EyePos - centerVCVSOutput;\n","  vec3 base1;\n","  if (abs(orientVCVSOutput.z) < 0.99) {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,0.0,1.0))); }\n","  else {\n","    base1 = normalize(cross(orientVCVSOutput,vec3(0.0,1.0,0.0))); }\n","  vec3 base2 = cross(orientVCVSOutput,base1);\n","  EyePos = vec3(dot(EyePos,base1),dot(EyePos,base2),dot(EyePos,orientVCVSOutput));\n","  EyeDir = vec3(dot(EyeDir,base1),dot(EyeDir,base2),dot(EyeDir,orientVCVSOutput));\n","  EyePos = EyePos/radiusVCVSOutput;\n","  float a = EyeDir.x*EyeDir.x + EyeDir.y*EyeDir.y;\n","  float b = 2.0*(EyePos.x*EyeDir.x + EyePos.y*EyeDir.y);\n","  float c = EyePos.x*EyePos.x + EyePos.y*EyePos.y - 1.0;\n","  float d = b*b - 4.0*a*c;\n","  vec3 normalVCVSOutput = vec3(0.0,0.0,1.0);\n","  if (d < 0.0) { discard; }\n","  else {\n","    float t =  (-b - sqrt(d))/(2.0*a);\n","    float tz = EyePos.z + t*EyeDir.z;\n","    vec3 iPoint = EyePos + t*EyeDir;\n","    if (abs(iPoint.z)*radiusVCVSOutput > lengthVCVSOutput*0.5) {\n","      float t2 = (-b + sqrt(d))/(2.0*a);\n","      float tz2 = EyePos.z + t2*EyeDir.z;\n","      if (tz2*radiusVCVSOutput > lengthVCVSOutput*0.5 || tz*radiusVCVSOutput < -0.5*lengthVCVSOutput) { discard; }\n","      else {\n","        normalVCVSOutput = orientVCVSOutput;\n","        float t3 = (lengthVCVSOutput*0.5/radiusVCVSOutput - EyePos.z)/EyeDir.z;\n","        iPoint = EyePos + t3*EyeDir;\n","        vertexVC.xyz = radiusVCVSOutput*(iPoint.x*base1 + iPoint.y*base2 + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","        }\n","      }\n","    else {\n","      normalVCVSOutput = iPoint.x*base1 + iPoint.y*base2;\n","      vertexVC.xyz = radiusVCVSOutput*(normalVCVSOutput + iPoint.z*orientVCVSOutput) + centerVCVSOutput;\n","      }\n","    }\n","  vec4 pos = VCPCMatrix * vertexVC;\n",s]).result,i=gd.substitute(i,"//VTK::Normal::Impl","").result,t.haveSeenDepthRequest&&(i=gd.substitute(i,"//VTK::ZBuffer::Impl",["if (depthRequest == 1) {","float computedZ = (pos.z / pos.w + 1.0) / 2.0;","float iz = floor(computedZ * 65535.0 + 0.1);","float rf = floor(iz/256.0)/255.0;","float gf = mod(iz,256.0)/255.0;","gl_FragData[0] = vec4(rf, gf, 0.0, 1.0); }"]).result),e.Vertex=a,e.Fragment=i,n.replaceShaderValues(e,r,o)},e.setMapperShaderParameters=(e,r,o)=>{e.getCABO().getElementCount()&&(t.VBOBuildTime>e.getAttributeUpdateTime().getMTime()||e.getShaderSourceTime().getMTime()>e.getAttributeUpdateTime().getMTime())&&(e.getProgram().isAttributeUsed("orientMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"orientMC",12,e.getCABO().getStride(),t.context.FLOAT,3,!1)||Mm("Error setting 'orientMC' in shader VAO.")),e.getProgram().isAttributeUsed("offsetMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO().getColorBO(),"offsetMC",0,e.getCABO().getColorBOStride(),t.context.UNSIGNED_BYTE,3,!0)||Mm("Error setting 'offsetMC' in shader VAO.")),e.getProgram().isAttributeUsed("radiusMC")&&(e.getVAO().addAttributeArray(e.getProgram(),e.getCABO(),"radiusMC",24,e.getCABO().getStride(),t.context.FLOAT,1,!1)||Mm("Error setting 'radiusMC' in shader 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Image;n.crossOrigin&&(o.crossOrigin=n.crossOrigin),o.onload=()=>e(o),o.onerror=r,o.src=t}))}};zm("http",(e=>kh));const{vtkDebugMacro:Gh}=jt;function Uh(e,t){t.classHierarchy.push("vtkCamera");const n=new Float64Array(3),r=new Float64Array([0,0,-1]),o=new Float64Array([0,1,0]),a=g(new Float64Array(16)),i=g(new Float64Array(16)),s=new Float64Array(3),l=new Float64Array(3),c=new Float64Array(3),u=g(new Float64Array(16)),p=g(new Float64Array(16)),f=new Float64Array(3),v=new Float64Array(3);function y(){t.viewPlaneNormal[0]=-t.directionOfProjection[0],t.viewPlaneNormal[1]=-t.directionOfProjection[1],t.viewPlaneNormal[2]=-t.directionOfProjection[2]}e.orthogonalizeViewUp=()=>{const n=e.getViewMatrix();t.viewUp[0]=n[4],t.viewUp[1]=n[5],t.viewUp[2]=n[6],e.modified()},e.setPosition=(n,r,o)=>{n===t.position[0]&&r===t.position[1]&&o===t.position[2]||(t.position[0]=n,t.position[1]=r,t.position[2]=o,e.computeDistance(),e.modified())},e.setFocalPoint=(n,r,o)=>{n===t.focalPoint[0]&&r===t.focalPoint[1]&&o===t.focalPoint[2]||(t.focalPoint[0]=n,t.focalPoint[1]=r,t.focalPoint[2]=o,e.computeDistance(),e.modified())},e.setDistance=n=>{if(t.distance===n)return;t.distance=n,t.distance<1e-20&&(t.distance=1e-20,Gh("Distance is set to minimum."));const r=t.directionOfProjection;t.focalPoint[0]=t.position[0]+r[0]*t.distance,t.focalPoint[1]=t.position[1]+r[1]*t.distance,t.focalPoint[2]=t.position[2]+r[2]*t.distance,e.modified()},e.computeDistance=()=>{const e=t.focalPoint[0]-t.position[0],n=t.focalPoint[1]-t.position[1],r=t.focalPoint[2]-t.position[2];if(t.distance=Math.sqrt(e*e+n*n+r*r),t.distance<1e-20){t.distance=1e-20,Gh("Distance is set to minimum.");const e=t.directionOfProjection;t.focalPoint[0]=t.position[0]+e[0]*t.distance,t.focalPoint[1]=t.position[1]+e[1]*t.distance,t.focalPoint[2]=t.position[2]+e[2]*t.distance}t.directionOfProjection[0]=e/t.distance,t.directionOfProjection[1]=n/t.distance,t.directionOfProjection[2]=r/t.distance,y()},e.dolly=n=>{if(n<=0)return;const r=t.distance/n;e.setPosition(t.focalPoint[0]-r*t.directionOfProjection[0],t.focalPoint[1]-r*t.directionOfProjection[1],t.focalPoint[2]-r*t.directionOfProjection[2])},e.roll=n=>{const r=t.position,o=t.focalPoint,a=t.viewUp,i=new Float64Array([a[0],a[1],a[2],0]);g(u);const s=new Float64Array([o[0]-r[0],o[1]-r[1],o[2]-r[2]]);C(u,u,Oo(n),s),Gi(i,i,u),t.viewUp[0]=i[0],t.viewUp[1]=i[1],t.viewUp[2]=i[2],e.modified()},e.azimuth=n=>{const r=t.focalPoint;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.yaw=n=>{const r=t.position;g(p),b(p,p,r),C(p,p,Oo(n),t.viewUp),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(v[0],v[1],v[2])},e.elevation=n=>{const r=t.focalPoint,o=e.getViewMatrix(),a=[-o[0],-o[1],-o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(f,t.position,p),e.setPosition(f[0],f[1],f[2])},e.pitch=n=>{const r=t.position,o=e.getViewMatrix(),a=[o[0],o[1],o[2]];g(p),b(p,p,r),C(p,p,Oo(n),a),b(p,p,[-r[0],-r[1],-r[2]]),Vn(v,t.focalPoint,p),e.setFocalPoint(...v)},e.zoom=n=>{n<=0||(t.parallelProjection?t.parallelScale/=n:t.viewAngle/=n,e.modified())},e.translate=(n,r,o)=>{const a=[n,r,o];Go(t.position,a,t.position),Go(t.focalPoint,a,t.focalPoint),e.computeDistance(),e.modified()},e.applyTransform=n=>{const r=[...t.viewUp,1],o=[],a=[],i=[];r[0]+=t.position[0],r[1]+=t.position[1],r[2]+=t.position[2],Gi(o,[...t.position,1],n),Gi(a,[...t.focalPoint,1],n),Gi(i,r,n),i[0]-=o[0],i[1]-=o[1],i[2]-=o[2],e.setPosition(...o.slice(0,3)),e.setFocalPoint(...a.slice(0,3)),e.setViewUp(...i.slice(0,3))},e.getThickness=()=>t.clippingRange[1]-t.clippingRange[0],e.setThickness=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.clippingRange[0],t.clippingRange[0]+r)},e.setThicknessFromFocalPoint=n=>{let r=n;r<1e-20&&(r=1e-20,Gh("Thickness is set to minimum.")),e.setClippingRange(t.distance-r/2,t.distance+r/2)},e.setRoll=e=>{},e.getRoll=()=>{},e.setObliqueAngles=(e,t)=>{},e.getOrientation=()=>{},e.getOrientationWXYZ=()=>{},e.getFrustumPlanes=e=>{},e.getCameraLightTransformMatrix=e=>(d(e,t.cameraLightTransform),e),e.computeCameraLightTransform=()=>{d(a,e.getViewMatrix()),h(a,a),P(i,[t.distance,t.distance,t.distance]),T(a,a,i),g(t.cameraLightTransform),b(t.cameraLightTransform,a,[0,0,-1])},e.deepCopy=e=>{},e.physicalOrientationToWorldDirection=e=>{const t=Hi(e[0],e[1],e[2],e[3]),n=Ui(),r=Hi(0,0,1,0);var o,a;return a=t,(o=n)[0]=-a[0],o[1]=-a[1],o[2]=-a[2],o[3]=a[3],Wi(r,t,r),Wi(r,r,n),[r[0],r[1],r[2]]},e.getPhysicalToWorldMatrix=t=>{e.getWorldToPhysicalMatrix(t),h(t,t)},e.getWorldToPhysicalMatrix=e=>{g(e);const n=[3];Ko(t.physicalViewNorth,t.physicalViewUp,n),e[0]=n[0],e[1]=n[1],e[2]=n[2],e[4]=t.physicalViewUp[0],e[5]=t.physicalViewUp[1],e[6]=t.physicalViewUp[2],e[8]=-t.physicalViewNorth[0],e[9]=-t.physicalViewNorth[1],e[10]=-t.physicalViewNorth[2],m(e,e),In(s,1/t.physicalScale,1/t.physicalScale,1/t.physicalScale),x(e,e,s),b(e,e,t.physicalTranslation)},e.computeViewParametersFromViewMatrix=i=>{h(a,i),Vn(s,n,a),e.computeDistance();const u=t.distance;e.setPosition(s[0],s[1],s[2]),Vn(l,r,a),Pn(l,l,s),Mn(l,l),e.setDirectionOfProjection(l[0],l[1],l[2]),Vn(c,o,a),Pn(c,c,s),Mn(c,c),e.setViewUp(c[0],c[1],c[2]),e.setDistance(u)},e.computeViewParametersFromPhysicalMatrix=t=>{e.getWorldToPhysicalMatrix(a),T(a,t,a),e.computeViewParametersFromViewMatrix(a)},e.setViewMatrix=n=>{t.viewMatrix=n,t.viewMatrix&&(d(a,t.viewMatrix),e.computeViewParametersFromViewMatrix(a),m(t.viewMatrix,t.viewMatrix))},e.getViewMatrix=()=>{if(t.viewMatrix)return t.viewMatrix;q(a,t.position,t.focalPoint,t.viewUp),m(a,a);const e=new Float64Array(16);return d(e,a),e},e.setProjectionMatrix=e=>{t.projectionMatrix=e},e.getProjectionMatrix=(e,n,r)=>{const o=new Float64Array(16);if(g(o),t.projectionMatrix){const e=1/t.physicalScale;return In(s,e,e,e),d(o,t.projectionMatrix),x(o,o,s),m(o,o),o}g(a);const i=t.clippingRange[1]-t.clippingRange[0],l=[t.clippingRange[0]+(n+1)*i/2,t.clippingRange[0]+(r+1)*i/2];if(t.parallelProjection){const n=t.parallelScale*e,r=t.parallelScale,o=(t.windowCenter[0]-1)*n,i=(t.windowCenter[0]+1)*n,s=(t.windowCenter[1]-1)*r,c=(t.windowCenter[1]+1)*r;K(a,o,i,s,c,l[0],l[1]),m(a,a)}else{if(t.useOffAxisProjection)throw new Error("Off-Axis projection is not supported at this time");{const n=Math.tan(Oo(t.viewAngle)/2);let r,o;!0===t.useHorizontalViewAngle?(r=t.clippingRange[0]*n,o=t.clippingRange[0]*n/e):(r=t.clippingRange[0]*n*e,o=t.clippingRange[0]*n);const i=(t.windowCenter[0]-1)*r,s=(t.windowCenter[0]+1)*r,c=(t.windowCenter[1]-1)*o,u=(t.windowCenter[1]+1)*o,d=l[0],p=l[1];a[0]=2*d/(s-i),a[5]=2*d/(u-c),a[2]=(i+s)/(s-i),a[6]=(c+u)/(u-c),a[10]=-(d+p)/(p-d),a[14]=-1,a[11]=-2*d*p/(p-d),a[15]=0}}return d(o,a),o},e.getCompositeProjectionMatrix=(t,n,r)=>{const o=e.getViewMatrix(),a=e.getProjectionMatrix(t,n,r);return T(a,o,a),a},e.setDirectionOfProjection=(e,n,r)=>{if(t.directionOfProjection[0]===e&&t.directionOfProjection[1]===n&&t.directionOfProjection[2]===r)return;t.directionOfProjection[0]=e,t.directionOfProjection[1]=n,t.directionOfProjection[2]=r;const o=t.directionOfProjection;t.focalPoint[0]=t.position[0]+o[0]*t.distance,t.focalPoint[1]=t.position[1]+o[1]*t.distance,t.focalPoint[2]=t.position[2]+o[2]*t.distance,y()},e.setDeviceAngles=(n,r,o,a)=>{const i=[3];Ko(t.physicalViewNorth,t.physicalViewUp,i);const s=g(new Float64Array(16));C(s,s,Oo(n),t.physicalViewUp),C(s,s,Oo(r),i),C(s,s,Oo(o),t.physicalViewNorth),C(s,s,Oo(-a),t.physicalViewUp);const l=new Float64Array([-t.physicalViewUp[0],-t.physicalViewUp[1],-t.physicalViewUp[2]]),c=new Float64Array(t.physicalViewNorth);Vn(l,l,s),Vn(c,c,s),e.setDirectionOfProjection(l[0],l[1],l[2]),e.setViewUp(c[0],c[1],c[2]),e.modified()},e.setOrientationWXYZ=(t,n,r,o)=>{const a=g(new Float64Array(16));if(0!==t&&(0!==n||0!==r||0!==o)){const e=Oo(t),i=Ui();zi(i,[n,r,o],e),k(a,i)}const i=new Float64Array(3);Vn(i,[0,0,-1],a);const s=new Float64Array(3);Vn(s,[0,1,0],a),e.setDirectionOfProjection(...i),e.setViewUp(...s),e.modified()},e.computeClippingRange=e=>{let n=null,r=null;n=t.viewPlaneNormal,r=t.position;const o=-n[0],a=-n[1],i=-n[2],s=-(o*r[0]+a*r[1]+i*r[2]),l=[o*e[0]+a*e[2]+i*e[4]+s,1e-18];for(let t=0;t<2;t++)for(let n=0;n<2;n++)for(let r=0;r<2;r++){const c=o*e[r]+a*e[2+n]+i*e[4+t]+s;l[0]=c<l[0]?c:l[0],l[1]=c>l[1]?c:l[1]}return l}}const zh={position:[0,0,1],focalPoint:[0,0,0],viewUp:[0,1,0],directionOfProjection:[0,0,-1],parallelProjection:!1,useHorizontalViewAngle:!1,viewAngle:30,parallelScale:1,clippingRange:[.01,1000.01],windowCenter:[0,0],viewPlaneNormal:[0,0,1],useOffAxisProjection:!1,screenBottomLeft:[-.5,-.5,-.5],screenBottomRight:[.5,-.5,-.5],screenTopRight:[.5,.5,-.5],freezeFocalPoint:!1,projectionMatrix:null,viewMatrix:null,cameraLightTransform:c(),physicalTranslation:[0,0,0],physicalScale:1,physicalViewUp:[0,1,0],physicalViewNorth:[0,0,-1]};function Wh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,zh,n),jt.obj(e,t),jt.get(e,t,["distance"]),jt.setGet(e,t,["parallelProjection","useHorizontalViewAngle","viewAngle","parallelScale","useOffAxisProjection","freezeFocalPoint","physicalScale"]),jt.getArray(e,t,["directionOfProjection","viewPlaneNormal","position","focalPoint"]),jt.setGetArray(e,t,["clippingRange","windowCenter"],2),jt.setGetArray(e,t,["viewUp","screenBottomLeft","screenBottomRight","screenTopRight","physicalTranslation","physicalViewUp","physicalViewNorth"],3),Uh(e,t)}var Hh={newInstance:jt.newInstance(Wh,"vtkCamera"),extend:Wh};function jh(e,t){t.classHierarchy.push("vtkLight");const n=new Float64Array(3);e.getTransformedPosition=()=>(t.transformMatrix?Vn(n,t.position,t.transformMatrix):In(n,t.position[0],t.position[1],t.position[2]),n),e.getTransformedFocalPoint=()=>(t.transformMatrix?Vn(n,t.focalPoint,t.transformMatrix):In(n,t.focalPoint[0],t.focalPoint[1],t.focalPoint[2]),n),e.getDirection=()=>(t.directionMTime<t.mtime&&(_n(t.direction,t.focalPoint,t.position),qo(t.direction),t.directionMTime=t.mtime),t.direction),e.setDirection=e=>{const n=new Float64Array(3);_n(n,t.position,e),t.focalPoint=n},e.setDirectionAngle=(t,n)=>{const r=Oo(t),o=Oo(n);e.setPosition(Math.cos(r)*Math.sin(o),Math.sin(r),Math.cos(r)*Math.cos(o)),e.setFocalPoint(0,0,0),e.setPositional(0)},e.setLightTypeToHeadLight=()=>{e.setLightType("HeadLight")},e.setLightTypeToCameraLight=()=>{e.setLightType("CameraLight")},e.setLightTypeToSceneLight=()=>{e.setTransformMatrix(null),e.setLightType("SceneLight")},e.lightTypeIsHeadLight=()=>"HeadLight"===t.lightType,e.lightTypeIsSceneLight=()=>"SceneLight"===t.lightType,e.lightTypeIsCameraLight=()=>"CameraLight"===t.lightType}const Kh={switch:!0,intensity:1,color:[1,1,1],position:[0,0,1],focalPoint:[0,0,0],positional:!1,exponent:1,coneAngle:30,coneFalloff:5,attenuationValues:[1,0,0],transformMatrix:null,lightType:"SceneLight",shadowAttenuation:1,direction:[0,0,0],directionMTime:0};function $h(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Kh,n),jt.obj(e,t),jt.setGet(e,t,["intensity","switch","positional","exponent","coneAngle","coneFalloff","transformMatrix","lightType","shadowAttenuation","attenuationValues"]),jt.setGetArray(e,t,["color","position","focalPoint","attenuationValues"],3),jh(e,t)}var qh={newInstance:jt.newInstance($h,"vtkLight"),extend:$h,LIGHT_TYPES:["HeadLight","CameraLight","SceneLight"]};const{vtkErrorMacro:Xh}=jt;function Yh(e,t){function n(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:[];t.push(e);const r=e.getNestedProps();if(r&&r.length)for(let e=0;e<r.length;e++)n(r[e],t);return t}t.classHierarchy.push("vtkViewport"),e.getViewProps=()=>t.props,e.hasViewProp=e=>t.props.includes(e),e.addViewProp=n=>{n&&!e.hasViewProp(n)&&t.props.push(n)},e.removeViewProp=e=>{const n=t.props.filter((t=>t!==e));t.props.length!==n.length&&(t.props=n)},e.removeAllViewProps=()=>{t.props=[]},e.getViewPropsWithNestedProps=()=>{const e=[];for(let r=0;r<t.props.length;r++)n(t.props[r],e);return e},e.addActor2D=e.addViewProp,e.removeActor2D=t=>{e.removeViewProp(t)},e.getActors2D=()=>(t.actors2D=[],t.props.forEach((e=>{t.actors2D=t.actors2D.concat(e.getActors2D())})),t.actors2D),e.displayToView=()=>Xh("call displayToView on your view instead"),e.viewToDisplay=()=>Xh("callviewtodisplay on your view instead"),e.getSize=()=>Xh("call getSize on your View instead"),e.normalizedDisplayToProjection=(t,n,r)=>{const o=e.normalizedDisplayToNormalizedViewport(t,n,r);return e.normalizedViewportToProjection(o[0],o[1],o[2])},e.normalizedDisplayToNormalizedViewport=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[(e-t.viewport[0])/o[0],(n-t.viewport[1])/o[1],r]},e.normalizedViewportToProjection=(e,t,n)=>[2*e-1,2*t-1,2*n-1],e.projectionToNormalizedDisplay=(t,n,r)=>{const o=e.projectionToNormalizedViewport(t,n,r);return e.normalizedViewportToNormalizedDisplay(o[0],o[1],o[2])},e.normalizedViewportToNormalizedDisplay=(e,n,r)=>{const o=[t.viewport[2]-t.viewport[0],t.viewport[3]-t.viewport[1]];return[e*o[0]+t.viewport[0],n*o[1]+t.viewport[1],r]},e.projectionToNormalizedViewport=(e,t,n)=>[.5*(e+1),.5*(t+1),.5*(n+1)],e.PickPropFrom=("PickPropFrom",()=>Xh("vtkViewport::PickPropFrom - NOT IMPLEMENTED"))}const Zh={background:[0,0,0],background2:[.2,.2,.2],gradientBackground:!1,viewport:[0,0,1,1],aspect:[1,1],pixelAspect:[1,1],props:[],actors2D:[]};function Qh(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zh,n),jt.obj(e,t),jt.event(e,t,"event"),jt.setGetArray(e,t,["viewport"],4),jt.setGetArray(e,t,["background","background2"],3),Yh(e,t)}var Jh={newInstance:jt.newInstance(Qh,"vtkViewport"),extend:Qh};const{vtkDebugMacro:ev,vtkErrorMacro:tv,vtkWarningMacro:nv}=Kt;function rv(e){return()=>tv(`vtkRenderer::${e} - NOT IMPLEMENTED`)}function ov(e,t){t.classHierarchy.push("vtkRenderer");const n={type:"ComputeVisiblePropBoundsEvent",renderer:e},r={type:"ResetCameraClippingRangeEvent",renderer:e},o={type:"ResetCameraEvent",renderer:e};e.updateCamera=()=>(t.activeCamera||(ev("No cameras are on, creating one."),e.getActiveCameraAndResetIfCreated()),t.activeCamera.render(e),!0),e.updateLightsGeometryToFollowCamera=()=>{const n=e.getActiveCameraAndResetIfCreated();t.lights.forEach((e=>{e.lightTypeIsSceneLight()||(e.lightTypeIsHeadLight()?(e.setPositionFrom(n.getPositionByReference()),e.setFocalPointFrom(n.getFocalPointByReference()),e.modified(n.getMTime())):e.lightTypeIsCameraLight()?e.setTransformMatrix(n.getCameraLightTransformMatrix(c())):tv("light has unknown light type",e.get()))}))},e.updateLightGeometry=()=>!t.lightFollowCamera||e.updateLightsGeometryToFollowCamera(),e.allocateTime=rv("allocateTime"),e.updateGeometry=rv("updateGeometry"),e.getVTKWindow=()=>t._renderWindow,e.setLayer=n=>{ev(e.getClassName(),e,"setting Layer to ",n),t.layer!==n&&(t.layer=n,e.modified()),e.setPreserveColorBuffer(!!n)},e.setActiveCamera=n=>t.activeCamera!==n&&(t.activeCamera=n,e.modified(),e.invokeEvent({type:"ActiveCameraEvent",camera:n}),!0),e.makeCamera=()=>{const t=Hh.newInstance();return e.invokeEvent({type:"CreateCameraEvent",camera:t}),t},e.getActiveCamera=()=>(t.activeCamera||(t.activeCamera=e.makeCamera()),t.activeCamera),e.getActiveCameraAndResetIfCreated=()=>(t.activeCamera||(e.getActiveCamera(),e.resetCamera()),t.activeCamera),e.getActors=()=>(t.actors=[],t.props.forEach((e=>{t.actors=t.actors.concat(e.getActors())})),t.actors),e.addActor=e.addViewProp,e.removeActor=n=>{t.actors=t.actors.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllActors=()=>{e.getActors().forEach((t=>{e.removeViewProp(t)})),t.actors=[],e.modified()},e.getVolumes=()=>(t.volumes=[],t.props.forEach((e=>{t.volumes=t.volumes.concat(e.getVolumes())})),t.volumes),e.addVolume=e.addViewProp,e.removeVolume=n=>{t.volumes=t.volumes.filter((e=>e!==n)),e.removeViewProp(n),e.modified()},e.removeAllVolumes=()=>{e.getVolumes().forEach((t=>{e.removeViewProp(t)})),t.volumes=[],e.modified()},e.hasLight=e=>t.lights.includes(e),e.addLight=n=>{n&&!e.hasLight(n)&&(t.lights.push(n),e.modified())},e.removeLight=n=>{t.lights=t.lights.filter((e=>e!==n)),e.modified()},e.removeAllLights=()=>{t.lights=[],e.modified()},e.setLightCollection=n=>{t.lights=n,e.modified()},e.makeLight=qh.newInstance,e.createLight=()=>{t.automaticLightCreation&&(t._createdLight&&(e.removeLight(t._createdLight),t._createdLight.delete(),t._createdLight=null),t._createdLight=e.makeLight(),e.addLight(t._createdLight),t._createdLight.setLightTypeToHeadLight(),t._createdLight.setPosition(e.getActiveCamera().getPosition()),t._createdLight.setFocalPoint(e.getActiveCamera().getFocalPoint()))},e.normalizedDisplayToWorld=(t,n,r,o)=>{let 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Math.abs(jo(g,i))>.999&&(nv("Resetting view-up since view plane normal is parallel"),t.activeCamera.setViewUp(-g[2],g[0],g[1])),t.activeCamera.setFocalPoint(a[0],a[1],a[2]),t.activeCamera.setPosition(a[0]+f*i[0],a[1]+f*i[1],a[2]+f*i[2]),e.resetCameraClippingRange(r),t.activeCamera.setParallelScale(p),t.activeCamera.setPhysicalScale(u),t.activeCamera.setPhysicalTranslation(-a[0],-a[1],-a[2]),e.invokeEvent(o),!0},e.resetCameraClippingRange=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;const o=n||e.computeVisiblePropBounds();if(!Ra(o))return ev("Cannot reset camera clipping range!"),!1;if(e.getActiveCameraAndResetIfCreated(),!t.activeCamera)return tv("Trying to reset clipping range of non-existent camera"),!1;const a=t.activeCamera.computeClippingRange(o);let i=0;if(t.activeCamera.getParallelProjection())i=.2*t.activeCamera.getParallelScale();else{const e=Oo(t.activeCamera.getViewAngle());i=.2*Math.tan(e/2)*a[1]}return 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av={pickedProp:null,activeCamera:null,allBounds:[],ambient:[1,1,1],allocatedRenderTime:100,timeFactor:1,automaticLightCreation:!0,twoSidedLighting:!0,lastRenderTimeInSeconds:-1,renderWindow:null,lights:[],actors:[],volumes:[],lightFollowCamera:!0,numberOfPropsRendered:0,propArray:null,pathArray:null,layer:0,preserveColorBuffer:!1,preserveDepthBuffer:!1,computeVisiblePropBounds:Fa(),interactive:!0,nearClippingPlaneTolerance:0,clippingRangeExpansion:.05,erase:!0,draw:!0,useShadows:!1,useDepthPeeling:!1,occlusionRatio:0,maximumNumberOfPeels:4,selector:null,delegate:null,texturedBackground:!1,backgroundTexture:null,environmentTexture:null,environmentTextureDiffuseStrength:1,environmentTextureSpecularStrength:1,useEnvironmentTextureAsBackground:!1,pass:0};function iv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};for(Object.assign(t,av,n),Jh.extend(e,t,n),t.background||(t.background=[0,0,0,1]);t.background.length<3;)t.background.push(0);3===t.background.length&&t.background.push(1),bt(e,t,["_renderWindow","allocatedRenderTime","timeFactor","lastRenderTimeInSeconds","numberOfPropsRendered","lastRenderingUsedDepthPeeling","selector"]),At(e,t,["twoSidedLighting","lightFollowCamera","automaticLightCreation","erase","draw","nearClippingPlaneTolerance","clippingRangeExpansion","backingStore","interactive","layer","preserveColorBuffer","preserveDepthBuffer","useDepthPeeling","occlusionRatio","maximumNumberOfPeels","delegate","backgroundTexture","texturedBackground","environmentTexture","environmentTextureDiffuseStrength","environmentTextureSpecularStrength","useEnvironmentTextureAsBackground","useShadows","pass"]),It(e,t,["actors","volumes","lights"]),Pt(e,t,["background"],4,1),Ot(0,t,["renderWindow"]),ov(e,t)}var sv={newInstance:Dt(iv,"vtkRenderer"),extend:iv};const lv=Object.create(null);function cv(e,t){lv[e]=t}function uv(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return lv[e]&&lv[e](t)}function dv(e,t){t.classHierarchy.push("vtkRenderWindow"),e.addRenderer=n=>{e.hasRenderer(n)||(n.setRenderWindow(e),t.renderers.push(n),e.modified())},e.removeRenderer=n=>{t.renderers=t.renderers.filter((e=>e!==n)),e.modified()},e.hasRenderer=e=>-1!==t.renderers.indexOf(e),e.newAPISpecificView=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return uv(e||t.defaultViewAPI,n)},e.addView=n=>{e.hasView(n)||(n.setRenderable(e),t._views.push(n),e.modified())},e.removeView=n=>{t._views=t._views.filter((e=>e!==n)),e.modified()},e.hasView=e=>-1!==t._views.indexOf(e),e.preRender=()=>{t.renderers.forEach((e=>{e.isActiveCameraCreated()||e.resetCamera()}))},e.render=()=>{e.preRender(),t.interactor?t.interactor.render():t._views.forEach((e=>e.traverseAllPasses()))},e.getStatistics=()=>{const e={propCount:0,invisiblePropCount:0,gpuMemoryMB:0};return t._views.forEach((t=>{t.getGraphicsMemoryInfo&&(e.gpuMemoryMB+=t.getGraphicsMemoryInfo()/1e6)})),t.renderers.forEach((n=>{const r=n.getViewProps(),o=t._views[0].getViewNodeFor(n);r.forEach((t=>{if(t.getVisibility()){e.propCount+=1;const n=t.getMapper&&t.getMapper();if(n&&n.getPrimitiveCount){const t=o.getViewNodeFor(n);if(t){t.getAllocatedGPUMemoryInBytes&&(e.gpuMemoryMB+=t.getAllocatedGPUMemoryInBytes()/1e6);const r=n.getPrimitiveCount();Object.keys(r).forEach((t=>{e[t]||(e[t]=0),e[t]+=r[t]}))}}}else e.invisiblePropCount+=1}))})),e.str=Object.keys(e).map((t=>`${t}: ${e[t]}`)).join("\n"),e},e.captureImages=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return jt.setImmediate(e.render),t._views.map((e=>e.captureNextImage?e.captureNextImage(n,r):void 0)).filter((e=>!!e))},e.addRenderWindow=n=>!t.childRenderWindows.includes(n)&&(t.childRenderWindows.push(n),e.modified(),!0),e.removeRenderWindow=n=>{const r=t.childRenderWindows.findIndex((e=>e===n));return!(r<0||(t.childRenderWindows.splice(r,1),e.modified(),0))}}const pv={defaultViewAPI:"WebGL",renderers:[],views:[],interactor:null,neverRendered:!0,numberOfLayers:1,childRenderWindows:[]};function fv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,pv,n),jt.obj(e,t),jt.setGet(e,t,["interactor","numberOfLayers","_views","defaultViewAPI"]),jt.get(e,t,["neverRendered"]),jt.getArray(e,t,["renderers","childRenderWindows"]),jt.moveToProtected(e,t,["views"]),jt.event(e,t,"completion"),dv(e,t)}var gv={newInstance:jt.newInstance(fv,"vtkRenderWindow"),extend:fv,registerViewConstructor:cv,listViewAPIs:function(){return Object.keys(lv)},newAPISpecificView:uv};const mv={Unknown:0,LeftController:1,RightController:2},hv={Unknown:0,Trigger:1,TrackPad:2,Grip:3,Thumbstick:4,A:5,B:6,ApplicationMenu:7};var vv={Device:mv,Input:hv,Axis:{Unknown:0,TouchpadX:1,TouchpadY:2,ThumbstickX:3,ThumbstickY:4}};const{Device:yv,Input:Tv}=vv,{vtkWarningMacro:bv,vtkErrorMacro:xv,normalizeWheel:Cv,vtkOnceErrorMacro:Sv}=jt,Av={ctrlKey:!1,altKey:!1,shiftKey:!1},Iv={"xr-standard":[Tv.Trigger,Tv.Grip,Tv.TrackPad,Tv.Thumbstick,Tv.A,Tv.B]},wv=["StartAnimation","Animation","EndAnimation","PointerEnter","PointerLeave","MouseEnter","MouseLeave","StartMouseMove","MouseMove","EndMouseMove","LeftButtonPress","LeftButtonRelease","MiddleButtonPress","MiddleButtonRelease","RightButtonPress","RightButtonRelease","KeyPress","KeyDown","KeyUp","StartMouseWheel","MouseWheel","EndMouseWheel","StartPinch","Pinch","EndPinch","StartPan","Pan","EndPan","StartRotate","Rotate","EndRotate","Button3D","Move3D","StartPointerLock","EndPointerLock","StartInteraction","Interaction","EndInteraction","AnimationFrameRateUpdate"];function Pv(e){e.cancelable&&e.preventDefault()}function Ov(e){const t=Object.create(null);return e.forEach((e=>{let{pointerId:n,position:r}=e;t[n]=r})),t}function Rv(e,t){t.classHierarchy.push("vtkRenderWindowInteractor");const n={...e},r=new Set,o=new Map;let a=1;function i(n,r){t._forcedRenderer||(t.currentRenderer=e.findPokedRenderer(n,r))}e.start=()=>{(t.initialized||(e.initialize(),t.initialized))&&e.startEventLoop()},e.setRenderWindow=e=>{xv("you want to call setView(view) instead of setRenderWindow on a vtk.js interactor")},e.setInteractorStyle=n=>{t.interactorStyle!==n&&(null!=t.interactorStyle&&t.interactorStyle.setInteractor(null),t.interactorStyle=n,null!=t.interactorStyle&&t.interactorStyle.getInteractor()!==e&&t.interactorStyle.setInteractor(e))},e.initialize=()=>{t.initialized=!0,e.enable(),e.render()},e.enable=()=>e.setEnabled(!0),e.disable=()=>e.setEnabled(!1),e.startEventLoop=()=>bv("empty event loop"),e.getCurrentRenderer=()=>(t.currentRenderer||i(0,0),t.currentRenderer);const s=t._getScreenEventPositionFor||function(e){const n=t._view.getCanvas(),r=n.getBoundingClientRect(),a=n.width/r.width,s=n.height/r.height,l={x:a*(e.clientX-r.left),y:s*(r.height-e.clientY+r.top),z:0};return(o.size<=1||!t.currentRenderer)&&i(l.x,l.y),l};function l(e){return{controlKey:e.ctrlKey,altKey:e.altKey,shiftKey:e.shiftKey}}function c(e){const t=l(e);return{key:e.key,keyCode:e.charCode,...t}}function u(e){return e.pointerType||""}const d=()=>{if(null===t.container)return;const{container:n}=t;n.addEventListener("contextmenu",Pv),n.addEventListener("wheel",e.handleWheel),n.addEventListener("DOMMouseScroll",e.handleWheel),n.addEventListener("pointerenter",e.handlePointerEnter),n.addEventListener("pointerleave",e.handlePointerLeave),n.addEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.addEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.addEventListener("pointerup",e.handlePointerUp),n.addEventListener("pointercancel",e.handlePointerCancel),document.addEventListener("keypress",e.handleKeyPress),document.addEventListener("keydown",e.handleKeyDown),document.addEventListener("keyup",e.handleKeyUp),document.addEventListener("pointerlockchange",e.handlePointerLockChange),n.style.touchAction="none",n.style.userSelect="none",n.style.webkitTapHighlightColor="rgba(0,0,0,0)"};e.bindEvents=e=>{null!==e&&n.setContainer(e)&&d()};const p=()=>{clearTimeout(t.moveTimeoutID),clearTimeout(t.wheelTimeoutID),t.moveTimeoutID=0,t.wheelTimeoutID=0,a=1;const{container:n}=t;n&&(n.removeEventListener("contextmenu",Pv),n.removeEventListener("wheel",e.handleWheel),n.removeEventListener("DOMMouseScroll",e.handleWheel),n.removeEventListener("pointerenter",e.handlePointerEnter),n.removeEventListener("pointerleave",e.handlePointerLeave),n.removeEventListener("pointermove",e.handlePointerMove,{passive:!1}),n.removeEventListener("pointerdown",e.handlePointerDown,{passive:!1}),n.removeEventListener("pointerup",e.handlePointerUp),n.removeEventListener("pointercancel",e.handlePointerCancel)),document.removeEventListener("keypress",e.handleKeyPress),document.removeEventListener("keydown",e.handleKeyDown),document.removeEventListener("keyup",e.handleKeyUp),document.removeEventListener("pointerlockchange",e.handlePointerLockChange),o.clear()};function f(){t._view&&t.enabled&&t.enableRender&&(t.inRender=!0,t._view.traverseAllPasses(),t.inRender=!1),e.invokeRenderEvent()}e.unbindEvents=()=>{p(),n.setContainer(null)},e.handleKeyPress=t=>{const n=c(t);e.keyPressEvent(n)},e.handleKeyDown=t=>{const n=c(t);e.keyDownEvent(n)},e.handleKeyUp=t=>{const n=c(t);e.keyUpEvent(n)},e.handlePointerEnter=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerEnterEvent(n),"mouse"===n.deviceType&&e.mouseEnterEvent(n)},e.handlePointerLeave=t=>{const n={...l(t),position:s(t),deviceType:u(t)};e.pointerLeaveEvent(n),"mouse"===n.deviceType&&e.mouseLeaveEvent(n)},e.handlePointerDown=n=>{if(!(n.button>2||e.isPointerLocked()))switch(t.preventDefaultOnPointerDown&&Pv(n),n.target.hasPointerCapture(n.pointerId)&&n.target.releasePointerCapture(n.pointerId),t.container.setPointerCapture(n.pointerId),o.has(n.pointerId)&&bv("[RenderWindowInteractor] duplicate pointerId 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pressed: ${t.button}`)}},e.requestPointerLock=()=>{t.container&&t.container.requestPointerLock()},e.exitPointerLock=()=>document.exitPointerLock?.(),e.isPointerLocked=()=>!!t.container&&document.pointerLockElement===t.container,e.handlePointerLockChange=()=>{e.isPointerLocked()?e.startPointerLockEvent():e.endPointerLockEvent()},e.requestAnimation=n=>{void 0!==n?r.has(n)?bv("requester is already registered for animating"):(r.add(n),t.animationRequest||1!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())):xv("undefined requester, can not start animating")},e.extendAnimation=n=>{const o=Date.now()+n;t._animationExtendedEnd=Math.max(t._animationExtendedEnd,o),t.animationRequest||0!==r.size||t.xrAnimation||(t._animationStartTime=Date.now(),t._animationFrameCount=0,t.animationRequest=requestAnimationFrame(e.handleAnimation),e.startAnimationEvent())},e.isAnimating=()=>t.xrAnimation||null!==t.animationRequest,e.cancelAnimation=function(n){let o=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(r.has(n))r.delete(n),t.animationRequest&&0===r.size&&Date.now()>t._animationExtendedEnd&&(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render());else if(!o){const e=n&&n.getClassName?n.getClassName():n;bv(`${e} did not request an 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t.lastGamepadValues[s.index][l].buttons||(t.lastGamepadValues[s.index][l].buttons[r]=!1),t.lastGamepadValues[s.index][l].buttons[r]!==s.buttons[r].pressed&&null!=a&&(e.button3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,pressed:s.buttons[r].pressed,device:"left"===n.handedness?yv.LeftController:yv.RightController,input:Iv[s.mapping]&&Iv[s.mapping][r]?Iv[s.mapping][r]:Tv.Trigger}),t.lastGamepadValues[s.index][l].buttons[r]=s.buttons[r].pressed),t.lastGamepadValues[s.index][l].buttons[r]&&null!=a&&e.move3DEvent({gamepad:s,position:a.transform.position,orientation:a.transform.orientation,targetPosition:i.transform.position,targetOrientation:i.transform.orientation,device:"left"===n.handedness?yv.LeftController:yv.RightController})}}))},e.handleMouseMove=n=>{const r={...l(n),position:s(n),deviceType:u(n)};0===t.moveTimeoutID?e.startMouseMoveEvent(r):(e.mouseMoveEvent(r),clearTimeout(t.moveTimeoutID)),t.moveTimeoutID=setTimeout((()=>{e.endMouseMoveEvent(),t.moveTimeoutID=0}),200)},e.handleAnimation=()=>{const n=Date.now();t._animationFrameCount++,n-t._animationStartTime>1e3&&t._animationFrameCount>1&&(t.recentAnimationFrameRate=1e3*(t._animationFrameCount-1)/(n-t._animationStartTime),t.lastFrameTime=1/t.recentAnimationFrameRate,e.animationFrameRateUpdateEvent(),t._animationStartTime=n,t._animationFrameCount=1),e.animationEvent(),f(),r.size>0||Date.now()<t._animationExtendedEnd?t.animationRequest=requestAnimationFrame(e.handleAnimation):(cancelAnimationFrame(t.animationRequest),t.animationRequest=null,e.endAnimationEvent(),e.render())},e.handleWheel=n=>{Pv(n);const 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o={type:n,pokedRenderer:t.currentRenderer,firstRenderer:e.getFirstRenderer(),...r};e[`invoke${n}`](o)}})),e.recognizeGesture=(n,r)=>{if(Object.keys(r).length>2)return;if(t.startingEventPositions||(t.startingEventPositions={}),"TouchStart"===n)return Object.keys(r).forEach((e=>{t.startingEventPositions[e]=r[e]})),void(t.currentGesture="Start");if("TouchEnd"===n)return"Pinch"===t.currentGesture&&(e.render(),e.endPinchEvent()),"Rotate"===t.currentGesture&&(e.render(),e.endRotateEvent()),"Pan"===t.currentGesture&&(e.render(),e.endPanEvent()),t.currentGesture="Start",void(t.startingEventPositions={});let o=0;const a=[],i=[];Object.keys(r).forEach((e=>{a[o]=r[e],i[o]=t.startingEventPositions[e],o++}));const s=Math.sqrt((i[0].x-i[1].x)*(i[0].x-i[1].x)+(i[0].y-i[1].y)*(i[0].y-i[1].y)),l=Math.sqrt((a[0].x-a[1].x)*(a[0].x-a[1].x)+(a[0].y-a[1].y)*(a[0].y-a[1].y));let c=Ro(Math.atan2(i[1].y-i[0].y,i[1].x-i[0].x)),u=Ro(Math.atan2(a[1].y-a[0].y,a[1].x-a[0].x)),d=u-c;u=u+180>=360?u-180:u+180,c=c+180>=360?c-180:c+180,Math.abs(u-c)<Math.abs(d)&&(d=u-c);const p=[];if(p[0]=(a[0].x-i[0].x+a[1].x-i[1].x)/2,p[1]=(a[0].y-i[0].y+a[1].y-i[1].y)/2,"TouchMove"===n)if("Start"===t.currentGesture){let n=.01*Math.sqrt(t.container.clientWidth*t.container.clientWidth+t.container.clientHeight*t.container.clientHeight);n<15&&(n=15);const o=Math.abs(l-s),a=3.1415926*l*Math.abs(d)/360,i=Math.sqrt(p[0]*p[0]+p[1]*p[1]);if(o>n&&o>a&&o>i){t.currentGesture="Pinch";const n={scale:1,touches:r};e.startPinchEvent(n)}else if(a>n&&a>i){t.currentGesture="Rotate";const n={rotation:0,touches:r};e.startRotateEvent(n)}else if(i>n){t.currentGesture="Pan";const n={translation:[0,0],touches:r};e.startPanEvent(n)}}else{if("Rotate"===t.currentGesture){const t={rotation:d,touches:r};e.rotateEvent(t)}if("Pinch"===t.currentGesture){const t={scale:l/s,touches:r};e.pinchEvent(t)}if("Pan"===t.currentGesture){const t={translation:p,touches:r};e.panEvent(t)}}},e.handleVisibilityChange=()=>{t._animationStartTime=Date.now(),t._animationFrameCount=0},e.setCurrentRenderer=e=>{t._forcedRenderer=!!e,t.currentRenderer=e},e.setContainer=e=>{p();const t=n.setContainer(e??null);return t&&d(),t},e.delete=()=>{for(;r.size;)e.cancelAnimation(r.values().next().value);void 0!==document.hidden&&document.removeEventListener("visibilitychange",e.handleVisibilityChange),t.container&&e.setContainer(null),n.delete()},void 0!==document.hidden&&document.addEventListener("visibilitychange",e.handleVisibilityChange,!1)}const Mv={renderWindow:null,interactorStyle:null,picker:null,pickingManager:null,initialized:!1,enabled:!1,enableRender:!0,currentRenderer:null,lightFollowCamera:!0,desiredUpdateRate:30,stillUpdateRate:2,container:null,recognizeGestures:!0,currentGesture:"Start",animationRequest:null,lastFrameTime:.1,recentAnimationFrameRate:10,wheelTimeoutID:0,moveTimeoutID:0,lastGamepadValues:{},preventDefaultOnPointerDown:!1,preventDefaultOnPointerUp:!1,mouseScrollDebounceByPass:!1};function Ev(e,t){let n=arguments.length>2&&void 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o(){Dv.handledEvents.forEach((n=>{e[`handle${n}`]&&t.subscribedEvents.push(t._interactor[`on${n}`]((r=>t.processEvents?e[`handle${n}`](r):Lv),t.priority))}))}e.setInteractor=n=>{n!==t._interactor&&(r(),t._interactor=n,n&&t.enabled&&o(),e.modified())},e.setEnabled=n=>{n!==t.enabled&&(r(),n&&(t._interactor?o():Vv("\n          The interactor must be set before subscribing to events\n        ")),t.enabled=n,e.modified())},e.computeDisplayToWorld=(e,n,r,o)=>e?t._interactor.getView().displayToWorld(n,r,o,e):null,e.computeWorldToDisplay=(e,n,r,o)=>e?t._interactor.getView().worldToDisplay(n,r,o,e):null,e.setPriority=e=>{n.setPriority(e)&&t._interactor&&(r(),o())}}const Nv={enabled:!0,priority:0,processEvents:!0,subscribedEvents:[]};function _v(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Nv,n),jt.obj(e,t),jt.event(e,t,"InteractionEvent"),jt.event(e,t,"StartInteractionEvent"),jt.event(e,t,"EndInteractionEvent"),jt.get(e,t,["_interactor","enabled"]),jt.setGet(e,t,["priority","processEvents"]),jt.moveToProtected(e,t,["interactor"]),Bv(e,t)}var Fv={newInstance:jt.newInstance(_v,"vtkInteractorObserver"),extend:_v,computeWorldToDisplay:function(e,t,n,r){return e.getRenderWindow().getViews()[0].worldToDisplay(t,n,r,e)},computeDisplayToWorld:function(e,t,n,r){return e.getRenderWindow().getViews()[0].displayToWorld(t,n,r,e)}},kv={States:{IS_START:0,IS_NONE:0,IS_ROTATE:1,IS_PAN:2,IS_SPIN:3,IS_DOLLY:4,IS_CAMERA_POSE:11,IS_WINDOW_LEVEL:1024,IS_SLICE:1025}};const{States:Gv}=kv,Uv={Rotate:Gv.IS_ROTATE,Pan:Gv.IS_PAN,Spin:Gv.IS_SPIN,Dolly:Gv.IS_DOLLY,CameraPose:Gv.IS_CAMERA_POSE,WindowLevel:Gv.IS_WINDOW_LEVEL,Slice:Gv.IS_SLICE};function zv(e,t){t.classHierarchy.push("vtkInteractorStyle"),Object.keys(Uv).forEach((n=>{jt.event(e,t,`Start${n}Event`),e[`start${n}`]=()=>{t.state===Gv.IS_NONE&&(t.state=Uv[n],t._interactor.requestAnimation(e),e.invokeStartInteractionEvent({type:"StartInteractionEvent"}),e[`invokeStart${n}Event`]({type:`Start${n}Event`}))},jt.event(e,t,`End${n}Event`),e[`end${n}`]=()=>{t.state===Uv[n]&&(t.state=Gv.IS_NONE,t._interactor.cancelAnimation(e),e.invokeEndInteractionEvent({type:"EndInteractionEvent"}),e[`invokeEnd${n}Event`]({type:`End${n}Event`}),t._interactor.render())}})),t.getRenderer=e=>t.focusedRenderer||e.pokedRenderer,e.handleKeyPress=e=>{const n=t._interactor;let r=null;switch(e.key){case"r":case"R":t.getRenderer(e).resetCamera(),n.render();break;case"w":case"W":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToWireframe&&t.setRepresentationToWireframe()})),n.render();break;case"s":case"S":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToSurface&&t.setRepresentationToSurface()})),n.render();break;case"v":case"V":r=t.getRenderer(e).getActors(),r.forEach((e=>{const t=e.getProperty();t.setRepresentationToPoints&&t.setRepresentationToPoints()})),n.render()}}}const Wv={state:Gv.IS_NONE,handleObservers:1,autoAdjustCameraClippingRange:1};function Hv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wv,n),Fv.extend(e,t,n),jt.setGet(e,t,["focusedRenderer"]),zv(e,t)}var jv={newInstance:jt.newInstance(Hv,"vtkInteractorStyle"),extend:Hv,...kv};const{States:Kv}=kv;function $v(e,t){t.classHierarchy.push("vtkInteractorStyleTrackballCamera"),e.handleMouseMove=n=>{const r=n.position,o=t.getRenderer(n);switch(t.state){case Kv.IS_ROTATE:e.handleMouseRotate(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_PAN:e.handleMousePan(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_DOLLY:e.handleMouseDolly(o,r),e.invokeInteractionEvent({type:"InteractionEvent"});break;case Kv.IS_SPIN:e.handleMouseSpin(o,r),e.invokeInteractionEvent({type:"InteractionEvent"})}t.previousPosition=r},e.handleButton3D=n=>{!n||!n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad?!n||n.pressed||n.device!==mv.RightController||n.input!==hv.Trigger&&n.input!==hv.TrackPad||t.state!==Kv.IS_CAMERA_POSE||e.endCameraPose():e.startCameraPose()},e.handleMove3D=n=>{t.state===Kv.IS_CAMERA_POSE&&e.updateCameraPose(n)},e.updateCameraPose=e=>{const n=t.getRenderer(e).getActiveCamera(),r=n.getPhysicalTranslation(),o=.025*n.getPhysicalScale(),a=n.physicalOrientationToWorldDirection([e.orientation.x,e.orientation.y,e.orientation.z,e.orientation.w]);n.setPhysicalTranslation(r[0]+a[0]*o,r[1]+a[1]*o,r[2]+a[2]*o)},e.handleLeftButtonPress=n=>{const r=n.position;t.previousPosition=r,n.shiftKey?n.controlKey||n.altKey?e.startDolly():e.startPan():n.controlKey||n.altKey?e.startSpin():e.startRotate()},e.handleLeftButtonRelease=()=>{switch(t.state){case Kv.IS_DOLLY:e.endDolly();break;case Kv.IS_PAN:e.endPan();break;case Kv.IS_SPIN:e.endSpin();break;case Kv.IS_ROTATE:e.endRotate()}},e.handleStartMouseWheel=()=>{e.startDolly()},e.handleEndMouseWheel=()=>{e.endDolly()},e.handleStartPinch=n=>{t.previousScale=n.scale,e.startDolly()},e.handleEndPinch=()=>{e.endDolly()},e.handleStartRotate=n=>{t.previousRotation=n.rotation,e.startRotate()},e.handleEndRotate=()=>{e.endRotate()},e.handleStartPan=n=>{t.previousTranslation=n.translation,e.startPan()},e.handleEndPan=()=>{e.endPan()},e.handlePinch=n=>{e.dollyByFactor(t.getRenderer(n),n.scale/t.previousScale),t.previousScale=n.scale},e.handlePan=n=>{const r=t.getRenderer(n).getActiveCamera();let o=r.getFocalPoint();o=e.computeWorldToDisplay(t.getRenderer(n),o[0],o[1],o[2]);const a=o[2],i=n.translation,s=t.previousTranslation,l=e.computeDisplayToWorld(t.getRenderer(n),o[0]+i[0]-s[0],o[1]+i[1]-s[1],a),c=e.computeDisplayToWorld(t.getRenderer(n),o[0],o[1],a),u=[];u[0]=c[0]-l[0],u[1]=c[1]-l[1],u[2]=c[2]-l[2],o=r.getFocalPoint();const d=r.getPosition();r.setFocalPoint(u[0]+o[0],u[1]+o[1],u[2]+o[2]),r.setPosition(u[0]+d[0],u[1]+d[1],u[2]+d[2]),t._interactor.getLightFollowCamera()&&t.getRenderer(n).updateLightsGeometryToFollowCamera(),r.orthogonalizeViewUp(),t.previousTranslation=n.translation},e.handleRotate=e=>{const n=t.getRenderer(e).getActiveCamera();n.roll(e.rotation-t.previousRotation),n.orthogonalizeViewUp(),t.previousRotation=e.rotation},e.handleMouseRotate=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=n.x-t.previousPosition.x,a=n.y-t.previousPosition.y,i=r.getView().getViewportSize(e);let s=-.1,l=-.1;i[0]&&i[1]&&(s=-20/i[1],l=-20/i[0]);const c=o*l*t.motionFactor,u=a*s*t.motionFactor,d=e.getActiveCamera();Number.isNaN(c)||Number.isNaN(u)||(d.azimuth(c),d.elevation(u),d.orthogonalizeViewUp()),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange(),r.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()},e.handleMouseSpin=(e,n)=>{if(!t.previousPosition)return;const r=t._interactor,o=e.getActiveCamera(),a=r.getView().getViewportCenter(e),i=Ro(Math.atan2(t.previousPosition.y-a[1],t.previousPosition.x-a[0])),s=Ro(Math.atan2(n.y-a[1],n.x-a[0]))-i;Number.isNaN(s)||(o.roll(s),o.orthogonalizeViewUp())},e.handleMousePan=(n,r)=>{if(!t.previousPosition)return;const o=n.getActiveCamera();let a=o.getFocalPoint();a=e.computeWorldToDisplay(n,a[0],a[1],a[2]);const i=a[2],s=e.computeDisplayToWorld(n,r.x,r.y,i),l=e.computeDisplayToWorld(n,t.previousPosition.x,t.previousPosition.y,i),c=[];c[0]=l[0]-s[0],c[1]=l[1]-s[1],c[2]=l[2]-s[2],a=o.getFocalPoint();const u=o.getPosition();o.setFocalPoint(c[0]+a[0],c[1]+a[1],c[2]+a[2]),o.setPosition(c[0]+u[0],c[1]+u[1],c[2]+u[2]),t._interactor.getLightFollowCamera()&&n.updateLightsGeometryToFollowCamera()},e.handleMouseDolly=(n,r)=>{if(!t.previousPosition)return;const o=r.y-t.previousPosition.y,a=t._interactor.getView().getViewportCenter(n),i=t.motionFactor*o/a[1];e.dollyByFactor(n,1.1**i)},e.handleMouseWheel=n=>{const r=1-n.spinY/t.zoomFactor;e.dollyByFactor(t.getRenderer(n),r)},e.dollyByFactor=(e,n)=>{if(Number.isNaN(n))return;const r=e.getActiveCamera();r.getParallelProjection()?r.setParallelScale(r.getParallelScale()/n):(r.dolly(n),t.autoAdjustCameraClippingRange&&e.resetCameraClippingRange()),t._interactor.getLightFollowCamera()&&e.updateLightsGeometryToFollowCamera()}}const qv={motionFactor:10,zoomFactor:10};function Xv(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qv,n),jv.extend(e,t,n),jt.setGet(e,t,["motionFactor","zoomFactor"]),$v(e,t)}var Yv={newInstance:jt.newInstance(Xv,"vtkInteractorStyleTrackballCamera"),extend:Xv};function Zv(e){return e}function Qv(e){return null===e||"null"===e?null:"true"===e||"false"!==e&&(void 0!==e&&"undefined"!==e?"["===e[0]&&"]"===e[e.length-1]?e.substring(1,e.length-1).split(",").map((e=>Qv(e.trim()))):""===e||Number.isNaN(Number(e))?e:Number(e):void 0)}var Jv=function(){let e=!(arguments.length>0&&void 0!==arguments[0])||arguments[0],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:window.location.search;const n={},r=e?Qv:Zv,o=(t||"").replace(/#.*/,"").replace("?","").split("&");return o.forEach((e=>{const[t,o]=e.split("=").map((e=>decodeURIComponent(e)));t&&(n[t]=!o||r(o))})),n};function ey(e,t){t.classHierarchy.push("vtkRenderPass"),e.getOperation=()=>t.currentOperation,e.setCurrentOperation=e=>{t.currentOperation=e,t.currentTraverseOperation=`traverse${jt.capitalize(t.currentOperation)}`},e.getTraverseOperation=()=>t.currentTraverseOperation,e.traverse=function(n){let 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n=e.getSize(),r=e.getContext();t.framebuffer=Xp.newInstance(),t.framebuffer.setOpenGLRenderWindow(e),t.framebuffer.create(...n),t.framebuffer.saveCurrentBindingsAndBuffers(),t.framebuffer.bind(),t.translucentRGBATexture=Ud.newInstance(),t.translucentRGBATexture.setInternalFormat(r.RGBA16F),t.translucentRGBATexture.setFormat(r.RGBA),t.translucentRGBATexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRGBATexture.setOpenGLRenderWindow(e),t.translucentRGBATexture.create2DFromRaw(n[0],n[1],4,"Float32Array",null),t.translucentRTexture=Ud.newInstance(),t.translucentRTexture.setInternalFormat(r.R16F),t.translucentRTexture.setFormat(r.RED),t.translucentRTexture.setOpenGLDataType(r.HALF_FLOAT),t.translucentRTexture.setOpenGLRenderWindow(e),t.translucentRTexture.create2DFromRaw(n[0],n[1],1,"Float32Array",null),t.translucentZTexture=Ud.newInstance(),t.translucentZTexture.setOpenGLRenderWindow(e),t.translucentZTexture.createDepthFromRaw(n[0],n[1],"Float32Array",null),t.framebuffer.setColorBuffer(t.translucentRGBATexture,0),t.framebuffer.setColorBuffer(t.translucentRTexture,1),t.framebuffer.setDepthBuffer(t.translucentZTexture)},e.createCopyShader=e=>{t.copyShader=e.getShaderCache().readyShaderProgramArray(["//VTK::System::Dec","attribute 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wy={newInstance:jt.newInstance(Iy,"vtkOpenGLTextureUnitManager"),extend:Iy};function Py(e,t){t.classHierarchy.push("vtkRenderWindowViewNode"),e.getViewNodeFactory=()=>null,e.getAspectRatio=()=>t.size[0]/t.size[1],e.getAspectRatioForRenderer=e=>{const n=e.getViewportByReference();return t.size[0]*(n[2]-n[0])/((n[3]-n[1])*t.size[1])},e.isInViewport=(t,n,r)=>{const o=r.getViewportByReference(),a=e.getFramebufferSize();return o[0]*a[0]<=t&&o[2]*a[0]>=t&&o[1]*a[1]<=n&&o[3]*a[1]>=n},e.getViewportSize=t=>{const n=t.getViewportByReference(),r=e.getFramebufferSize();return[(n[2]-n[0])*r[0],(n[3]-n[1])*r[1]]},e.getViewportCenter=t=>{const n=e.getViewportSize(t);return[.5*n[0],.5*n[1]]},e.displayToNormalizedDisplay=(t,n,r)=>{const o=e.getFramebufferSize();return[t/o[0],n/o[1],r]},e.normalizedDisplayToDisplay=(t,n,r)=>{const 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My={newInstance:jt.newInstance(Ry,"vtkRenderWindowViewNode"),extend:Ry};const{vtkDebugMacro:Ey,vtkErrorMacro:Dy}=jt,Vy={position:"absolute",top:0,left:0,width:"100%",height:"100%"},Ly=["activateTexture","deactivateTexture","disableCullFace","enableCullFace","get3DContext","getActiveFramebuffer","getContext","getDefaultTextureByteSize","getDefaultTextureInternalFormat","getDefaultToWebgl2","getGLInformations","getGraphicsMemoryInfo","getGraphicsResourceForObject","getHardwareMaximumLineWidth","getPixelData","getShaderCache","getTextureUnitForTexture","getTextureUnitManager","getWebgl2","makeCurrent","releaseGraphicsResources","registerGraphicsResourceUser","unregisterGraphicsResourceUser","restoreContext","setActiveFramebuffer","setContext","setDefaultToWebgl2","setGraphicsResourceForObject"];function By(e,t,n){const 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r=t.vertexInput.getIndexBuffer();r?e.drawIndexed(r.getIndexCount(),t.numberOfInstances,0,0,0):e.draw(t.numberOfVertices,t.numberOfInstances,0,0)},e.getBindables=()=>{const e=[...t.additionalBindables];t.UBO&&e.push(t.UBO),t.SSBO&&e.push(t.SSBO);for(let n=0;n<t.textureViews.length;n++){e.push(t.textureViews[n]);const r=t.textureViews[n].getSampler();r&&e.push(r)}return e},e.updatePipeline=()=>{e.computePipelineHash(),t.pipeline=t.device.getPipeline(t.pipelineHash),t.pipeline||(t.pipeline=rT.newInstance(),t.pipeline.setDevice(t.device),t.WebGPURenderer&&t.pipeline.addBindGroupLayout(t.WebGPURenderer.getBindGroup()),t.pipeline.addBindGroupLayout(t.bindGroup),e.generateShaderDescriptions(t.pipelineHash,t.pipeline,t.vertexInput),t.pipeline.setTopology(t.topology),t.pipeline.setRenderEncoder(t.renderEncoder),t.pipeline.setVertexState(t.vertexInput.getVertexInputInformation()),t.device.createPipeline(t.pipelineHash,t.pipeline))}}const ST={additionalBindables:void 0,bindGroup:null,device:null,fragmentShaderTemplate:null,numberOfInstances:1,numberOfVertices:0,pipelineHash:null,shaderReplacements:null,SSBO:null,textureViews:null,topology:"triangle-list",UBO:null,vertexShaderTemplate:null,WebGPURenderer:null};function AT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,ST,n),Zt.extend(e,t,n),t.textureViews=[],t.vertexInput=TT.newInstance(),t.bindGroup=Jy.newInstance({label:"mapperBG"}),t.additionalBindables=[],t.fragmentShaderTemplate=t.fragmentShaderTemplate||xT,t.vertexShaderTemplate=t.vertexShaderTemplate||bT,t.shaderReplacements=new Map,jt.get(e,t,["pipeline","vertexInput"]),jt.setGet(e,t,["additionalBindables","device","fragmentShaderTemplate","interpolate","numberOfInstances","numberOfVertices","pipelineHash","shaderReplacements","SSBO","textureViews","topology","UBO","vertexShaderTemplate","WebGPURenderer"]),CT(e,t)}var IT={newInstance:jt.newInstance(AT,"vtkWebGPUSimpleMapper"),extend:AT};function wT(e,t){t.classHierarchy.push("vtkWebGPUFullScreenQuad"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position"),r.addOutput("vec4<f32>","vertexVC");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);","output.vertexVC = vec4<f32>(vertexBC, 1);"]).result,r.setCode(o)},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.updateBuffers=()=>{const e=t.device.getBufferManager().getFullScreenQuadBuffer();t.vertexInput.addBuffer(e,["vertexBC"]),t.numberOfVertices=6}}const PT={};function OT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,PT,n),IT.extend(e,t,n),wT(e,t)}var RT={newInstance:jt.newInstance(OT,"vtkWebGPUFullScreenQuad"),extend:OT};const MT=["setBindGroup","setIndexBuffer","setVertexBuffer","draw","drawIndexed"];function ET(e,t){t.classHierarchy.push("vtkWebGPURenderEncoder"),e.begin=e=>{t.drawCallbacks=[],t.handle=e.beginRenderPass(t.description),t.label&&t.handle.pushDebugGroup(t.label)},e.end=()=>{for(let n=0;n<t.drawCallbacks.length;n++){const r=t.drawCallbacks[n],o=r.pipeline;e.setPipeline(o);for(let t=0;t<r.callbacks.length;t++)r.callbacks[t](e)}t.label&&t.handle.popDebugGroup(),t.handle.end(),t.boundPipeline=null},e.setPipeline=e=>{if(t.boundPipeline===e)return;t.handle.setPipeline(e.getHandle());const n=e.getPipelineDescription();if(t.colorTextureViews.length!==n.fragment.targets.length)console.log(`mismatched attachment counts on pipeline ${n.fragment.targets.length} while encoder has ${t.colorTextureViews.length}`),console.trace();else for(let e=0;e<t.colorTextureViews.length;e++){const r=t.colorTextureViews[e].getTexture()?.getFormat();r&&r!==n.fragment.targets[e].format&&(console.log(`mismatched attachments for attachment ${e} on pipeline ${n.fragment.targets[e].format} while encoder has ${r}`),console.trace())}if(!t.depthTextureView!=!("depthStencil"in n))console.log("mismatched depth attachments"),console.trace();else if(t.depthTextureView){const e=t.depthTextureView.getTexture()?.getFormat();e&&e!==n.depthStencil.format&&(console.log(`mismatched depth attachments on pipeline ${n.depthStencil.format} while encoder has ${e}`),console.trace())}t.boundPipeline=e},e.replaceShaderCode=e=>{t.replaceShaderCodeFunction(e)},e.setColorTextureView=(e,n)=>{t.colorTextureViews[e]!==n&&(t.colorTextureViews[e]=n)},e.activateBindGroup=e=>{const n=t.boundPipeline.getDevice(),r=t.boundPipeline.getBindGroupLayoutCount(e.getLabel());t.handle.setBindGroup(r,e.getBindGroup(n));const o=n.getBindGroupLayoutDescription(e.getBindGroupLayout(n)),a=n.getBindGroupLayoutDescription(t.boundPipeline.getBindGroupLayout(r));o!==a&&(console.log(`renderEncoder ${t.pipelineHash} mismatched bind group layouts bind group has\n${o}\n versus pipeline\n${a}\n`),console.trace())},e.attachTextureViews=()=>{for(let e=0;e<t.colorTextureViews.length;e++)t.description.colorAttachments[e]?t.description.colorAttachments[e].view=t.colorTextureViews[e].getHandle():t.description.colorAttachments[e]={view:t.colorTextureViews[e].getHandle()};t.depthTextureView&&(t.description.depthStencilAttachment.view=t.depthTextureView.getHandle())},e.registerDrawCallback=(e,n)=>{for(let r=0;r<t.drawCallbacks.length;r++)if(t.drawCallbacks[r].pipeline===e)return void t.drawCallbacks[r].callbacks.push(n);t.drawCallbacks.push({pipeline:e,callbacks:[n]})};for(let n=0;n<MT.length;n++)e[MT[n]]=function(){return t.handle[MT[n]](...arguments)}}const DT={description:null,handle:null,boundPipeline:null,pipelineHash:null,pipelineSettings:null,replaceShaderCodeFunction:null,depthTextureView:null,label:null};function VT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,DT,n),yt(e,t),t.description={colorAttachments:[{view:void 0,loadOp:"load",storeOp:"store"}],depthStencilAttachment:{view:void 0,depthLoadOp:"clear",depthClearValue:0,depthStoreOp:"store"}},t.replaceShaderCodeFunction=e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = computedColor;"]).result,t.setCode(n)},t.pipelineSettings={primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater-equal",format:"depth32float"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}},t.colorTextureViews=[],bt(e,t,["boundPipeline","colorTextureViews"]),At(e,t,["depthTextureView","description","handle","label","pipelineHash","pipelineSettings","replaceShaderCodeFunction"]),ET(e,t)}var LT={newInstance:Dt(VT,"vtkWebGPURenderEncoder"),extend:VT},BT={BufferUsage:{Verts:0,Lines:1,Triangles:2,Strips:3,LinesFromStrips:4,LinesFromTriangles:5,Points:6,UniformArray:7,PointArray:8,NormalsFromPoints:9,Texture:10,RawVertex:11,Storage:12,Index:13},PrimitiveTypes:{Start:0,Points:0,Lines:1,Triangles:2,TriangleStrips:3,TriangleEdges:4,TriangleStripEdges:5,End:6}};const NT=["getMappedRange","mapAsync","unmap"];function _T(e,t){t.classHierarchy.push("vtkWebGPUBuffer"),e.create=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e,usage:n,label:t.label}),t.sizeInBytes=e,t.usage=n},e.write=e=>{!function(e,t,n,r){const o=r.byteLength,a=e.createBuffer({size:o,usage:GPUBufferUsage.COPY_SRC,mappedAtCreation:!0}),i=a.getMappedRange(0,o);new Uint8Array(i).set(new Uint8Array(r)),a.unmap();const s=e.createCommandEncoder();s.copyBufferToBuffer(a,0,t,0,o);const l=s.finish();e.queue.submit([l]),a.destroy()}(t.device.getHandle(),t.handle,0,e.buffer)},e.createAndWrite=(e,n)=>{t.handle=t.device.getHandle().createBuffer({size:e.byteLength,usage:n,mappedAtCreation:!0,label:t.label}),t.sizeInBytes=e.byteLength,t.usage=n,new Uint8Array(t.handle.getMappedRange()).set(new Uint8Array(e.buffer)),t.handle.unmap()};for(let n=0;n<NT.length;n++)e[NT[n]]=function(){return t.handle[NT[n]](...arguments)}}const FT={device:null,handle:null,sizeInBytes:0,strideInBytes:0,arrayInformation:null,usage:null,label:null,sourceTime:null};function kT(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,FT,n),jt.obj(e,t),jt.get(e,t,["handle","sizeInBytes","usage"]),jt.setGet(e,t,["strideInBytes","device","arrayInformation","label","sourceTime"]),_T(e,t)}var GT={newInstance:jt.newInstance(kT),extend:kT,...BT};const{Representation:UT}=ds,{PrimitiveTypes:zT}=BT;class WT{constructor(){this.keys=new Uint32Array(10),this.values=new Uint32Array(10),this.count=0}clear(){this.count=0}has(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return!0}get(e){for(let t=0;t<this.count;t++)if(this.keys[t]===e)return this.values[t]}set(e,t){this.count<9&&(this.keys[this.count]=e,this.values[this.count++]=t)}}function HT(e,t,n){let r=e.pointIdToFlatId[t];return r<0&&(r=e.flatId,e.pointIdToFlatId[t]=r,e.flatIdToPointId[e.flatId]=t,e.flatIdToCellId[e.flatId]=n,e.flatId++),r}function jT(e,t,n){const r=e.length;for(let o=0;o<r;o++){let a=e[o];if(n.cellProvokedMap.has(a)){n.ibo[n.iboId++]=n.cellProvokedMap.get(a);for(let i=o+1;i<o+r;i++){a=e[i%r];const o=HT(n,a,t);n.ibo[n.iboId++]=o}return}}for(let o=0;o<r;o++){let a=e[o];if(!n.provokedPointIds[a]){let i=HT(n,a,t);n.provokedPointIds[a]=1,n.cellProvokedMap.set(a,i),n.flatIdToCellId[i]=t,n.ibo[n.iboId++]=i;for(let s=o+1;s<o+r;s++)a=e[s%r],i=HT(n,a,t),n.ibo[n.iboId++]=i;return}}let o=e[0],a=n.flatId;n.cellProvokedMap.set(o,a),n.flatIdToPointId[n.flatId]=o,n.flatIdToCellId[n.flatId]=t,n.flatId++,n.ibo[n.iboId++]=a;for(let i=1;i<r;i++)o=e[i],a=HT(n,o,t),n.ibo[n.iboId++]=a}function KT(e,t,n){const r=e.length;n.iboSize+=r;for(let t=0;t<r;t++){const r=e[t];if(n.cellProvokedMap.has(r))return}for(let t=0;t<r;t++){const r=e[t];if(!n.provokedPointIds[r])return n.provokedPointIds[r]=1,void n.cellProvokedMap.set(r,1)}n.cellProvokedMap.set(e[0],1),n.extraPoints++}let $T;const qT=new Uint32Array(1),XT=new Uint32Array(2),YT=new Uint32Array(3),ZT={anythingToPoints(e,t,n,r,o){for(let a=0;a<e;++a)qT[0]=t[n+a],$T(qT,r,o)},linesToWireframe(e,t,n,r,o){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o)},polysToWireframe(e,t,n,r,o){if(e>2)for(let a=0;a<e;++a)XT[0]=t[n+a],XT[1]=t[n+(a+1)%e],$T(XT,r,o)},stripsToWireframe(e,t,n,r,o){if(e>2){for(let a=0;a<e-1;++a)XT[0]=t[n+a],XT[1]=t[n+a+1],$T(XT,r,o);for(let a=0;a<e-2;a++)XT[0]=t[n+a],XT[1]=t[n+a+2],$T(XT,r,o)}},polysToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n],YT[1]=t[n+a+1],YT[2]=t[n+a+2],$T(YT,r,o)},stripsToSurface(e,t,n,r,o){for(let a=0;a<e-2;a++)YT[0]=t[n+a],YT[1]=t[n+a+1+a%2],YT[2]=t[n+a+1+(a+1)%2],$T(YT,r,o)}};function QT(e,t){t.classHierarchy.push("vtkWebGPUIndexBuffer"),e.buildIndexBuffer=e=>{const n=e.cells,r=e.primitiveType,o=e.representation,a=e.cellOffset,i=n.getData(),s=i.length,l=function(e){switch(e){case zT.Points:return"points";case zT.Lines:return"lines";case zT.Triangles:case zT.TriangleEdges:return"polys";case zT.TriangleStripEdges:case zT.TriangleStrips:return"strips";default:return""}}(r),c=e.numberOfPoints,u={provokedPointIds:new Uint8Array(c),extraPoints:0,iboSize:0,flatId:0,iboId:0,cellProvokedMap:new WT};let d=null;d=o===UT.POINTS||r===zT.Points?ZT.anythingToPoints:o===UT.WIREFRAME||r===zT.Lines?ZT[`${l}ToWireframe`]:ZT[`${l}ToSurface`],$T=KT;let p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;u.flatIdToPointId=c<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),c+u.extraPoints<36863?u.pointIdToFlatId=new Int16Array(c):u.pointIdToFlatId=new Int32Array(c),c+u.extraPoints<=65535?(u.ibo=new Uint16Array(u.iboSize),e.format="uint16"):(u.ibo=new Uint32Array(u.iboSize),e.format="uint32"),u.flatIdToCellId=p<=65535?new Uint16Array(c+u.extraPoints):new Uint32Array(c+u.extraPoints),u.pointIdToFlatId.fill(-1),u.provokedPointIds.fill(0),$T=jT,p=a||0;for(let e=0;e<s;)u.cellProvokedMap.clear(),d(i[e],i,e+1,p,u),e+=i[e]+1,p++;delete u.provokedPointIds,delete u.pointIdToFlatId,e.nativeArray=u.ibo,t.flatIdToPointId=u.flatIdToPointId,t.flatIdToCellId=u.flatIdToCellId,t.flatSize=u.flatId,t.indexCount=u.iboId}}const JT={flatIdToPointId:null,flatIdToCellId:null,flatSize:0,indexCount:0};function eb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,JT,n),GT.extend(e,t,n),jt.setGet(e,t,["flatIdToPointId","flatIdToCellId","flatSize","indexCount"]),QT(e,t)}var tb={newInstance:jt.newInstance(eb),extend:eb,...BT};const{BufferUsage:nb}=BT,{vtkErrorMacro:rb}=Kt,{VtkDataTypes:ob}=Ds;function ab(e,t,n,r,o){const a={},i=e.getFlatSize();if(!i)return a;let s=[0,0,0,0];o.shift&&(o.shift.length?s=o.shift:s.fill(o.shift));let l=[1,1,1,1];o.scale&&(o.scale.length?l=o.scale:l.fill(o.scale));const c=!!Object.prototype.hasOwnProperty.call(o,"packExtra")&&o.packExtra;let u,d=0;const p=at(r,i*(n+(c?1:0)));let f=e.getFlatIdToPointId();o.cellData&&(f=e.getFlatIdToCellId()),1===n?u=function(e){p[d++]=l[0]*t[e]+s[0]}:2===n?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1]}:3!==n||c?3===n&&c?u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=1*l[3]+s[3]}:4===n&&(u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2],p[d++]=l[3]*t[e+3]+s[3]}):u=function(e){p[d++]=l[0]*t[e]+s[0],p[d++]=l[1]*t[e+1]+s[1],p[d++]=l[2]*t[e+2]+s[2]};for(let e=0;e<i;e++)u(n*f[e]);return a.nativeArray=p,a}function ib(e,t,n,r){const o=[];return Ko([e[3*r]-e[3*n],e[3*r+1]-e[3*n+1],e[3*r+2]-e[3*n+2]],[e[3*t]-e[3*n],e[3*t+1]-e[3*n+1],e[3*t+2]-e[3*n+2]],o),qo(o),o}function sb(e,t){function n(e){let n,r;if(e.dataArray&&!e.nativeArray&&(e.nativeArray=e.dataArray.getData()),e.usage===nb.Index&&(n=tb.newInstance({label:e.label}),n.setDevice(t.device),r=GPUBufferUsage.INDEX|GPUBufferUsage.COPY_DST,n.buildIndexBuffer(e),n.createAndWrite(e.nativeArray,r),n.setArrayInformation([{format:e.format}])),n||(n=GT.newInstance({label:e.label}),n.setDevice(t.device)),e.usage===nb.UniformArray&&(r=GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Storage&&(r=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST,n.createAndWrite(e.nativeArray,r)),e.usage===nb.Texture&&(r=GPUBufferUsage.COPY_SRC,n.createAndWrite(e.nativeArray,r)),e.usage===nb.PointArray){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=ab(e.indexBuffer,e.dataArray.getData(),e.dataArray.getNumberOfComponents(),t,{packExtra:e.packExtra,shift:e.shift,scale:e.scale,cellData:e.cellData,cellOffset:e.cellOffset});n.createAndWrite(o.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:e.cellData?"flat":"perspective"}])}if(e.usage===nb.NormalsFromPoints){r=GPUBufferUsage.VERTEX;const t=dT(e.format),o=function(e,t){const n=t.getData(),r=e.getData();if(!r||!n)return null;const o=new Int8Array(4*e.getNumberOfCells()),a=r.length;let i=0;for(let e=0;e<a;){const t=ib(n,r[e+1],r[e+2],r[e+3]);o[i++]=127*t[0],o[i++]=127*t[1],o[i++]=127*t[2],o[i++]=127,e+=r[e]+1}return o}(e.cells,e.dataArray),a=ab(e.indexBuffer,o,4,t,{cellData:!0});n.createAndWrite(a.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format,interpolation:"flat"}])}return e.usage===nb.RawVertex&&(r=GPUBufferUsage.VERTEX,n.createAndWrite(e.nativeArray,r),n.setStrideInBytes(uT(e.format)),n.setArrayInformation([{offset:0,format:e.format}])),n.setSourceTime(e.time),n}t.classHierarchy.push("vtkWebGPUBufferManager"),e.hasBuffer=e=>t.device.hasCachedObject(e),e.getBuffer=e=>e.hash?t.device.getCachedObject(e.hash,n,e):n(e),e.getBufferForPointArray=(t,n)=>{const r=function(e){let t;switch(e.getDataType()){case ob.UNSIGNED_CHAR:t="uint8";break;case ob.FLOAT:t="float32";break;case ob.UNSIGNED_INT:t="uint32";break;case ob.INT:t="sint32";break;case ob.DOUBLE:t="float32";break;case ob.UNSIGNED_SHORT:t="uint16";break;case ob.SHORT:t="sin16";break;default:t="float32"}switch(e.getNumberOfComponents()){case 2:t+="x2";break;case 3:t.includes("32")||rb(`unsupported x3 type for ${t}`),t+="x3";break;case 4:t+="x4"}return t}(t),o={hash:`${t.getMTime()}I${n.getMTime()}${r}`,usage:nb.PointArray,format:r,dataArray:t,indexBuffer:n};return e.getBuffer(o)},e.getFullScreenQuadBuffer=()=>{if(t.fullScreenQuadBuffer)return t.fullScreenQuadBuffer;t.fullScreenQuadBuffer=GT.newInstance(),t.fullScreenQuadBuffer.setDevice(t.device);const e=new Float32Array([-1,-1,0,1,-1,0,1,1,0,-1,-1,0,1,1,0,-1,1,0]);return t.fullScreenQuadBuffer.createAndWrite(e,GPUBufferUsage.VERTEX),t.fullScreenQuadBuffer.setStrideInBytes(12),t.fullScreenQuadBuffer.setArrayInformation([{offset:0,format:"float32x3"}]),t.fullScreenQuadBuffer}}const lb={device:null,fullScreenQuadBuffer:null};function cb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,lb,n),yt(e,t),At(e,t,["device"]),sb(e,t)}var ub={newInstance:Dt(cb),extend:cb,...BT};function db(e,t){t.classHierarchy.push("vtkWebGPUSampler"),e.create=function(e){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};t.device=e,t.options.addressModeU=n.addressModeU?n.addressModeU:"clamp-to-edge",t.options.addressModeV=n.addressModeV?n.addressModeV:"clamp-to-edge",t.options.addressModeW=n.addressModeW?n.addressModeW:"clamp-to-edge",t.options.magFilter=n.magFilter?n.magFilter:"nearest",t.options.minFilter=n.minFilter?n.minFilter:"nearest",t.options.mipmapFilter=n.mipmapFilter?n.mipmapFilter:"nearest",t.options.label=t.label,t.handle=t.device.getHandle().createSampler(t.options),t.bindGroupTime.modified()},e.getShaderCode=(e,n)=>`@binding(${e}) @group(${n}) var ${t.label}: sampler;`,e.getBindGroupEntry=()=>({resource:t.handle})}const pb={device:null,handle:null,label:null,options:null};function fb(e,t){let n=arguments.length>2&&void 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yb={newInstance:jt.newInstance(vb),extend:vb};const{BufferUsage:Tb}=ub;function bb(e,t){t.classHierarchy.push("vtkWebGPUTexture"),e.create=(e,n)=>{t.device=e,t.width=n.width,t.height=n.height,t.depth=n.depth?n.depth:1;const r=1===t.depth?"2d":"3d";t.format=n.format?n.format:"rgba8unorm",t.mipLevel=n.mipLevel?n.mipLevel:0,t.usage=n.usage?n.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST,t.handle=t.device.getHandle().createTexture({size:[t.width,t.height,t.depth],format:t.format,usage:t.usage,label:t.label,dimension:r,mipLevelCount:t.mipLevel+1})},e.assignFromHandle=(e,n,r)=>{t.device=e,t.handle=n,t.width=r.width,t.height=r.height,t.depth=r.depth?r.depth:1,t.format=r.format?r.format:"rgba8unorm",t.usage=r.usage?r.usage:GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST},e.writeImageData=n=>{let r=[];if(n.canvas)return 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a=t.translucentAccumulateTexture.createView("oitpAccumTexture");t.translucentRenderEncoder.setColorTextureView(1,a),t.fullScreenQuad=RT.newInstance(),t.fullScreenQuad.setDevice(r.getDevice()),t.fullScreenQuad.setPipelineHash("oitpfsq"),t.fullScreenQuad.setTextureViews(t.translucentRenderEncoder.getColorTextureViews()),t.fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var tcoord: vec2<i32> = vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y));\n  var reveal: f32 = textureLoad(oitpAccumTexture, tcoord, 0).r;\n  if (reveal == 1.0) { discard; }\n  var tcolor: vec4<f32> = textureLoad(oitpColorTexture, tcoord, 0);\n  var total: f32 = max(tcolor.a, 0.01);\n  var computedColor: vec4<f32> = vec4<f32>(tcolor.r/total, tcolor.g/total, tcolor.b/total, 1.0 - reveal);\n\n  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0,clearValue:[1,0,0,0],loadOp:"clear",storeOp:"store"}],e.depthStencilAttachment={view:void 0,depthLoadOp:"load",depthStoreOp:"store"},t.translucentRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor"),t.addOutput("f32","outAccum"),t.addBuiltinInput("vec4<f32>","@builtin(position) fragPos");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["var w: f32 = computedColor.a * pow(0.1 + input.fragPos.z, 2.0);","output.outColor = vec4<f32>(computedColor.rgb*w, w);","output.outAccum = 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Bb={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,bindGroupLayoutEntry:null,bindGroupEntry:null};function Nb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Bb,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"uniform"}},t.sendTime={},jt.obj(t.sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.sendDirty=!0,t.sortDirty=!0,jt.get(e,t,["binding","bindGroupTime"]),jt.setGet(e,t,["bindGroupLayoutEntry","device","label","sizeInBytes"]),Lb(e,t)}var _b={newInstance:jt.newInstance(Nb,"vtkWebGPUUniformBuffer"),extend:Nb};const{BufferUsage:Fb}=ub,{vtkErrorMacro:kb}=jt;function Gb(e,t){t.classHierarchy.push("vtkWebGPUStorageBuffer"),e.addEntry=(e,n)=>{if(t._bufferEntryNames.has(e))return void kb(`entry named ${e} already exists`);t._bufferEntryNames.set(e,t.bufferEntries.length);const 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o=t._bufferEntryNames.get(n);if(void 0===o)return void kb(`entry named ${n} not found in UBO`);const a=t.bufferEntries[o];e.createView(a.nativeType);const i=t[a.nativeType];for(let e=0;e<t.numberOfInstances;e++){const n=(a.offset+e*t.sizeInBytes)/i.BYTES_PER_ELEMENT;for(let t=0;t<3;t++)for(let o=0;o<3;o++)i[n+4*t+o]=r[9*e+3*t+o]}},e.getSendTime=()=>t._sendTime.getMTime(),e.getShaderCode=(e,n)=>{const r=[`struct ${t.label}StructEntry\n{`];for(let e=0;e<t.bufferEntries.length;e++){const n=t.bufferEntries[e];r.push(`  ${n.name}: ${n.type},`)}return r.push(`\n};\nstruct ${t.label}Struct\n{\n  values: array<${t.label}StructEntry>,\n};\n@binding(${e}) @group(${n}) var<storage, read> ${t.label}: ${t.label}Struct;\n`),r.join("\n")},e.getBindGroupEntry=()=>({resource:{buffer:t._buffer.getHandle()}}),e.clearData=()=>{t.numberOfInstances=0,t.sizeInBytes=0,t.bufferEntries=[],t._bufferEntryNames=new Map,t._buffer=null,delete t.arrayBuffer,delete t.Float32Array}}const Ub={bufferEntries:null,bufferEntryNames:null,sizeInBytes:0,label:null,numberOfInstances:1};function zb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Ub,n),jt.obj(e,t),t._bufferEntryNames=new Map,t.bufferEntries=[],t._sendTime={},jt.obj(t._sendTime,{mtime:0}),t.bindGroupTime={},jt.obj(t.bindGroupTime,{mtime:0}),t.bindGroupLayoutEntry=t.bindGroupLayoutEntry||{buffer:{type:"read-only-storage"}},jt.get(e,t,["bindGroupTime"]),jt.setGet(e,t,["device","bindGroupLayoutEntry","label","numberOfInstances","sizeInBytes"]),Gb(e,t)}var Wb={newInstance:jt.newInstance(zb,"vtkWebGPUStorageBuffer"),extend:zb};const Hb="\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::Volume::TraverseDec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\nfn getTextureValue(vTex: texture_3d<f32>, tpos: vec4<f32>) -> f32\n{\n  // todo multicomponent support\n  return textureSampleLevel(vTex, clampSampler, tpos.xyz, 0.0).r;\n}\n\nfn getGradient(vTex: texture_3d<f32>, tpos: vec4<f32>, vNum: i32, scalar: f32) -> vec4<f32>\n{\n  var result: vec4<f32>;\n\n  var tstep: vec4<f32> = volumeSSBO.values[vNum].tstep;\n  result.x = getTextureValue(vTex, tpos + vec4<f32>(tstep.x, 0.0, 0.0, 1.0)) - scalar;\n  result.y = getTextureValue(vTex, tpos + vec4<f32>(0.0, tstep.y, 0.0, 1.0)) - scalar;\n  result.z = getTextureValue(vTex, tpos + vec4<f32>(0.0, 0.0, tstep.z, 1.0)) - scalar;\n  result.w = 0.0;\n\n  // divide by spacing as that is our delta\n  result = result / volumeSSBO.values[vNum].spacing;\n  // now we have a gradient in unit tcoords\n\n  var grad: f32 = length(result.xyz);\n  if (grad > 0.0)\n  {\n    // rotate to View Coords, needed for lighting and shading\n    var nMat: mat4x4<f32> = rendererUBO.SCVCMatrix * volumeSSBO.values[vNum].planeNormals;\n    result = nMat * result;\n    result = result / length(result);\n  }\n\n  // store gradient magnitude in .w\n  result.w = grad;\n\n  return result;\n}\n\nfn processVolume(vTex: texture_3d<f32>, vNum: i32, cNum: i32, posSC: vec4<f32>, tfunRows: f32) -> vec4<f32>\n{\n  var outColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n\n  // convert to tcoords and reject if outside the volume\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*posSC;\n  if (tpos.x < 0.0 || tpos.y < 0.0 || tpos.z < 0.0 ||\n      tpos.x > 1.0 || tpos.y > 1.0 || tpos.z > 1.0) { return outColor; }\n\n  var scalar: f32 = getTextureValue(vTex, tpos);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n\n  var gofactor: f32 = 1.0;\n  var normal: vec4<f32> = vec4<f32>(0.0,0.0,0.0,0.0);\n  if (componentSSBO.values[cNum].gomin <  1.0 || volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    normal = getGradient(vTex, tpos, vNum, scalar);\n    if (componentSSBO.values[cNum].gomin <  1.0)\n    {\n      gofactor = clamp(normal.a*componentSSBO.values[cNum].goScale + componentSSBO.values[cNum].goShift,\n      componentSSBO.values[cNum].gomin, componentSSBO.values[cNum].gomax);\n    }\n  }\n\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n\n  if (volumeSSBO.values[vNum].shade[0] > 0.0)\n  {\n    color = color*abs(normal.z);\n  }\n\n  outColor = vec4<f32>(color.rgb, gofactor * opacity);\n\n  return outColor;\n}\n\n// adjust the start and end point of a raycast such that it intersects the unit cube.\n// This function is used to take a raycast starting point and step vector\n// and numSteps and return the startijng and ending steps for intersecting the\n// unit cube. Recall for a 3D texture, the unit cube is the range of texture coordsinates\n// that have valid values. So this funtion can be used to take a ray in texture coordinates\n// and bound it to intersecting the texture.\n//\nfn adjustBounds(tpos: vec4<f32>, tstep: vec4<f32>, numSteps: f32) -> vec2<f32>\n{\n  var result: vec2<f32> = vec2<f32>(0.0, numSteps);\n  var tpos2: vec4<f32> = tpos + tstep*numSteps;\n\n  // move tpos to the start of the volume\n  var adjust: f32 =\n    min(\n      max(tpos.x/tstep.x, (tpos.x - 1.0)/tstep.x),\n      min(\n        max((tpos.y - 1.0)/tstep.y, tpos.y/tstep.y),\n        max((tpos.z - 1.0)/tstep.z, tpos.z/tstep.z)));\n  if (adjust < 0.0)\n  {\n    result.x = result.x - adjust;\n  }\n\n  // adjust length to the end\n  adjust =\n    max(\n      min(tpos2.x/tstep.x, (tpos2.x - 1.0)/tstep.x),\n      max(\n        min((tpos2.y - 1.0)/tstep.y, tpos2.y/tstep.y),\n        min((tpos2.z - 1.0)/tstep.z, tpos2.z/tstep.z)));\n  if (adjust > 0.0)\n  {\n    result.y = result.y - adjust;\n  }\n\n  return result;\n}\n\nfn getSimpleColor(scalar: f32, vNum: i32, cNum: i32) -> vec4<f32>\n{\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var coord: vec2<f32> =\n    vec2<f32>(scalar * componentSSBO.values[cNum].cScale + componentSSBO.values[cNum].cShift,\n      (0.5 + 2.0 * f32(vNum)) / tfunRows);\n  var color: vec4<f32> = textureSampleLevel(tfunTexture, clampSampler, coord, 0.0);\n  coord.x = (scalar * componentSSBO.values[cNum].oScale + componentSSBO.values[cNum].oShift);\n  var opacity: f32 = textureSampleLevel(ofunTexture, clampSampler, coord, 0.0).r;\n  return vec4<f32>(color.rgb, opacity);\n}\n\nfn traverseMax(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var maxVal: f32 = -1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar > maxVal)\n    {\n      maxVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(maxVal, vNum, cNum);\n}\n\nfn traverseMin(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var minVal: f32 = 1.0e37;\n  loop\n  {\n    var scalar: f32 = getTextureValue(vTex, tpos);\n    if (scalar < minVal)\n    {\n      minVal = scalar;\n    }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(minVal, vNum, cNum);\n}\n\nfn traverseAverage(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var avgVal: f32 = 0.0;\n  var sampleCount: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      avgVal = avgVal + sample;\n      sampleCount = sampleCount + 1.0;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  if (sampleCount <= 0.0)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(avgVal/sampleCount, vNum, cNum);\n}\n\nfn traverseAdditive(vTex: texture_3d<f32>, vNum: i32, cNum: i32, rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>)\n{\n  // convert to tcoords and reject if outside the volume\n  var numSteps: f32 = rayLengthSC/mapperUBO.SampleDistance;\n  var tpos: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*minPosSC;\n  var tpos2: vec4<f32> = volumeSSBO.values[vNum].SCTCMatrix*(minPosSC + rayStepSC);\n  var tstep: vec4<f32> = tpos2 - tpos;\n\n  var rayBounds: vec2<f32> = adjustBounds(tpos, tstep, numSteps);\n\n  // did we hit anything\n  if (rayBounds.x >= rayBounds.y)\n  {\n    traverseVals[vNum] = vec4<f32>(0.0,0.0,0.0,0.0);\n    return;\n  }\n\n  let ipRange: vec4<f32> = volumeSSBO.values[vNum].ipScalarRange;\n  tpos = tpos + tstep*rayBounds.x;\n  var curDist: f32 = rayBounds.x;\n  var sumVal: f32 = 0.0;\n  loop\n  {\n    var sample: f32 = getTextureValue(vTex, tpos);\n    // right now leave filtering off until WebGL changes get merged\n    // if (ipRange.z == 0.0 || sample >= ipRange.x && sample <= ipRange.y)\n    // {\n      sumVal = sumVal + sample;\n    // }\n\n    // increment position\n    curDist = curDist + 1.0;\n    tpos = tpos + tstep;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayBounds.y) { break; }\n  }\n\n  // process to get the color and opacity\n  traverseVals[vNum] = getSimpleColor(sumVal, vNum, cNum);\n}\n\nfn composite(rayLengthSC: f32, minPosSC: vec4<f32>, rayStepSC: vec4<f32>) -> vec4<f32>\n{\n  // initial ray position is at the beginning\n  var rayPosSC: vec4<f32> = minPosSC;\n\n  // how many rows (tfuns) do we have in our tfunTexture\n  var tfunRows: f32 = f32(textureDimensions(tfunTexture).y);\n\n  var curDist: f32 = 0.0;\n  var computedColor: vec4<f32> = vec4<f32>(0.0, 0.0, 0.0, 0.0);\n  var sampleColor: vec4<f32>;\n//VTK::Volume::TraverseCalls\n\n  loop\n  {\n    // for each volume, sample and accumulate color\n//VTK::Volume::CompositeCalls\n\n    // increment position\n    curDist = curDist + mapperUBO.SampleDistance;\n    rayPosSC = rayPosSC + rayStepSC;\n\n    // check if we have reached a terminating condition\n    if (curDist > rayLengthSC) { break; }\n    if (computedColor.a > 0.98) { break; }\n  }\n  return computedColor;\n}\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var rayMax: f32 = textureSampleLevel(maxTexture, clampSampler, input.tcoordVS, 0.0).r;\n  var rayMin: f32 = textureSampleLevel(minTexture, clampSampler, input.tcoordVS, 0.0).r;\n\n  // discard empty rays\n  if (rayMax <= rayMin) { discard; }\n  else\n  {\n    // compute start and end ray positions in view coordinates\n    var minPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMax, 1.0);\n    minPosSC = minPosSC * (1.0 / minPosSC.w);\n    var maxPosSC: vec4<f32> = rendererUBO.PCSCMatrix*vec4<f32>(2.0 * input.tcoordVS.x - 1.0, 1.0 - 2.0 * input.tcoordVS.y, rayMin, 1.0);\n    maxPosSC = maxPosSC * (1.0 / maxPosSC.w);\n\n    var rayLengthSC: f32 = distance(minPosSC.xyz, maxPosSC.xyz);\n    var rayStepSC: vec4<f32> = (maxPosSC - minPosSC)*(mapperUBO.SampleDistance/rayLengthSC);\n    rayStepSC.w = 0.0;\n\n    var computedColor: vec4<f32>;\n\n//VTK::Volume::Loop\n\n//VTK::RenderEncoder::Impl\n  }\n\n  return output;\n}\n",jb=new Float64Array(16),Kb=new Float64Array(16);function $b(e,t){t.classHierarchy.push("vtkWebGPUVolumePassFSQ"),e.replaceShaderPosition=(e,t,n)=>{const r=t.getShaderDescription("vertex");r.addBuiltinOutput("vec4<f32>","@builtin(position) Position");let o=r.getCode();o=Xy.substitute(o,"//VTK::Position::Impl",["output.tcoordVS = vec2<f32>(vertexBC.x * 0.5 + 0.5, 1.0 - vertexBC.y * 0.5 - 0.5);","output.Position = vec4<f32>(vertexBC, 1.0);"]).result,r.setCode(o),t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")},t.shaderReplacements.set("replaceShaderPosition",e.replaceShaderPosition),e.replaceShaderVolume=(e,n,r)=>{const o=n.getShaderDescription("fragment");let a=o.getCode();const i=[],s=[];for(let e=0;e<t.volumes.length;e++)t.volumes[e].getRenderable().getMapper().getBlendMode()===Fg.COMPOSITE_BLEND?(i.push(`    sampleColor = processVolume(volTexture${e}, ${e}, ${t.rowStarts[e]}, rayPosSC, tfunRows);`),i.push("    computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);")):(s.push(`  sampleColor = traverseVals[${e}];`),s.push("  computedColor = vec4<f32>(\n          sampleColor.a * sampleColor.rgb * (1.0 - computedColor.a) + computedColor.rgb,\n          (1.0 - computedColor.a)*sampleColor.a + computedColor.a);"));a=Xy.substitute(a,"//VTK::Volume::CompositeCalls",i).result,a=Xy.substitute(a,"//VTK::Volume::TraverseCalls",s).result,a=Xy.substitute(a,"//VTK::Volume::TraverseDec",[`var<private> traverseVals: array<vec4<f32>,${t.volumes.length}>;`]).result;let l=!1;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();n===Fg.COMPOSITE_BLEND?l=!0:n===Fg.MAXIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMax(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.MINIMUM_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseMin(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.AVERAGE_INTENSITY_BLEND?a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAverage(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result:n===Fg.ADDITIVE_INTENSITY_BLEND&&(a=Xy.substitute(a,"//VTK::Volume::Loop",[`    traverseAdditive(volTexture${e}, ${e}, ${e}, rayLengthSC, minPosSC, rayStepSC);`,`    computedColor = traverseVals[${e}];`,"//VTK::Volume::Loop"]).result)}l&&(a=Xy.substitute(a,"//VTK::Volume::Loop",["    computedColor = composite(rayLengthSC, minPosSC, rayStepSC);"]).result),o.setCode(a)},t.shaderReplacements.set("replaceShaderVolume",e.replaceShaderVolume),e.updateLUTImage=n=>{let r=e.getMTime();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper().getInputData();r=Math.max(r,n.getMTime(),o.getMTime())}if(r<t.lutBuildTime.getMTime())return;t.numRows=0,t.rowStarts=[];for(let e=0;e<t.volumes.length;e++){t.rowStarts.push(t.numRows);const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents()?i:1;t.numRows+=s}const o=new Uint8ClampedArray(2*t.numRows*t.rowLength*4),a=new Float32Array(2*t.numRows*t.rowLength);let i=0;const s=new Float32Array(3*t.rowLength),l=t.rowLength;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),c=n.getProperty(),u=r.getInputData(),d=(u.getPointData()&&u.getPointData().getScalars()).getNumberOfComponents(),p=c.getIndependentComponents()?d:1;for(let e=0;e<p;++e){const n=c.getRGBTransferFunction(e),r=n.getRange();n.getTable(r[0],r[1],l,s,1);let u=i*l*4;for(let e=0;e<l;++e){o[u+4*e]=255*s[3*e],o[u+4*e+1]=255*s[3*e+1],o[u+4*e+2]=255*s[3*e+2],o[u+4*e+3]=255;for(let t=0;t<4;t++)o[u+4*(l+e)+t]=o[u+4*e+t]}const d=c.getScalarOpacity(e),p=t.sampleDist/c.getScalarOpacityUnitDistance(e),f=d.getRange();d.getTable(f[0],f[1],l,s,1),u=i*l;for(let e=0;e<l;++e)a[u+e]=1-(1-s[e])**p,a[u+e+l]=a[u+e];i+=2}}{const e={nativeArray:o,width:t.rowLength,height:2*t.numRows,depth:1,format:"rgba8unorm"},r=n.getTextureManager().getTexture(e).createView("tfunTexture");t.textureViews[2]=r}{const e={nativeArray:a,width:t.rowLength,height:2*t.numRows,depth:1,format:"r16float"},r=n.getTextureManager().getTexture(e).createView("ofunTexture");t.textureViews[3]=r}t.lutBuildTime.modified()},e.updateSSBO=n=>{let r=Math.max(e.getMTime(),t.WebGPURenderer.getStabilizedTime());for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),o=n.getMapper(),a=o.getInputData();r=Math.max(r,n.getMTime(),a.getMTime(),o.getMTime())}if(r<t.SSBO.getSendTime())return;const o=t.WebGPURenderer.getStabilizedCenterByReference();t.SSBO.clearData(),t.SSBO.setNumberOfInstances(t.volumes.length);const a=new Float64Array(16*t.volumes.length),i=new Float64Array(16*t.volumes.length),s=new Float64Array(4*t.volumes.length),l=new Float64Array(4*t.volumes.length),c=new Float64Array(4*t.volumes.length),u=new Float64Array(4*t.volumes.length);for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),d=r.getInputData();g(jb),b(jb,jb,o);const p=n.getMatrix();m(Kb,p),h(Kb,Kb),T(jb,Kb,jb);const f=d.getWorldToIndex();T(jb,f,jb);const v=d.getDimensions();g(Kb),x(Kb,Kb,[1/v[0],1/v[1],1/v[2]]),T(jb,Kb,jb);for(let t=0;t<16;t++)a[16*e+t]=jb[t];h(jb,jb);for(let t=0;t<4;t++)i[16*e+4*t]=jb[4*t],i[16*e+4*t+1]=jb[4*t+1],i[16*e+4*t+2]=jb[4*t+2],i[16*e+4*t+3]=0;s[4*e]=1/v[0],s[4*e+1]=1/v[1],s[4*e+2]=1/v[2],s[4*e+3]=1,l[4*e]=n.getProperty().getShade()?1:0;const y=d.getSpacing();c[4*e]=y[0],c[4*e+1]=y[1],c[4*e+2]=y[2],c[4*e+3]=1;const C=t.textureViews[e+4].getTexture().getScale(),S=r.getIpScalarRange();u[4*e]=S[0]/C,u[4*e+1]=S[1]/C,u[4*e+2]=r.getFilterMode()}t.SSBO.addEntry("SCTCMatrix","mat4x4<f32>"),t.SSBO.addEntry("planeNormals","mat4x4<f32>"),t.SSBO.addEntry("shade","vec4<f32>"),t.SSBO.addEntry("tstep","vec4<f32>"),t.SSBO.addEntry("spacing","vec4<f32>"),t.SSBO.addEntry("ipScalarRange","vec4<f32>"),t.SSBO.setAllInstancesFromArray("SCTCMatrix",a),t.SSBO.setAllInstancesFromArray("planeNormals",i),t.SSBO.setAllInstancesFromArray("shade",l),t.SSBO.setAllInstancesFromArray("tstep",s),t.SSBO.setAllInstancesFromArray("spacing",c),t.SSBO.setAllInstancesFromArray("ipScalarRange",u),t.SSBO.send(n),t.componentSSBO.clearData(),t.componentSSBO.setNumberOfInstances(t.numRows);const d=new Float64Array(t.numRows),p=new Float64Array(t.numRows),f=new Float64Array(t.numRows),v=new Float64Array(t.numRows),y=new Float64Array(t.numRows),C=new Float64Array(t.numRows),S=new Float64Array(t.numRows),A=new Float64Array(t.numRows);let I=0;for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable(),r=n.getMapper(),o=n.getProperty(),a=r.getInputData(),i=(a.getPointData()&&a.getPointData().getScalars()).getNumberOfComponents(),s=o.getIndependentComponents(),l=t.textureViews[e+4].getTexture().getFormat(),c=cT(l),u={scale:[255],offset:[0]};2===c.elementSize&&"float"===c.sampleType&&(u.scale[0]=1);for(let e=0;e<i;e++){const t=s?e:0,n=u.scale[e],r=o.getScalarOpacity(t).getRange(),a=n/(r[1]-r[0]),i=(u.offset[e]-r[0])/(r[1]-r[0]);v[I]=i,f[I]=a;const l=o.getRGBTransferFunction(t).getRange();if(p[I]=(u.offset[e]-l[0])/(l[1]-l[0]),d[I]=n/(l[1]-l[0]),o.getUseGradientOpacity(t)){const e=o.getGradientOpacityMinimumOpacity(t),r=o.getGradientOpacityMaximumOpacity(t);y[I]=e,C[I]=r;const a=[o.getGradientOpacityMinimumValue(t),o.getGradientOpacityMaximumValue(t)];A[I]=n*(r-e)/(a[1]-a[0]),S[I]=-a[0]*(r-e)/(a[1]-a[0])+e}else y[I]=1,C[I]=1,A[I]=0,S[I]=1;I++}}t.componentSSBO.addEntry("cScale","f32"),t.componentSSBO.addEntry("cShift","f32"),t.componentSSBO.addEntry("oScale","f32"),t.componentSSBO.addEntry("oShift","f32"),t.componentSSBO.addEntry("goShift","f32"),t.componentSSBO.addEntry("goScale","f32"),t.componentSSBO.addEntry("gomin","f32"),t.componentSSBO.addEntry("gomax","f32"),t.componentSSBO.setAllInstancesFromArray("cScale",d),t.componentSSBO.setAllInstancesFromArray("cShift",p),t.componentSSBO.setAllInstancesFromArray("oScale",f),t.componentSSBO.setAllInstancesFromArray("oShift",v),t.componentSSBO.setAllInstancesFromArray("goScale",A),t.componentSSBO.setAllInstancesFromArray("goShift",S),t.componentSSBO.setAllInstancesFromArray("gomin",y),t.componentSSBO.setAllInstancesFromArray("gomax",C),t.componentSSBO.send(n)};const n=e.updateBuffers;e.updateBuffers=()=>{n();let r=t.volumes[0].getRenderable().getMapper().getSampleDistance();for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getSampleDistance();n<r&&(r=n)}t.sampleDist!==r&&(t.sampleDist=r,t.UBO.setValue("SampleDistance",r),t.UBO.sendIfNeeded(t.device));for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getInputData(),r=t.device.getTextureManager().getTextureForImageData(n);if(!t.textureViews[e+4]||t.textureViews[e+4].getTexture()!==r){const n=r.createView(`volTexture${e}`);t.textureViews[e+4]=n}}if(t.volumes.length<t.lastVolumeLength)for(let e=t.volumes.length;e<t.lastVolumeLength;e++)t.textureViews.pop();t.lastVolumeLength=t.volumes.length,e.updateLUTImage(t.device),e.updateSSBO(t.device),t.clampSampler||(t.clampSampler=gb.newInstance({label:"clampSampler"}),t.clampSampler.create(t.device,{minFilter:"linear",magFilter:"linear"}))},e.computePipelineHash=()=>{t.pipelineHash="volfsq";for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getMapper().getBlendMode();t.pipelineHash+=`${n}`}},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()};const r=e.getBindables;e.getBindables=()=>{const e=r();return e.push(t.componentSSBO),e.push(t.clampSampler),e}}const qb={volumes:null,rowLength:1024,lastVolumeLength:0};function Xb(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,qb,n),RT.extend(e,t,n),t.fragmentShaderTemplate=Hb,t.UBO=_b.newInstance({label:"mapperUBO"}),t.UBO.addEntry("SampleDistance","f32"),t.SSBO=Wb.newInstance({label:"volumeSSBO"}),t.componentSSBO=Wb.newInstance({label:"componentSSBO"}),t.lutBuildTime={},jt.obj(t.lutBuildTime,{mtime:0}),$b(e,t)}var Yb={newInstance:jt.newInstance(Xb,"vtkWebGPUVolumePassFSQ"),extend:Xb};const{Representation:Zb}=ds,{BufferUsage:Qb,PrimitiveTypes:Jb}=ub,ex=[[0,4,6],[0,6,2],[1,3,7],[1,7,5],[0,5,4],[0,1,5],[2,6,7],[2,7,3],[0,3,1],[0,2,3],[4,5,7],[4,7,6]],tx="\n//VTK::Renderer::Dec\n\n//VTK::Select::Dec\n\n//VTK::VolumePass::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output : fragmentOutput;\n\n  //VTK::Select::Impl\n\n  //VTK::TCoord::Impl\n\n  //VTK::VolumePass::Impl\n\n  // use the maximum (closest) of the current value and the zbuffer\n  // the blend func will then take the min to find the farthest stop value\n  var stopval: f32 = max(input.fragPos.z, textureLoad(opaquePassDepthTexture, vec2<i32>(i32(input.fragPos.x), i32(input.fragPos.y)), 0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n";function nx(e,t){t.classHierarchy.push("vtkWebGPUVolumePass"),e.initialize=n=>{t._clearEncoder||e.createClearEncoder(n),t._mergeEncoder||e.createMergeEncoder(n),t._copyEncoder||e.createCopyEncoder(n),t._depthRangeEncoder||e.createDepthRangeEncoder(n),t.fullScreenQuad||(t.fullScreenQuad=Yb.newInstance(),t.fullScreenQuad.setDevice(n.getDevice()),t.fullScreenQuad.setTextureViews([...t._depthRangeEncoder.getColorTextureViews()])),t._volumeCopyQuad||(t._volumeCopyQuad=RT.newInstance(),t._volumeCopyQuad.setPipelineHash("volpassfsq"),t._volumeCopyQuad.setDevice(n.getDevice()),t._volumeCopyQuad.setFragmentShaderTemplate("\n//VTK::Renderer::Dec\n\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = textureSample(volumePassColorTexture,\n    volumePassColorTextureSampler, mapperUBO.tscale*input.tcoordVS);\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._copyUBO=_b.newInstance({label:"mapperUBO"}),t._copyUBO.addEntry("tscale","vec2<f32>"),t._volumeCopyQuad.setUBO(t._copyUBO),t._volumeCopyQuad.setTextureViews([t._colorTextureView]))},e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=r,e.initialize(r),e.computeTiming(r),e.renderDepthBounds(n,r),t._firstGroup=!0;const o=r.getDevice(),a=o.getHandle().limits.maxSampledTexturesPerShaderStage-4;if(t.volumes.length>a){const o=n.getRenderable().getActiveCamera().getPosition(),i=[];for(let e=0;e<t.volumes.length;e++){const n=t.volumes[e].getRenderable().getBounds(),r=[.5*(n[1]+n[0]),.5*(n[3]+n[2]),.5*(n[5]+n[4])];i[e]=Yo(r,o)}const s=[...Array(t.volumes.length).keys()];s.sort(((e,t)=>i[t]-i[e]));let l=[],c=s.length%a;for(let o=0;o<s.length;o++)l.push(t.volumes[s[o]]),l.length>=c&&(e.rayCastPass(r,n,l),l=[],c=a,t._firstGroup=!1)}else e.rayCastPass(r,n,t.volumes);if(t._volumeCopyQuad.setWebGPURenderer(n),t._useSmallViewport){const e=t._colorTextureView.getTexture().getWidth(),n=t._colorTextureView.getTexture().getHeight();t._copyUBO.setArray("tscale",[t._smallViewportWidth/e,t._smallViewportHeight/n])}else t._copyUBO.setArray("tscale",[1,1]);t._copyUBO.sendIfNeeded(o),t._copyEncoder.setColorTextureView(0,t.colorTextureView),t._copyEncoder.attachTextureViews(),t._copyEncoder.begin(r.getCommandEncoder()),n.scissorAndViewport(t._copyEncoder),t._volumeCopyQuad.prepareAndDraw(t._copyEncoder),t._copyEncoder.end()},e.delete=jt.chain((()=>{t._animationRateSubscription&&(t._animationRateSubscription.unsubscribe(),t._animationRateSubscription=null)}),e.delete),e.computeTiming=e=>{const n=e.getRenderable().getInteractor();if(null==t._lastScale){const e=t.volumes[0].getRenderable().getMapper();t._lastScale=e.getInitialInteractionScale()||1}t._useSmallViewport=!1,n.isAnimating()&&t._lastScale>1.5&&(t._useSmallViewport=!0),t._colorTexture.resize(e.getCanvas().width,e.getCanvas().height),t._animationRateSubscription||(t._animationRateSubscription=n.onAnimationFrameRateUpdate((()=>{const e=t.volumes[0].getRenderable().getMapper();if(e.getAutoAdjustSampleDistances()){const e=n.getRecentAnimationFrameRate(),r=t._lastScale*n.getDesiredUpdateRate()/e;t._lastScale=r,t._lastScale>400&&(t._lastScale=400)}else t._lastScale=e.getImageSampleDistance()*e.getImageSampleDistance();t._lastScale<1.5&&(t._lastScale=1.5)})))},e.rayCastPass=(e,n,r)=>{const o=t._firstGroup?t._clearEncoder:t._mergeEncoder;o.attachTextureViews(),o.begin(e.getCommandEncoder());let a=t._colorTextureView.getTexture().getWidth(),i=t._colorTextureView.getTexture().getHeight();if(t._useSmallViewport){const n=e.getCanvas(),r=1/Math.sqrt(t._lastScale);t._smallViewportWidth=Math.ceil(r*n.width),t._smallViewportHeight=Math.ceil(r*n.height),a=t._smallViewportWidth,i=t._smallViewportHeight}o.getHandle().setViewport(0,0,a,i,0,1),o.getHandle().setScissorRect(0,0,a,i),t.fullScreenQuad.setWebGPURenderer(n),t.fullScreenQuad.setVolumes(r),t.fullScreenQuad.prepareAndDraw(o),o.end()},e.renderDepthBounds=(n,r)=>{e.updateDepthPolyData(n);const o=t._boundsPoly,a=o.getPoints(),i=o.getPolys();let s={hash:`vp${i.getMTime()}`,usage:Qb.Index,cells:i,numberOfPoints:a.getNumberOfPoints(),primitiveType:Jb.Triangles,representation:Zb.SURFACE};const l=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().setIndexBuffer(l),s={usage:Qb.PointArray,format:"float32x4",hash:`vp${a.getMTime()}${i.getMTime()}`,dataArray:a,indexBuffer:l,packExtra:!0};const c=r.getDevice().getBufferManager().getBuffer(s);t._mapper.getVertexInput().addBuffer(c,["vertexBC"]),t._mapper.setNumberOfVertices(c.getSizeInBytes()/c.getStrideInBytes()),e.drawDepthRange(n,r)},e.updateDepthPolyData=e=>{let n=!1;for(let e=0;e<t.volumes.length;e++){const r=t.volumes[e].getMTime();t._lastMTimes[e]&&r===t._lastMTimes[e]||(n=!0,t._lastMTimes[e]=r)}const r=e.getStabilizedTime();if((t._lastMTimes.length<=t.volumes.length||r!==t._lastMTimes[t.volumes.length])&&(n=!0,t._lastMTimes[t.volumes.length]=r),!n)return;const o=e.getStabilizedCenterByReference(),a=8*t.volumes.length,i=new Float64Array(3*a),s=12*t.volumes.length,l=new Uint16Array(4*s);for(let e=0;e<t.volumes.length;e++){t.volumes[e].getBoundingCubePoints(i,24*e);let n=12*e*4;const r=8*e;for(let e=0;e<12;e++)l[n++]=3,l[n++]=r+ex[e][0],l[n++]=r+ex[e][1],l[n++]=r+ex[e][2]}for(let e=0;e<i.length;e+=3)i[e]-=o[0],i[e+1]-=o[1],i[e+2]-=o[2];t._boundsPoly.getPoints().setData(i,3),t._boundsPoly.getPoints().modified(),t._boundsPoly.getPolys().setData(l,1),t._boundsPoly.getPolys().modified(),t._boundsPoly.modified()},e.drawDepthRange=(n,r)=>{t._depthRangeTexture.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeTexture2.resizeToMatch(t.colorTextureView.getTexture()),t._depthRangeEncoder.attachTextureViews(),e.setCurrentOperation("volumeDepthRangePass"),n.setRenderEncoder(t._depthRangeEncoder),n.volumeDepthRangePass(!0),t._mapper.setWebGPURenderer(n),t._mapper.prepareToDraw(t._depthRangeEncoder),t._mapper.registerDrawCallback(t._depthRangeEncoder),n.volumeDepthRangePass(!1)},e.createDepthRangeEncoder=e=>{const n=e.getDevice();t._depthRangeEncoder=LT.newInstance({label:"VolumePass DepthRange"}),t._depthRangeEncoder.setPipelineHash("volr"),t._depthRangeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor1"),t.addOutput("vec4<f32>","outColor2");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor1 = vec4<f32>(input.fragPos.z, 0.0, 0.0, 0.0);","output.outColor2 = vec4<f32>(stopval, 0.0, 0.0, 0.0);"]).result,t.setCode(n)})),t._depthRangeEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"},{view:null,clearValue:[1,1,1,1],loadOp:"clear",storeOp:"store"}]}),t._depthRangeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"max"},alpha:{srcfactor:"one",dstFactor:"one",operation:"max"}}},{format:"r16float",blend:{color:{srcFactor:"one",dstFactor:"one",operation:"min"},alpha:{srcfactor:"one",dstFactor:"one",operation:"min"}}}]}}),t._depthRangeTexture=Sb.newInstance({label:"volumePassMaxDepth"}),t._depthRangeTexture.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const r=t._depthRangeTexture.createView("maxTexture");t._depthRangeEncoder.setColorTextureView(0,r),t._depthRangeTexture2=Sb.newInstance({label:"volumePassDepthMin"}),t._depthRangeTexture2.create(n,{width:e.getCanvas().width,height:e.getCanvas().height,format:"r16float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING});const o=t._depthRangeTexture2.createView("minTexture");t._depthRangeEncoder.setColorTextureView(1,o),t._mapper.setDevice(e.getDevice()),t._mapper.setTextureViews([t.depthTextureView])},e.createClearEncoder=e=>{t._colorTexture=Sb.newInstance({label:"volumePassColor"}),t._colorTexture.create(e.getDevice(),{width:e.getCanvas().width,height:e.getCanvas().height,format:"bgra8unorm",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_SRC}),t._colorTextureView=t._colorTexture.createView("volumePassColorTexture"),t._colorTextureView.addSampler(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._clearEncoder=LT.newInstance({label:"VolumePass Clear"}),t._clearEncoder.setColorTextureView(0,t._colorTextureView),t._clearEncoder.setDescription({colorAttachments:[{view:null,clearValue:[0,0,0,0],loadOp:"clear",storeOp:"store"}]}),t._clearEncoder.setPipelineHash("volpf"),t._clearEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createCopyEncoder=e=>{t._copyEncoder=LT.newInstance({label:"volumePassCopy"}),t._copyEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._copyEncoder.setPipelineHash("volcopypf"),t._copyEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"rgba16float",blend:{color:{srcFactor:"one",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.createMergeEncoder=e=>{t._mergeEncoder=LT.newInstance({label:"volumePassMerge"}),t._mergeEncoder.setColorTextureView(0,t._colorTextureView),t._mergeEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._mergeEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<f32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<f32>(computedColor.rgb, computedColor.a);"]).result,t.setCode(n)})),t._mergeEncoder.setPipelineHash("volpf"),t._mergeEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:"bgra8unorm",blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}})},e.setVolumes=n=>{if(!t.volumes||t.volumes.length!==n.length)return t.volumes=[...n],void e.modified();for(let r=0;r<n.length;r++)if(n[r]!==t.volumes[r])return t.volumes=[...n],void e.modified()}}const rx={colorTextureView:null,depthTextureView:null,volumes:null};function ox(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,rx,n),ry.extend(e,t,n),t._mapper=IT.newInstance(),t._mapper.setFragmentShaderTemplate(tx),t._mapper.getShaderReplacements().set("replaceShaderVolumePass",((e,t,n)=>{t.getShaderDescription("fragment").addBuiltinInput("vec4<f32>","@builtin(position) fragPos")})),t._boundsPoly=yu.newInstance(),t._lastMTimes=[],jt.setGet(e,t,["colorTextureView","depthTextureView"]),nx(e,t)}var ax={newInstance:jt.newInstance(ox,"vtkWebGPUVolumePass"),extend:ox};function ix(e,t){t.classHierarchy.push("vtkForwardPass"),e.traverse=function(n){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(t.deleted)return;t._currentParent=r,e.setCurrentOperation("buildPass"),n.traverse(e),t.opaquePass||(t.opaquePass=Pb.newInstance());const o=n.getRenderable().getNumberOfLayers(),a=n.getChildren();for(let r=0;r<o;r++)for(let o=0;o<a.length;o++){const i=a[o],s=n.getRenderable().getRenderers()[o];s.getDraw()&&s.getLayer()===r&&(t.opaqueActorCount=0,t.translucentActorCount=0,t.volumes=[],e.setCurrentOperation("queryPass"),i.traverse(e),e.setCurrentOperation("cameraPass"),i.traverse(e),t.opaquePass.traverse(i,n),t.translucentActorCount>0&&(t.translucentPass||(t.translucentPass=Eb.newInstance()),t.translucentPass.setColorTextureView(t.opaquePass.getColorTextureView()),t.translucentPass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.translucentPass.traverse(i,n)),t.volumes.length>0&&(t.volumePass||(t.volumePass=ax.newInstance()),t.volumePass.setColorTextureView(t.opaquePass.getColorTextureView()),t.volumePass.setDepthTextureView(t.opaquePass.getDepthTextureView()),t.volumePass.setVolumes(t.volumes),t.volumePass.traverse(i,n)),e.finalPass(n,i))}},e.finalPass=(n,r)=>{t._finalBlitEncoder||e.createFinalBlitEncoder(n),t._finalBlitOutputTextureView.createFromTextureHandle(n.getCurrentTexture(),{depth:1,format:n.getPresentationFormat()}),t._finalBlitEncoder.attachTextureViews(),t._finalBlitEncoder.begin(n.getCommandEncoder()),r.scissorAndViewport(t._finalBlitEncoder),t._fullScreenQuad.prepareAndDraw(t._finalBlitEncoder),t._finalBlitEncoder.end()},e.createFinalBlitEncoder=e=>{t._finalBlitEncoder=LT.newInstance({label:"forwardPassBlit"}),t._finalBlitEncoder.setDescription({colorAttachments:[{view:null,loadOp:"load",storeOp:"store"}]}),t._finalBlitEncoder.setPipelineHash("fpf"),t._finalBlitEncoder.setPipelineSettings({primitive:{cullMode:"none"},fragment:{targets:[{format:e.getPresentationFormat(),blend:{color:{srcFactor:"src-alpha",dstFactor:"one-minus-src-alpha"},alpha:{srcfactor:"one",dstFactor:"one-minus-src-alpha"}}}]}}),t._fsqSampler=gb.newInstance({label:"finalPassSampler"}),t._fsqSampler.create(e.getDevice(),{minFilter:"linear",magFilter:"linear"}),t._fullScreenQuad=RT.newInstance(),t._fullScreenQuad.setDevice(e.getDevice()),t._fullScreenQuad.setPipelineHash("fpfsq"),t._fullScreenQuad.setTextureViews([t.opaquePass.getColorTextureView()]),t._fullScreenQuad.setAdditionalBindables([t._fsqSampler]),t._fullScreenQuad.setFragmentShaderTemplate("\n//VTK::Mapper::Dec\n\n//VTK::TCoord::Dec\n\n//VTK::RenderEncoder::Dec\n\n//VTK::IOStructs::Dec\n\n@fragment\nfn main(\n//VTK::IOStructs::Input\n)\n//VTK::IOStructs::Output\n{\n  var output: fragmentOutput;\n\n  var computedColor: vec4<f32> = clamp(textureSampleLevel(opaquePassColorTexture, finalPassSampler, input.tcoordVS, 0.0),vec4<f32>(0.0),vec4<f32>(1.0));\n\n  //VTK::RenderEncoder::Impl\n  return output;\n}\n"),t._finalBlitOutputTextureView=yb.newInstance(),t._finalBlitEncoder.setColorTextureView(0,t._finalBlitOutputTextureView)},e.incrementOpaqueActorCount=()=>t.opaqueActorCount++,e.incrementTranslucentActorCount=()=>t.translucentActorCount++,e.addVolume=e=>{t.volumes.push(e)}}const sx={opaqueActorCount:0,translucentActorCount:0,volumes:null,opaqueRenderEncoder:null,translucentPass:null,volumePass:null};function lx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,sx,n),ry.extend(e,t,n),jt.setGet(e,t,["opaquePass","translucentPass","volumePass"]),ix(e,t)}var cx={newInstance:jt.newInstance(lx,"vtkForwardPass"),extend:lx};const{VtkDataTypes:ux}=Ds;function dx(e,t){function n(e){if(e.imageData){e.dataArray=e.imageData.getPointData().getScalars(),e.time=e.dataArray.getMTime(),e.nativeArray=e.dataArray.getData();const t=e.imageData.getDimensions();switch(e.width=t[0],e.height=t[1],e.depth=t[2],e.dataArray.getNumberOfComponents()){case 1:e.format="r";break;case 2:e.format="rg";break;default:e.format="rgba"}switch(e.dataArray.getDataType()){case ux.UNSIGNED_CHAR:e.format+="8unorm";break;case ux.FLOAT:case ux.UNSIGNED_INT:case ux.INT:case ux.DOUBLE:case ux.UNSIGNED_SHORT:case ux.SHORT:default:e.format+="16float"}}e.image&&(e.width=e.image.width,e.height=e.image.height,e.depth=1,e.format="rgba8unorm"),e.jsImageData&&(e.width=e.jsImageData.width,e.height=e.jsImageData.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.nativeArray=e.jsImageData.data),e.canvas&&(e.width=e.canvas.width,e.height=e.canvas.height,e.depth=1,e.format="rgba8unorm",e.flip=!0,e.usage=GPUTextureUsage.TEXTURE_BINDING|GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT)}function r(e){const n=Sb.newInstance();return n.create(t.device,{width:e.width,height:e.height,depth:e.depth,format:e.format,usage:e.usage,mipLevel:e.mipLevel}),(e.nativeArray||e.image||e.canvas)&&n.writeImageData(e),n}t.classHierarchy.push("vtkWebGPUTextureManager"),e.getTexture=e=>e.hash?t.device.getCachedObject(e.hash,r,e):r(e),e.getTextureForImageData=e=>{const r={time:e.getMTime()};return r.imageData=e,n(r),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)},e.getTextureForVTKTexture=e=>{const r={time:e.getMTime()};return e.getInputData()?r.imageData=e.getInputData():e.getImage()?r.image=e.getImage():e.getJsImageData()?r.jsImageData=e.getJsImageData():e.getCanvas()&&(r.canvas=e.getCanvas()),n(r),r.mipLevel=e.getMipLevel(),r.hash=r.time+r.format+r.mipLevel,t.device.getTextureManager().getTexture(r)}}const px={handle:null,device:null};function fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,px,n),jt.obj(e,t),jt.setGet(e,t,["device"]),dx(e,t)}var gx={newInstance:jt.newInstance(fx),extend:fx};class mx extends Map{constructor(){super(),this.registry=new FinalizationRegistry((e=>{const t=super.get(e);t&&t.deref&&void 0===t.deref()&&super.delete(e)}))}getValue(e){const t=super.get(e);if(t){const n=t.deref();if(void 0!==n)return n;super.delete(e)}}setValue(e,t){let n;return t&&"object"==typeof t&&(n=new WeakRef(t),this.registry.register(t,e),super.set(e,n)),n}}function hx(e,t){t.classHierarchy.push("vtkWebGPUDevice"),e.initialize=e=>{t.handle=e},e.createCommandEncoder=()=>t.handle.createCommandEncoder(),e.submitCommandEncoder=e=>{t.handle.queue.submit([e.finish()])},e.getShaderModule=e=>t.shaderCache.getShaderModule(e),e.getBindGroupLayout=e=>{if(!e.entries)return null;for(let t=0;t<e.entries.length;t++){const n=e.entries[t];n.binding=n.binding||0,n.visibility=n.visibility||GPUShaderStage.VERTEX|GPUShaderStage.FRAGMENT}const n=JSON.stringify(e);for(let e=0;e<t.bindGroupLayouts.length;e++)if(t.bindGroupLayouts[e].sval===n)return t.bindGroupLayouts[e].layout;const r=t.handle.createBindGroupLayout(e);return t.bindGroupLayouts.push({sval:n,layout:r}),r},e.getBindGroupLayoutDescription=e=>{for(let n=0;n<t.bindGroupLayouts.length;n++)if(t.bindGroupLayouts[n].layout===e)return t.bindGroupLayouts[n].sval;return vtkErrorMacro("layout not found"),console.trace(),null},e.getPipeline=e=>e in t.pipelines?t.pipelines[e]:null,e.createPipeline=(n,r)=>{r.initialize(e,n),t.pipelines[n]=r},e.onSubmittedWorkDone=()=>t.handle.queue.onSubmittedWorkDone(),e.hasCachedObject=e=>t.objectCache.getValue(e),e.getCachedObject=function(e,n){if(!e)return vtkErrorMacro("attempt to cache an object without a hash"),null;const r=t.objectCache.getValue(e);if(r)return r;for(var o=arguments.length,a=new Array(o>2?o-2:0),i=2;i<o;i++)a[i-2]=arguments[i];const s=n(...a);return t.objectCache.setValue(e,s),s}}const vx={handle:null,pipelines:null,shaderCache:null,bindGroupLayouts:null,bufferManager:null,textureManager:null};function yx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vx,n),yt(e,t),At(e,t,["handle"]),bt(e,t,["bufferManager","shaderCache","textureManager"]),t.objectCache=new mx,t.shaderCache=Xy.newInstance(),t.shaderCache.setDevice(e),t.bindGroupLayouts=[],t.bufferManager=ub.newInstance(),t.bufferManager.setDevice(e),t.textureManager=gx.newInstance(),t.textureManager.setDevice(e),t.pipelines={},hx(e,t)}var Tx={newInstance:Dt(yx,"vtkWebGPUDevice"),extend:yx};function bx(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelectionPass"),e.traverse=(n,r)=>{if(t.deleted)return;t._currentParent=null,e.setCurrentOperation("buildPass"),n.traverse(e);const o=n.getDevice();if(t.selectionRenderEncoder)t.colorTexture.resize(n.getCanvas().width,n.getCanvas().height),t.depthTexture.resizeToMatch(t.colorTexture);else{e.createRenderEncoder(),t.colorTexture=Sb.newInstance({label:"hardwareSelectorColor"}),t.colorTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"rgba32uint",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const r=t.colorTexture.createView("hardwareSelectColorTexture");t.selectionRenderEncoder.setColorTextureView(0,r),t.depthTexture=Sb.newInstance({label:"hardwareSelectorDepth"}),t.depthTexture.create(o,{width:n.getCanvas().width,height:n.getCanvas().height,format:"depth32float",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_SRC});const a=t.depthTexture.createView("hardwareSelectDepthTexture");t.selectionRenderEncoder.setDepthTextureView(a)}t.selectionRenderEncoder.attachTextureViews(),r.setRenderEncoder(t.selectionRenderEncoder),e.setCurrentOperation("cameraPass"),r.traverse(e),e.setCurrentOperation("opaquePass"),r.traverse(e)},e.createRenderEncoder=()=>{t.selectionRenderEncoder=LT.newInstance({label:"HardwareSelectionPass"}),t.selectionRenderEncoder.setPipelineHash("sel"),t.selectionRenderEncoder.setReplaceShaderCodeFunction((e=>{const t=e.getShaderDescription("fragment");t.addOutput("vec4<u32>","outColor");let n=t.getCode();n=Xy.substitute(n,"//VTK::RenderEncoder::Impl",["output.outColor = vec4<u32>(mapperUBO.PropID, compositeID, 0u, 0u);"]).result,t.setCode(n)})),t.selectionRenderEncoder.getDescription().colorAttachments[0].clearValue=[0,0,0,0],t.selectionRenderEncoder.setPipelineSettings({primitive:{cullMode:"none"},depthStencil:{depthWriteEnabled:!0,depthCompare:"greater",format:"depth32float"},fragment:{targets:[{format:"rgba32uint",blend:void 0}]}})}}const xx={selectionRenderEncoder:null,colorTexture:null,depthTexture:null};function Cx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xx,n),ry.extend(e,t,n),jt.get(e,t,["colorTexture","depthTexture"]),bx(e,t)}var Sx={newInstance:jt.newInstance(Cx,"vtkWebGPUHardwareSelectionPass"),extend:Cx};const{SelectionContent:Ax,SelectionField:Ix}=Jp,{FieldAssociations:wx}=ol,{vtkErrorMacro:Px}=jt;function Ox(e){return`${e.propID} ${e.compositeID}`}function Rx(e,t,n,r){const o=4*((n.height-t-1)*n.colorBufferWidth+e)+r;return n.colorValues[o]}function Mx(e,t,n,r){const o=n<0?0:n;if(0===o){if(r[0]=t[0],r[1]=t[1],t[0]<0||t[0]>=e.width||t[1]<0||t[1]>=e.height)return null;const n=Rx(t[0],t[1],e,0);if(n<=0)return null;const o={};o.propID=n;let a=Rx(t[0],t[1],e,1);if((a<0||a>16777215)&&(a=0),o.compositeID=a,e.captureZValues){const n=(e.height-t[1]-1)*e.zbufferBufferWidth+t[0];o.zValue=e.depthValues[n],o.zValue=e.webGPURenderer.convertToOpenGLDepth(o.zValue),o.displayPosition=t}return o}const a=[t[0],t[1]],i=[0,0];let s=Mx(e,t,0,r);if(s)return s;for(let t=1;t<o;++t){for(let n=a[1]>t?a[1]-t:0;n<=a[1]+t;++n){if(i[1]=n,a[0]>=t&&(i[0]=a[0]-t,s=Mx(e,i,0,r),s))return s;if(i[0]=a[0]+t,s=Mx(e,i,0,r),s)return s}for(let n=a[0]>=t?a[0]-(t-1):0;n<=a[0]+(t-1);++n){if(i[0]=n,a[1]>=t&&(i[1]=a[1]-t,s=Mx(e,i,0,r),s))return s;if(i[1]=a[1]+t,s=Mx(e,i,0,r),s)return s}}return r[0]=t[0],r[1]=t[1],null}function Ex(e,t){t.classHierarchy.push("vtkWebGPUHardwareSelector"),e.endSelection=()=>{t.WebGPURenderer.setSelector(null)},e.getSourceDataAsync=async e=>{if(!e||!t._WebGPURenderWindow)return Px("Renderer and view must be set before calling Select."),!1;t._WebGPURenderWindow.getRenderable().preRender(),t._WebGPURenderWindow.getInitialized()||(t._WebGPURenderWindow.initialize(),await new Promise((e=>{t._WebGPURenderWindow.onInitialized(e)})));const n=t._WebGPURenderWindow.getViewNodeFor(e);if(!n)return!1;const r=n.getSuppressClear();n.setSuppressClear(!0),t._selectionPass.traverse(t._WebGPURenderWindow,n),n.setSuppressClear(r);const o=t._WebGPURenderWindow.getDevice(),a=t._selectionPass.getColorTexture(),i=t._selectionPass.getDepthTexture(),s={area:[0,0,a.getWidth()-1,a.getHeight()-1],captureZValues:t.captureZValues,fieldAssociation:t.fieldAssociation,renderer:e,webGPURenderer:n,webGPURenderWindow:t._WebGPURenderWindow,width:a.getWidth(),height:a.getHeight()};s.colorBufferWidth=16*Math.floor((s.width+15)/16),s.colorBufferSizeInBytes=s.colorBufferWidth*s.height*4*4;const l=GT.newInstance({label:"hardwareSelectColorBuffer"});l.setDevice(o),l.create(s.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const c=t._WebGPURenderWindow.getCommandEncoder();let u;c.copyTextureToBuffer({texture:a.getHandle()},{buffer:l.getHandle(),bytesPerRow:16*s.colorBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1}),t.captureZValues&&(s.zbufferBufferWidth=64*Math.floor((s.width+63)/64),u=GT.newInstance({label:"hardwareSelectDepthBuffer"}),u.setDevice(o),s.zbufferSizeInBytes=s.height*s.zbufferBufferWidth*4,u.create(s.zbufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),c.copyTextureToBuffer({texture:i.getHandle(),aspect:"depth-only"},{buffer:u.getHandle(),bytesPerRow:4*s.zbufferBufferWidth,rowsPerImage:s.height},{width:s.width,height:s.height,depthOrArrayLayers:1})),o.submitCommandEncoder(c);const d=l.mapAsync(GPUMapMode.READ);if(t.captureZValues){const e=u.mapAsync(GPUMapMode.READ);await Promise.all([d,e]),s.depthValues=new Float32Array(u.getMappedRange().slice()),u.unmap()}else await d;return s.colorValues=new Uint32Array(l.getMappedRange().slice()),l.unmap(),s.generateSelection=(e,t,n,r)=>function(e,t,n,r,o){const a=Math.floor(t),i=Math.floor(n),s=Math.floor(r),l=Math.floor(o),c=new Map,u=[0,0];for(let t=i;t<=l;t++)for(let n=a;n<=s;n++){const r=Mx(e,[n,t],0,u);if(r){const t=Ox(r);if(c.has(t)){const n=c.get(t);n.pixelCount++,e.captureZValues&&r.zValue<n.info.zValue&&(n.info=r),-1===n.attributeIDs.indexOf(r.attributeID)&&n.attributeIDs.push(r.attributeID)}else c.set(t,{info:r,pixelCount:1,attributeIDs:[r.attributeID]})}}return function(e,t,n){const r=[];let o=0;return t.forEach(((t,a)=>{const i=Jp.newInstance();switch(i.setContentType(Ax.INDICES),e){case wx.FIELD_ASSOCIATION_CELLS:i.setFieldType(Ix.CELL);break;case wx.FIELD_ASSOCIATION_POINTS:i.setFieldType(Ix.POINT);break;default:Px("Unknown field association")}i.getProperties().propID=t.info.propID;const s=n.webGPURenderer.getPropFromID(t.info.propID);i.getProperties().prop=s.getRenderable(),i.getProperties().compositeID=t.info.compositeID,i.getProperties().pixelCount=t.pixelCount,n.captureZValues&&(i.getProperties().displayPosition=[t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue],i.getProperties().worldPosition=n.webGPURenderWindow.displayToWorld(t.info.displayPosition[0],t.info.displayPosition[1],t.info.zValue,n.renderer)),i.setSelectionList(t.attributeIDs),r[o]=i,o++})),r}(e.fieldAssociation,c,e)}(s,e,t,n,r),s}}const Dx={};function Vx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Dx,n),jp.extend(e,t,n),t._selectionPass=Sx.newInstance(),jt.setGet(e,t,["_WebGPURenderWindow"]),jt.moveToProtected(e,t,["WebGPURenderWindow"]),Ex(e,t)}var Lx={newInstance:jt.newInstance(Vx,"vtkWebGPUHardwareSelector"),extend:Vx};const Bx=Object.create(null);function Nx(e,t){t.classHierarchy.push("vtkWebGPUViewNodeFactory")}const _x={};function Fx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,_x,n),t.overrides=Bx,tn.extend(e,t,n),Nx(0,t)}var kx={newInstance:jt.newInstance(Fx,"vtkWebGPUViewNodeFactory"),extend:Fx};const{vtkErrorMacro:Gx}=jt,Ux={position:"absolute",top:0,left:0,width:"100%",height:"100%"};function zx(e,t){t.classHierarchy.push("vtkWebGPURenderWindow"),e.getViewNodeFactory=()=>t.myFactory;const n=[0,0];e.onModified((function(){t.renderable&&(t.size[0]===n[0]&&t.size[1]===n[1]||(n[0]=t.size[0],n[1]=t.size[1],t.canvas.setAttribute("width",t.size[0]),t.canvas.setAttribute("height",t.size[1]),e.recreateSwapChain())),t.viewStream&&t.viewStream.setSize(t.size[0],t.size[1]),t.canvas.style.display=t.useOffScreen?"none":"block",t.el&&(t.el.style.cursor=t.cursorVisibility?t.cursor:"none"),t.containerSize=null})),e.recreateSwapChain=()=>{t.context&&(t.context.unconfigure(),t.presentationFormat=navigator.gpu.getPreferredCanvasFormat(t.adapter),t.context.configure({device:t.device.getHandle(),format:t.presentationFormat,alphaMode:"premultiplied",usage:GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.COPY_DST,width:t.size[0],height:t.size[1]}),t._configured=!0)},e.getCurrentTexture=()=>t.context.getCurrentTexture(),e.buildPass=n=>{if(n){if(!t.renderable)return;e.prepareNodes(),e.addMissingNodes(t.renderable.getRenderersByReference()),e.removeUnusedNodes(),e.initialize()}else t.initialized&&(t._configured||e.recreateSwapChain(),t.commandEncoder=t.device.createCommandEncoder())},e.initialize=()=>{if(!t.initializing){if(t.initializing=!0,!navigator.gpu)return void Gx("WebGPU is not enabled.");e.create3DContextAsync().then((()=>{t.initialized=!0,t.deleted||e.invokeInitialized()}))}},e.setContainer=n=>{t.el&&t.el!==n&&(t.canvas.parentNode!==t.el&&Gx("Error: canvas parent node does not match container"),t.el.removeChild(t.canvas),t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)),t.el!==n&&(t.el=n,t.el&&(t.el.appendChild(t.canvas),t.useBackgroundImage&&t.el.appendChild(t.bgImage)),e.modified())},e.getContainer=()=>t.el,e.getContainerSize=()=>{if(!t.containerSize&&t.el){const{width:e,height:n}=t.el.getBoundingClientRect();t.containerSize=[e,n]}return t.containerSize||t.size},e.getFramebufferSize=()=>t.size,e.create3DContextAsync=async()=>{t.adapter=await navigator.gpu.requestAdapter({powerPreference:"high-performance"}),t.deleted||(t.device=Tx.newInstance(),t.device.initialize(await t.adapter.requestDevice()),t.deleted?t.device=null:t.context=t.canvas.getContext("webgpu"))},e.releaseGraphicsResources=()=>{const n=ry.newInstance();n.setCurrentOperation("Release"),n.traverse(e,null),t.adapter=null,t.device=null,t.context=null,t.initialized=!1,t.initializing=!1},e.setBackgroundImage=e=>{t.bgImage.src=e.src},e.setUseBackgroundImage=e=>{t.useBackgroundImage=e,t.useBackgroundImage&&!t.el.contains(t.bgImage)?t.el.appendChild(t.bgImage):!t.useBackgroundImage&&t.el.contains(t.bgImage)&&t.el.removeChild(t.bgImage)},e.captureNextImage=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"image/png",{resetCamera:r=!1,size:o=null,scale:a=1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(t.deleted)return null;t.imageFormat=n;const i=t.notifyStartCaptureImage;return t.notifyStartCaptureImage=!0,t._screenshot={size:o||1!==a?o||t.size.map((e=>e*a)):null},new Promise(((n,o)=>{const a=e.onImageReady((o=>{if(null===t._screenshot.size)t.notifyStartCaptureImage=i,a.unsubscribe(),t._screenshot.placeHolder&&(t.size=t._screenshot.originalSize,e.modified(),t._screenshot.cameras&&t._screenshot.cameras.forEach((e=>{let{restoreParamsFn:t,arg:n}=e;return t(n)})),e.traverseAllPasses(),t.el.removeChild(t._screenshot.placeHolder),t._screenshot.placeHolder.remove(),t._screenshot=null),n(o);else{const n=document.createElement("img");if(n.style=Ux,n.src=o,t._screenshot.placeHolder=t.el.appendChild(n),t.canvas.style.display="none",t._screenshot.originalSize=t.size,t.size=t._screenshot.size,t._screenshot.size=null,e.modified(),r){const e=!0!==r;t._screenshot.cameras=t.renderable.getRenderers().map((t=>{const n=t.getActiveCamera(),o=n.get("focalPoint","position","parallelScale");return{resetCameraArgs:e?{renderer:t}:void 0,resetCameraFn:e?r:t.resetCamera,restoreParamsFn:n.set,arg:JSON.parse(JSON.stringify(o))}})),t._screenshot.cameras.forEach((e=>{let{resetCameraFn:t,resetCameraArgs:n}=e;return t(n)}))}e.traverseAllPasses()}}))}))},e.traverseAllPasses=()=>{if(!t.deleted)if(t.initialized){if(t.renderPasses)for(let n=0;n<t.renderPasses.length;++n)t.renderPasses[n].traverse(e,null);t.commandEncoder&&(t.device.submitCommandEncoder(t.commandEncoder),t.commandEncoder=null,t.notifyStartCaptureImage&&t.device.onSubmittedWorkDone().then((()=>{!async function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.imageFormat;const r=document.createElement("canvas"),o=r.getContext("2d");r.width=t.canvas.width,r.height=t.canvas.height;const a=await e.getPixelsAsync(),i=new ImageData(a.colorValues,a.width,a.height);o.putImageData(i,0,0);const s=t.canvas.getBoundingClientRect(),l=t.renderable;l.getRenderers().forEach((e=>{e.getViewProps().forEach((e=>{if(e.getContainer){const t=e.getContainer().getElementsByTagName("canvas");for(let e=0;e<t.length;e++){const n=t[e],r=n.getBoundingClientRect(),a=r.x-s.x,i=r.y-s.y;o.drawImage(n,a,i)}}}))}));const c=r.toDataURL(n);r.remove(),e.invokeImageReady(c)}()})))}else{e.initialize();const t=e.onInitialized((()=>{t.unsubscribe(),e.traverseAllPasses()}))}},e.setViewStream=n=>t.viewStream!==n&&(t.subscription&&(t.subscription.unsubscribe(),t.subscription=null),t.viewStream=n,t.viewStream&&(t.renderable.getRenderers()[0].getBackgroundByReference()[3]=0,e.setUseBackgroundImage(!0),t.subscription=t.viewStream.onImageReady((t=>e.setBackgroundImage(t.image))),t.viewStream.setSize(t.size[0],t.size[1]),t.viewStream.invalidateCache(),t.viewStream.render(),e.modified()),!0),e.getUniquePropID=()=>t.nextPropID++,e.getPropFromID=e=>{for(let n=0;n<t.children.length;n++){const r=t.children[n].getPropFromID(e);if(null!==r)return r}return null},e.getPixelsAsync=async()=>{const e=t.device,n=t.renderPasses[0].getOpaquePass().getColorTexture(),r={width:n.getWidth(),height:n.getHeight()};r.colorBufferWidth=32*Math.floor((r.width+31)/32),r.colorBufferSizeInBytes=r.colorBufferWidth*r.height*8;const o=GT.newInstance();o.setDevice(e),o.create(r.colorBufferSizeInBytes,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);const a=t.device.createCommandEncoder();a.copyTextureToBuffer({texture:n.getHandle()},{buffer:o.getHandle(),bytesPerRow:8*r.colorBufferWidth,rowsPerImage:r.height},{width:r.width,height:r.height,depthOrArrayLayers:1}),e.submitCommandEncoder(a);const i=o.mapAsync(GPUMapMode.READ);await i,r.colorValues=new Uint16Array(o.getMappedRange().slice()),o.unmap();const s=new Uint8ClampedArray(r.height*r.width*4);for(let e=0;e<r.height;e++)for(let t=0;t<r.width;t++){const n=4*(e*r.width+t),o=4*(e*r.colorBufferWidth+t);s[n]=255*Rd.fromHalf(r.colorValues[o]),s[n+1]=255*Rd.fromHalf(r.colorValues[o+1]),s[n+2]=255*Rd.fromHalf(r.colorValues[o+2]),s[n+3]=255*Rd.fromHalf(r.colorValues[o+3])}return r.colorValues=s,r},e.createSelector=()=>{const t=Lx.newInstance();return t.setWebGPURenderWindow(e),t};const r=e.setSize;e.setSize=(t,n)=>{const o=r(t,n);return o&&e.invokeWindowResizeEvent({width:t,height:n}),o},e.delete=jt.chain(e.delete,e.setViewStream)}const Wx={initialized:!1,context:null,adapter:null,device:null,canvas:null,cursorVisibility:!0,cursor:"pointer",containerSize:null,renderPasses:[],notifyStartCaptureImage:!1,imageFormat:"image/png",useOffScreen:!1,useBackgroundImage:!1,nextPropID:1,xrSupported:!1,presentationFormat:null};const Hx=jt.newInstance((function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Wx,n),t.canvas=document.createElement("canvas"),t.canvas.style.width="100%",t.bgImage=new Image,t.bgImage.style.position="absolute",t.bgImage.style.left="0",t.bgImage.style.top="0",t.bgImage.style.width="100%",t.bgImage.style.height="100%",t.bgImage.style.zIndex="-1",My.extend(e,t,n),t.myFactory=kx.newInstance(),t.renderPasses[0]=cx.newInstance(),t.selector||(t.selector=Lx.newInstance(),t.selector.setWebGPURenderWindow(e)),jt.event(e,t,"imageReady"),jt.event(e,t,"initialized"),jt.get(e,t,["commandEncoder","device","presentationFormat","useBackgroundImage","xrSupported"]),jt.setGet(e,t,["initialized","context","canvas","device","renderPasses","notifyStartCaptureImage","cursor","useOffScreen"]),jt.setGetArray(e,t,["size"],2),jt.event(e,t,"windowResizeEvent"),zx(e,t)}),"vtkWebGPURenderWindow");var jx;cv("WebGPU",Hx),jx=Hx,Bx.vtkRenderWindow=jx;const Kx=Jv(),$x={margin:"0",padding:"0",position:"absolute",top:"0",left:"0",width:"100%",height:"100%",overflow:"hidden"},qx={position:"absolute",left:"25px",top:"25px",backgroundColor:"white",borderRadius:"5px",listStyle:"none",padding:"5px 10px",margin:"0",display:"block",border:"solid 1px black",maxWidth:"calc(100% - 70px)",maxHeight:"calc(100% - 60px)",overflow:"auto"};function Xx(e,t){Object.keys(t).forEach((n=>{e.style[n]=t[n]}))}function Yx(e,t){t.classHierarchy.push("vtkFullScreenRenderWindow");const n=document.querySelector("body");function r(t){"c"===String.fromCharCode(t.charCode)&&e.toggleControllerVisibility()}t.rootContainer||(t.rootContainer=n),t.container||(t.container=document.createElement("div"),Xx(t.container,t.containerStyle||$x),t.rootContainer.appendChild(t.container)),t.rootContainer===n&&(document.documentElement.style.height="100%",n.style.height="100%",n.style.padding="0",n.style.margin="0"),t.renderWindow=gv.newInstance(),t.renderer=sv.newInstance(),t.renderWindow.addRenderer(t.renderer),t.apiSpecificRenderWindow=t.renderWindow.newAPISpecificView(Kx.viewAPI??t.defaultViewAPI),t.apiSpecificRenderWindow.setContainer(t.container),t.renderWindow.addView(t.apiSpecificRenderWindow),t.interactor=Dv.newInstance(),t.interactor.setInteractorStyle(Yv.newInstance()),t.interactor.setView(t.apiSpecificRenderWindow),t.interactor.initialize(),t.interactor.bindEvents(t.container),e.setBackground=t.renderer.setBackground,e.removeController=()=>{const e=t.controlContainer;e&&e.parentNode.removeChild(e)},e.setControllerVisibility=e=>{t.controllerVisibility=e,t.controlContainer&&(t.controlContainer.style.display=e?"block":"none")},e.toggleControllerVisibility=()=>{e.setControllerVisibility(!t.controllerVisibility)},e.addController=n=>{t.controlContainer=document.createElement("div"),Xx(t.controlContainer,t.controlPanelStyle||qx),t.rootContainer.appendChild(t.controlContainer),t.controlContainer.innerHTML=n,e.setControllerVisibility(t.controllerVisibility),t.rootContainer.addEventListener("keypress",r)},e.setBackground(...t.background),e.addRepresentation=e=>{e.getActors().forEach((e=>{t.renderer.addActor(e)}))},e.removeRepresentation=e=>{e.getActors().forEach((e=>t.renderer.removeActor(e)))},e.delete=jt.chain(e.setContainer,t.apiSpecificRenderWindow.delete,(()=>{t.rootContainer?.removeEventListener("keypress",r),window.removeEventListener("resize",e.resize)}),e.delete),e.resize=()=>{const e=t.container.getBoundingClientRect(),n=window.devicePixelRatio||1;t.apiSpecificRenderWindow.setSize(Math.floor(e.width*n),Math.floor(e.height*n)),t.resizeCallback&&t.resizeCallback(e),t.renderWindow.render()},e.setResizeCallback=n=>{t.resizeCallback=n,e.resize()},t.listenWindowResize&&window.addEventListener("resize",e.resize),e.resize()}const Zx={background:[.32,.34,.43],containerStyle:null,controlPanelStyle:null,listenWindowResize:!0,resizeCallback:null,controllerVisibility:!0};function Qx(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,Zx,n),jt.obj(e,t),jt.get(e,t,["renderWindow","renderer","apiSpecificRenderWindow","interactor","rootContainer","container","controlContainer"]),Yx(e,t)}var Jx={newInstance:jt.newInstance(Qx),extend:Qx},eC={ColorSpace:{RGB:0,HSV:1,LAB:2,DIVERGING:3},Scale:{LINEAR:0,LOG10:1}};const{ColorSpace:tC,Scale:nC}=eC,{ScalarMappingTarget:rC}=Pl,{vtkDebugMacro:oC,vtkErrorMacro:aC,vtkWarningMacro:iC}=jt;function sC(e,t){const n=e[0],r=e[1],o=e[2],a=Math.sqrt(n*n+r*r+o*o),i=a>.001?Math.acos(n/a):0,s=i>.001?Math.atan2(o,r):0;t[0]=a,t[1]=i,t[2]=s}function lC(e,t){if(e[0]>=t-.1)return e[2];const n=e[1]*Math.sqrt(t*t-e[0]*e[0])/(e[0]*Math.sin(e[1]));return e[2]>-.3*Math.PI?e[2]+n:e[2]-n}function cC(e,t,n,r){const o=[],a=[];wa(t,o),wa(n,a);const i=[],s=[];sC(o,i),sC(a,s);let l=e;if(i[1]>.05&&s[1]>.05&&function(e,t){let n=e-t;for(n<0&&(n=-n);n>=2*Math.PI;)n-=2*Math.PI;return n>Math.PI&&(n=2*Math.PI-n),n}(i[2],s[2])>.33*Math.PI){let t=Math.max(i[0],s[0]);t=Math.max(88,t),e<.5?(s[0]=t,s[1]=0,s[2]=0,l*=2):(i[0]=t,i[1]=0,i[2]=0,l=2*l-1)}i[1]<.05&&s[1]>.05?i[2]=lC(s,i[0]):s[1]<.05&&i[1]>.05&&(s[2]=lC(i,s[0]));const c=[];c[0]=(1-l)*i[0]+l*s[0],c[1]=(1-l)*i[1]+l*s[1],c[2]=(1-l)*i[2]+l*s[2];const u=[];!function(e,t){const n=e[0],r=e[1],o=e[2];t[0]=n*Math.cos(r),t[1]=n*Math.sin(r)*Math.cos(o),t[2]=n*Math.sin(r)*Math.sin(o)}(c,u),Pa(u,r)}function uC(e,t){t.classHierarchy.push("vtkColorTransferFunction"),e.getSize=()=>t.nodes.length,e.addRGBPoint=(t,n,r,o)=>e.addRGBPointLong(t,n,r,o,.5,0),e.addRGBPointLong=function(n,r,o,a){let i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;if(i<0||i>1)return aC("Midpoint outside range [0.0, 1.0]"),-1;if(s<0||s>1)return aC("Sharpness outside range [0.0, 1.0]"),-1;t.allowDuplicateScalars||e.removePoint(n);const l={x:n,r:r,g:o,b:a,midpoint:i,sharpness:s};t.nodes.push(l),e.sortAndUpdateRange();let c=0;for(;c<t.nodes.length&&t.nodes[c].x!==n;c++);return c<t.nodes.length?c:-1},e.addHSVPoint=(t,n,r,o)=>e.addHSVPointLong(t,n,r,o,.5,0),e.addHSVPointLong=function(t,n,r,o){let a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.5,i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:0;const s=[],l=[n,r,o];return xa(l,s),e.addRGBPoint(t,s[0],s[1],s[2],a,i)},e.setNodes=n=>{if(t.nodes!==n){const r=JSON.stringify(t.nodes);t.nodes=n;const o=JSON.stringify(t.nodes);if(e.sortAndUpdateRange()||r!==o)return e.modified(),!0}return!1},e.sortAndUpdateRange=()=>{const n=JSON.stringify(t.nodes);t.nodes.sort(((e,t)=>e.x-t.x));const r=JSON.stringify(t.nodes),o=e.updateRange();return o||n===r?o:(e.modified(),!0)},e.updateRange=()=>{const n=[2];n[0]=t.mappingRange[0],n[1]=t.mappingRange[1];const r=t.nodes.length;return r?(t.mappingRange[0]=t.nodes[0].x,t.mappingRange[1]=t.nodes[r-1].x):(t.mappingRange[0]=0,t.mappingRange[1]=0),(n[0]!==t.mappingRange[0]||n[1]!==t.mappingRange[1])&&(e.modified(),!0)},e.removePoint=n=>{let r=0;for(;r<t.nodes.length&&t.nodes[r].x!==n;r++);const o=r;if(r>=t.nodes.length)return-1;let a=!1;return t.nodes.splice(r,1),0!==r&&r!==t.nodes.length||(a=e.updateRange()),a||e.modified(),o},e.movePoint=(n,r)=>{if(n!==r){e.removePoint(r);for(let o=0;o<t.nodes.length;o++)if(t.nodes[o].x===n){t.nodes[o].x=r,e.sortAndUpdateRange();break}}},e.removeAllPoints=()=>{t.nodes=[],e.sortAndUpdateRange()},e.addRGBSegment=(n,r,o,a,i,s,l,c)=>{e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n&&t.nodes[e].x<=i?t.nodes.splice(e,1):e++;e.addRGBPointLong(n,r,o,a,.5,0),e.addRGBPointLong(i,s,l,c,.5,0),e.modified()},e.addHSVSegment=(t,n,r,o,a,i,s,l)=>{const c=[i,s,l],u=[],d=[];xa([n,r,o],u),xa(c,d),e.addRGBSegment(t,u[0],u[1],u[2],a,d[0],d[1],d[2])},e.mapValue=t=>{const n=[];return e.getColor(t,n),[Math.floor(255*n[0]+.5),Math.floor(255*n[1]+.5),Math.floor(255*n[2]+.5),255]},e.getColor=(n,r)=>{if(t.indexedLookup){const t=e.getSize(),o=e.getAnnotatedValueIndexInternal(n);if(o<0||0===t){const t=e.getNanColorByReference();r[0]=t[0],r[1]=t[1],r[2]=t[2]}else{const n=[];e.getNodeValue(o%t,n),r[0]=n[1],r[1]=n[2],r[2]=n[3]}}else e.getTable(n,n,1,r)},e.getRedValue=t=>{const n=[];return e.getColor(t,n),n[0]},e.getGreenValue=t=>{const n=[];return e.getColor(t,n),n[1]},e.getBlueValue=t=>{const n=[];return e.getColor(t,n),n[2]},e.getTable=(n,r,o,a)=>{const i=Number(n),s=Number(r);if(_a(i)||_a(s)){for(let e=0;e<o;e++)a[3*e+0]=t.nanColor[0],a[3*e+1]=t.nanColor[1],a[3*e+2]=t.nanColor[2];return}let l=0;const c=t.nodes.length;let u=0,d=0,p=0;0!==c&&(u=t.nodes[c-1].r,d=t.nodes[c-1].g,p=t.nodes[c-1].b);let f=0,g=0,m=0;const h=[0,0,0],v=[0,0,0];let y=0,T=0;const b=[];let x=t.scale===nC.LOG10;x&&(x=t.mappingRange[0]>0);let C=0,S=0,A=0;x&&(C=Math.log10(i),S=Math.log10(s));for(let n=0;n<o;n++){const r=3*n;if(o>1?x?(A=C+n/(o-1)*(S-C),f=10**A):f=i+n/(o-1)*(s-i):x?(A=.5*(C+S),f=10**A):f=.5*(i+s),t.discretize){const e=t.mappingRange;if(f>=e[0]&&f<=e[1]){const n=t.numberOfValues,r=e[1]-e[0];if(n<=1)f=e[0]+r/2;else{const t=(f-e[0])/r,o=Eo(n*t);f=e[0]+o/(n-1)*r}}}for(;l<c&&f>t.nodes[l].x;)l++,l<c&&(g=t.nodes[l-1].x,m=t.nodes[l].x,x&&(g=Math.log10(g),m=Math.log10(m)),h[0]=t.nodes[l-1].r,v[0]=t.nodes[l].r,h[1]=t.nodes[l-1].g,v[1]=t.nodes[l].g,h[2]=t.nodes[l-1].b,v[2]=t.nodes[l].b,y=t.nodes[l-1].midpoint,T=t.nodes[l-1].sharpness,y<1e-5&&(y=1e-5),y>.99999&&(y=.99999));if(f>t.mappingRange[1])a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseAboveRangeColor()?(a[r]=t.aboveRangeColor[0],a[r+1]=t.aboveRangeColor[1],a[r+2]=t.aboveRangeColor[2]):(a[r]=u,a[r+1]=d,a[r+2]=p));else if(f<t.mappingRange[0]||La(f)&&f<0)a[r]=0,a[r+1]=0,a[r+2]=0,t.clamping&&(e.getUseBelowRangeColor()?(a[r]=t.belowRangeColor[0],a[r+1]=t.belowRangeColor[1],a[r+2]=t.belowRangeColor[2]):c>0&&(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b));else if(0===l&&(Math.abs(f-i)<1e-6||t.discretize))c>0?(a[r]=t.nodes[0].r,a[r+1]=t.nodes[0].g,a[r+2]=t.nodes[0].b):(a[r]=0,a[r+1]=0,a[r+2]=0);else{let e=0;if(e=x?(A-g)/(m-g):(f-g)/(m-g),e=e<y?.5*e/y:.5+.5*(e-y)/(1-y),T>.99){if(e<.5){a[r]=h[0],a[r+1]=h[1],a[r+2]=h[2];continue}a[r]=v[0],a[r+1]=v[1],a[r+2]=v[2];continue}if(T<.01){if(t.colorSpace===tC.RGB)a[r]=(1-e)*h[0]+e*v[0],a[r+1]=(1-e)*h[1]+e*v[1],a[r+2]=(1-e)*h[2]+e*v[2];else if(t.colorSpace===tC.HSV){const n=[],o=[];ba(h,n),ba(v,o),t.hSVWrap&&(n[0]-o[0]>.5||o[0]-n[0]>.5)&&(n[0]>o[0]?n[0]-=1:o[0]-=1);const i=[];i[0]=(1-e)*n[0]+e*o[0],i[0]<0&&(i[0]+=1),i[1]=(1-e)*n[1]+e*o[1],i[2]=(1-e)*n[2]+e*o[2],xa(i,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const t=[],n=[];wa(h,t),wa(v,n);const o=[];o[0]=(1-e)*t[0]+e*n[0],o[1]=(1-e)*t[1]+e*n[1],o[2]=(1-e)*t[2]+e*n[2],Pa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.",t.colorSpace);continue}e<.5?e=.5*(2*e)**(1+10*T):e>.5&&(e=1-.5*(2*(1-e))**(1+10*T));const n=e*e,o=n*e,i=2*o-3*n+1,s=-2*o+3*n,l=o-2*n+e,c=o-n;let u,d;if(t.colorSpace===tC.RGB)for(let e=0;e<3;e++)u=v[e]-h[e],d=(1-T)*u,a[r+e]=i*h[e]+s*v[e]+l*d+c*d;else if(t.colorSpace===tC.HSV){const e=[],n=[];ba(h,e),ba(v,n),t.hSVWrap&&(e[0]-n[0]>.5||n[0]-e[0]>.5)&&(e[0]>n[0]?e[0]-=1:n[0]-=1);const o=[];for(let t=0;t<3;t++)u=n[t]-e[t],d=(1-T)*u,o[t]=i*e[t]+s*n[t]+l*d+c*d,0===t&&o[t]<0&&(o[t]+=1);xa(o,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else if(t.colorSpace===tC.LAB){const e=[],t=[];wa(h,e),wa(v,t);const n=[];for(let r=0;r<3;r++)u=t[r]-e[r],d=(1-T)*u,n[r]=i*e[r]+s*t[r]+l*d+c*d;Pa(n,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]}else t.colorSpace===tC.DIVERGING?(cC(e,h,v,b),a[r]=b[0],a[r+1]=b[1],a[r+2]=b[2]):aC("ColorSpace set to invalid value.");for(let e=0;e<3;e++)a[r+e]=a[r+e]<0?0:a[r+e],a[r+e]=a[r+e]>1?1:a[r+e]}}},e.getUint8Table=function(n,r,o){let a=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(e.getMTime()<=t.buildTime&&t.tableSize===o&&t.tableWithAlpha!==a)return t.table;if(0===t.nodes.length)return aC("Attempting to lookup a value with no points in the function"),t.table;const i=a?4:3;t.tableSize===o&&t.tableWithAlpha===a||(t.table=new Uint8Array(o*i),t.tableSize=o,t.tableWithAlpha=a);const s=[];e.getTable(n,r,o,s);for(let e=0;e<o;e++)t.table[e*i+0]=Math.floor(255*s[3*e+0]+.5),t.table[e*i+1]=Math.floor(255*s[3*e+1]+.5),t.table[e*i+2]=Math.floor(255*s[3*e+2]+.5),a&&(t.table[e*i+3]=255);return t.buildTime.modified(),t.table},e.buildFunctionFromArray=n=>{e.removeAllPoints();const r=n.getNumberOfComponents();for(let e=0;e<n.getNumberOfTuples();e++)switch(r){case 3:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:.5,sharpness:0});break;case 4:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:.5,sharpness:0});break;case 5:t.nodes.push({x:e,r:n.getComponent(e,0),g:n.getComponent(e,1),b:n.getComponent(e,2),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)});break;case 6:t.nodes.push({x:n.getComponent(e,0),r:n.getComponent(e,1),g:n.getComponent(e,2),b:n.getComponent(e,3),midpoint:n.getComponent(e,4),sharpness:n.getComponent(e,5)})}e.sortAndUpdateRange()},e.buildFunctionFromTable=(n,r,o,a)=>{let i=0;e.removeAllPoints(),o>1&&(i=(r-n)/(o-1));for(let e=0;e<o;e++){const r={x:n+i*e,r:a[3*e],g:a[3*e+1],b:a[3*e+2],sharpness:0,midpoint:.5};t.nodes.push(r)}e.sortAndUpdateRange()},e.getNodeValue=(e,n)=>e<0||e>=t.nodes.length?(aC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].r,n[2]=t.nodes[e].g,n[3]=t.nodes[e].b,n[4]=t.nodes[e].midpoint,n[5]=t.nodes[e].sharpness,1),e.setNodeValue=(n,r)=>{if(n<0||n>=t.nodes.length)return aC("Index out of range!"),-1;const o=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].r=r[1],t.nodes[n].g=r[2],t.nodes[n].b=r[3],t.nodes[n].midpoint=r[4],t.nodes[n].sharpness=r[5],o!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.getNumberOfAvailableColors=()=>{if(t.indexedLookup&&e.getSize())return e.getSize();if(t.tableSize)return t.tableSize;const n=t.nodes?.length??0;return Math.max(4094,n)},e.getIndexedColor=(t,n)=>{const r=e.getSize();if(r>0&&t>=0){const o=[];e.getNodeValue(t%r,o);for(let e=0;e<3;++e)n[e]=o[e+1];return void(n[3]=1)}const o=e.getNanColorByReference();n[0]=o[0],n[1]=o[1],n[2]=o[2],n[3]=1},e.fillFromDataPointer=(t,n)=>{if(!(t<=0)&&n){e.removeAllPoints();for(let r=0;r<t;r++)e.addRGBPoint(n[4*r],n[4*r+1],n[4*r+2],n[4*r+3])}},e.setMappingRange=(n,r)=>{const o=[n,r],a=e.getRange();if(a[1]===o[1]&&a[0]===o[0])return;if(o[1]===o[0])return void aC("attempt to set zero width color range");const i=(o[1]-o[0])/(a[1]-a[0]),s=o[0]-a[0]*i;for(let e=0;e<t.nodes.length;++e)t.nodes[e].x=t.nodes[e].x*i+s;t.mappingRange[0]=o[0],t.mappingRange[1]=o[1],e.modified()},e.adjustRange=n=>{const r=e.getRange(),o=[];r[0]<n[0]?(e.getColor(n[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])):(e.getColor(r[0],o),e.addRGBPoint(n[0],o[0],o[1],o[2])),r[1]>n[1]?(e.getColor(n[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])):(e.getColor(r[1],o),e.addRGBPoint(n[1],o[0],o[1],o[2])),e.sortAndUpdateRange();for(let e=0;e<t.nodes.length;)t.nodes[e].x>=n[0]&&t.nodes[e].x<=n[1]?t.nodes.splice(e,1):++e;return 1},e.estimateMinNumberOfSamples=(t,n)=>{const r=e.findMinimumXDistance();return Math.ceil((n-t)/r)},e.findMinimumXDistance=()=>{if(t.nodes.length<2)return-1;let e=Number.MAX_VALUE;for(let n=0;n<t.nodes.length-1;n++){const r=t.nodes[n+1].x-t.nodes[n].x;r<e&&(e=r)}return e},e.mapScalarsThroughTable=(n,r,o,a)=>{0!==e.getSize()?t.indexedLookup?e.mapDataIndexed(n,r,o,a):e.mapData(n,r,o,a):oC("Transfer Function Has No Points!")},e.mapData=(t,n,r,o)=>{if(0===e.getSize())return void iC("Transfer Function Has No Points!");const a=Math.floor(255*e.getAlpha()+.5),i=t.getNumberOfTuples(),s=t.getNumberOfComponents(),l=n.getData(),c=t.getData(),u=[];if(r===rC.RGBA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[4*t]=Math.floor(255*u[0]+.5),l[4*t+1]=Math.floor(255*u[1]+.5),l[4*t+2]=Math.floor(255*u[2]+.5),l[4*t+3]=a}if(r===rC.RGB)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[3*t]=Math.floor(255*u[0]+.5),l[3*t+1]=Math.floor(255*u[1]+.5),l[3*t+2]=Math.floor(255*u[2]+.5)}if(r===rC.LUMINANCE)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5)}if(r===rC.LUMINANCE_ALPHA)for(let t=0;t<i;t++){const n=c[t*s+o];e.getColor(n,u),l[2*t]=Math.floor(76.5*u[0]+150.45*u[1]+28.05*u[2]+.5),l[2*t+1]=a}},e.applyColorMap=n=>{const r=JSON.stringify(t.colorSpace);n.ColorSpace&&(t.colorSpace=tC[n.ColorSpace.toUpperCase()],void 0===t.colorSpace&&(aC(`ColorSpace ${n.ColorSpace} not supported, using RGB instead`),t.colorSpace=tC.RGB));let o=r!==JSON.stringify(t.colorSpace);const a=o||JSON.stringify(t.nanColor);if(n.NanColor)for(t.nanColor=[].concat(n.NanColor);t.nanColor.length<4;)t.nanColor.push(1);o=o||a!==JSON.stringify(t.nanColor);const i=o||JSON.stringify(t.nodes);if(n.RGBPoints){const e=n.RGBPoints.length;t.nodes=[];const r=.5,o=0;for(let a=0;a<e;a+=4)t.nodes.push({x:n.RGBPoints[a],r:n.RGBPoints[a+1],g:n.RGBPoints[a+2],b:n.RGBPoints[a+3],midpoint:r,sharpness:o})}const s=e.sortAndUpdateRange(),l=!s&&(o||i!==JSON.stringify(t.nodes));return l&&e.modified(),s||l}}const dC={clamping:!0,colorSpace:tC.RGB,hSVWrap:!0,scale:nC.LINEAR,nanColor:null,belowRangeColor:null,aboveRangeColor:null,useAboveRangeColor:!1,useBelowRangeColor:!1,allowDuplicateScalars:!1,table:null,tableSize:0,buildTime:null,nodes:null,discretize:!1,numberOfValues:256};function pC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dC,n),Pl.extend(e,t,n),t.table=[],t.nodes=[],t.nanColor=[.5,0,0,1],t.belowRangeColor=[0,0,0,1],t.aboveRangeColor=[1,1,1,1],t.buildTime={},jt.obj(t.buildTime),jt.get(e,t,["buildTime","mappingRange"]),jt.setGet(e,t,["useAboveRangeColor","useBelowRangeColor","discretize","numberOfValues",{type:"enum",name:"colorSpace",enum:tC},{type:"enum",name:"scale",enum:nC}]),jt.setArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"],4),jt.getArray(e,t,["nanColor","belowRangeColor","aboveRangeColor"]),uC(e,t)}var fC={newInstance:jt.newInstance(pC,"vtkColorTransferFunction"),extend:pC,...eC},gC={OrientationModes:{DIRECTION:0,ROTATION:1,MATRIX:2},ScaleModes:{SCALE_BY_CONSTANT:0,SCALE_BY_MAGNITUDE:1,SCALE_BY_COMPONENTS:2}};const{OrientationModes:mC,ScaleModes:hC}=gC,{vtkErrorMacro:vC}=jt;function yC(e,t){t.classHierarchy.push("vtkGlyph3DMapper"),e.getOrientationModeAsString=()=>jt.enumToString(mC,t.orientationMode),e.setOrientationModeToDirection=()=>e.setOrientationMode(mC.DIRECTION),e.setOrientationModeToRotation=()=>e.setOrientationMode(mC.ROTATION),e.setOrientationModeToMatrix=()=>e.setOrientationMode(mC.MATRIX),e.getOrientationArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.orientationArray?n.getPointData().getArray(t.orientationArray):n.getPointData().getVectors():null},e.getScaleModeAsString=()=>jt.enumToString(hC,t.scaleMode),e.setScaleModeToScaleByMagnitude=()=>e.setScaleMode(hC.SCALE_BY_MAGNITUDE),e.setScaleModeToScaleByComponents=()=>e.setScaleMode(hC.SCALE_BY_COMPONENTS),e.setScaleModeToScaleByConstant=()=>e.setScaleMode(hC.SCALE_BY_CONSTANT),e.getScaleArrayData=()=>{const n=e.getInputData(0);return n&&n.getPointData()?t.scaleArray?n.getPointData().getArray(t.scaleArray):n.getPointData().getScalars():null},e.getBounds=()=>{const n=e.getInputData(0),r=e.getInputData(1);return n&&r?(e.buildArrays(),t.bounds):Fa()},e.buildArrays=()=>{const n=e.getInputData(0),r=e.getInputData(1);if(t.buildTime.getMTime()<r.getMTime()||t.buildTime.getMTime()<n.getMTime()||t.buildTime.getMTime()<e.getMTime()){const o=n.getPoints().getData();let a=e.getScaleArrayData(),i=null,s=0;a&&(i=a.getData(),s=a.getNumberOfComponents()),t.scaling&&a&&t.scaleMode===hC.SCALE_BY_COMPONENTS&&3!==a.getNumberOfComponents()&&(vC("Cannot scale by components since scale array does not have 3 components."),a=null);const l=r.getBounds(),c=[];ki.getCorners(l,c),t.bounds[0]=ki.INIT_BOUNDS[0],t.bounds[1]=ki.INIT_BOUNDS[1],t.bounds[2]=ki.INIT_BOUNDS[2],t.bounds[3]=ki.INIT_BOUNDS[3],t.bounds[4]=ki.INIT_BOUNDS[4],t.bounds[5]=ki.INIT_BOUNDS[5];const u=new Float64Array(3),d=e.getOrientationArrayData(),p=g(new Float64Array(16)),f=[],m=[],h=o.length/3;t.matrixArray=new Float32Array(16*h);const v=t.matrixArray.buffer;t.normalArray=new Float32Array(9*h);const y=t.normalArray.buffer,w=[],P=[];for(let e=0;e<h;++e){const n=new Float32Array(v,64*e,16);if(f[0]=o[3*e],f[1]=o[3*e+1],f[2]=o[3*e+2],b(n,p,f),d)switch(d.getTuple(e,P),t.orientationMode){case mC.MATRIX:T(n,n,[...P.slice(0,3),0,...P.slice(3,6),0,...P.slice(6,9),0,0,0,0,1]);break;case mC.ROTATION:I(n,n,P[2]),S(n,n,P[0]),A(n,n,P[1]);break;case mC.DIRECTION:if(0===P[1]&&0===P[2])P[0]<0&&A(n,n,3.1415926);else{const e=$o(P),t=[];t[0]=(P[0]+e)/2,t[1]=P[1]/2,t[2]=P[2]/2,C(n,n,3.1415926,t)}}if(t.scaling){if(m[0]=t.scaleFactor,m[1]=t.scaleFactor,m[2]=t.scaleFactor,a)switch(t.scaleMode){case hC.SCALE_BY_MAGNITUDE:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=$o(w,s),m[1]=m[0],m[2]=m[0];break;case hC.SCALE_BY_COMPONENTS:for(let t=0;t<s;++t)w[t]=i[e*s+t];m[0]*=w[0],m[1]*=w[1],m[2]*=w[2];case hC.SCALE_BY_CONSTANT:}0===m[0]&&(m[0]=1e-10),0===m[1]&&(m[1]=1e-10),0===m[2]&&(m[2]=1e-10),x(n,n,m)}for(let e=0;e<8;++e)Vn(u,c[e],n),u[0]<t.bounds[0]&&(t.bounds[0]=u[0]),u[1]<t.bounds[2]&&(t.bounds[2]=u[1]),u[2]<t.bounds[4]&&(t.bounds[4]=u[2]),u[0]>t.bounds[1]&&(t.bounds[1]=u[0]),u[1]>t.bounds[3]&&(t.bounds[3]=u[1]),u[2]>t.bounds[5]&&(t.bounds[5]=u[2]);const r=new Float32Array(y,36*e,9);se(r,n),ge(r,r),fe(r,r)}const O=e.getAbstractScalars(n,t.scalarMode,t.arrayAccessMode,t.arrayId,t.colorByArrayName).scalars;t.useLookupTableScalarRange||e.getLookupTable().setRange(t.scalarRange[0],t.scalarRange[1]),t.colorArray=null;const R=e.getLookupTable();R&&O&&(R.build(),t.colorArray=R.mapScalars(O,t.colorMode,0)),t.buildTime.modified()}},e.getPrimitiveCount=()=>{const t=e.getInputData(1),n=e.getInputData().getPoints().getNumberOfValues()/3;return{points:n*t.getPoints().getNumberOfValues()/3,verts:n*(t.getVerts().getNumberOfValues()-t.getVerts().getNumberOfCells()),lines:n*(t.getLines().getNumberOfValues()-2*t.getLines().getNumberOfCells()),triangles:n*(t.getPolys().getNumberOfValues()-3*t.getLines().getNumberOfCells())}},e.setSourceConnection=t=>e.setInputConnection(t,1)}const TC={orient:!0,orientationMode:mC.DIRECTION,orientationArray:null,scaling:!0,scaleFactor:1,scaleMode:hC.SCALE_BY_MAGNITUDE,scaleArray:null,matrixArray:null,normalArray:null,colorArray:null};function bC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,TC,n),ic.extend(e,t,n),jt.algo(e,t,2,0),t.buildTime={},jt.obj(t.buildTime,{mtime:0}),t.boundsTime={},jt.obj(t.boundsTime,{mtime:0}),jt.setGet(e,t,["orient","orientationMode","orientationArray","scaleArray","scaleFactor","scaleMode","scaling"]),jt.get(e,t,["colorArray","matrixArray","normalArray","buildTime"]),yC(e,t)}var xC={newInstance:jt.newInstance(bC,"vtkGlyph3DMapper"),extend:bC,...gC};const{vtkErrorMacro:CC}=jt;function SC(e,t){t.classHierarchy.push("vtkPiecewiseFunction"),e.getSize=()=>t.nodes.length,e.getType=()=>{let e,n=0,r=0;t.nodes.length>0&&(n=t.nodes[0].y);for(let o=1;o<t.nodes.length;o++){if(e=t.nodes[o].y,e!==n)if(e>n)switch(r){case 0:case 1:r=1;break;default:r=3}else switch(r){case 0:case 2:r=2;break;default:r=3}if(n=e,3===r)break}switch(r){case 0:return"Constant";case 1:return"NonDecreasing";case 2:return"NonIncreasing";default:return"Varied"}},e.getDataPointer=()=>{const e=t.nodes.length;if(t.function=null,e>0){t.function=[];for(let n=0;n<e;n++)t.function[2*n]=t.nodes[n].x,t.function[2*n+1]=t.nodes[n].y}return t.function},e.getFirstNonZeroValue=()=>{if(0===t.nodes.length)return 0;let e=1,n=0,r=0;for(;r<t.nodes.length;r++)if(0!==t.nodes[r].y){e=0;break}return n=e?Number.MAX_VALUE:r>0?t.nodes[r-1].x:t.clamping?-Number.MAX_VALUE:t.nodes[0].x,n},e.getNodeValue=(e,n)=>{const r=t.nodes.length;return e<0||e>=r?(CC("Index out of range!"),-1):(n[0]=t.nodes[e].x,n[1]=t.nodes[e].y,n[2]=t.nodes[e].midpoint,n[3]=t.nodes[e].sharpness,1)},e.setNodeValue=(n,r)=>{const o=t.nodes.length;if(n<0||n>=o)return CC("Index out of range!"),-1;const a=t.nodes[n].x;return t.nodes[n].x=r[0],t.nodes[n].y=r[1],t.nodes[n].midpoint=r[2],t.nodes[n].sharpness=r[3],a!==r[0]?e.sortAndUpdateRange():e.modified(),1},e.addPoint=(t,n)=>e.addPointLong(t,n,.5,0),e.addPointLong=(n,r,o,a)=>{if(o<0||o>1)return CC("Midpoint outside range [0.0, 1.0]"),-1;if(a<0||a>1)return CC("Sharpness outside range 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u=0,d=0,p=0,f=0,g=0,m=0,h=0;for(a=0;a<r;a++){const v=i*a;for(u=r>1?e+a/(r-1)*(n-e):.5*(e+n);s<l&&u>t.nodes[s].x;)s++,s<l&&(d=t.nodes[s-1].x,p=t.nodes[s].x,f=t.nodes[s-1].y,g=t.nodes[s].y,m=t.nodes[s-1].midpoint,h=t.nodes[s-1].sharpness,m<1e-5&&(m=1e-5),m>.99999&&(m=.99999));if(s>=l)o[v]=t.clamping?c:0;else if(0===s)o[v]=t.clamping?t.nodes[0].y:0;else{let e=(u-d)/(p-d);if(e=e<m?.5*e/m:.5+.5*(e-m)/(1-m),h>.99){if(e<.5){o[v]=f;continue}o[v]=g;continue}if(h<.01){o[v]=(1-e)*f+e*g;continue}e<.5?e=.5*(2*e)**(1+10*h):e>.5&&(e=1-.5*(2*(1-e))**(1+10*h));const t=e*e,n=t*e,r=2*n-3*t+1,a=-2*n+3*t,i=n-2*t+e,s=n-t,l=(1-h)*(g-f);o[v]=r*f+a*g+i*l+s*l;const c=f<g?f:g,y=f>g?f:g;o[v]=o[v]<c?c:o[v],o[v]=o[v]>y?y:o[v]}}}}const AC={range:[0,0],clamping:!0,allowDuplicateScalars:!1};function IC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,AC,n),jt.obj(e,t),t.nodes=[],jt.setGet(e,t,["allowDuplicateScalars","clamping"]),jt.setArray(e,t,["range"],2),jt.getArray(e,t,["range"]),SC(e,t)}var wC={newInstance:jt.newInstance(IC,"vtkPiecewiseFunction"),extend:IC};const{InterpolationType:PC,OpacityMode:OC}=_g,{vtkErrorMacro:RC}=jt;function MC(e,t){t.classHierarchy.push("vtkVolumeProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)1===t.componentData[r].colorChannels?t.componentData[r].grayTransferFunction&&(e=t.componentData[r].grayTransferFunction.getMTime(),n=n>e?n:e):3===t.componentData[r].colorChannels&&t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].scalarOpacity&&(e=t.componentData[r].scalarOpacity.getMTime(),n=n>e?n:e),t.componentData[r].gradientOpacity&&(t.componentData[r].disableGradientOpacity||(e=t.componentData[r].gradientOpacity.getMTime(),n=n>e?n:e));return n},e.getColorChannels=e=>e<0||e>3?(RC("Bad index - must be between 0 and 3"),0):t.componentData[e].colorChannels,e.setGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].grayTransferFunction!==r&&(t.componentData[n].grayTransferFunction=r,o=!0),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1,o=!0),o&&e.modified(),o},e.getGrayTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].grayTransferFunction&&(t.componentData[n].grayTransferFunction=wC.newInstance(),t.componentData[n].grayTransferFunction.addPoint(0,0),t.componentData[n].grayTransferFunction.addPoint(1024,1),1!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=1),e.modified()),t.componentData[n].grayTransferFunction},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=!1;return t.componentData[n].rGBTransferFunction!==r&&(t.componentData[n].rGBTransferFunction=r,o=!0),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3,o=!0),o&&e.modified(),o},e.getRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].rGBTransferFunction&&(t.componentData[n].rGBTransferFunction=fC.newInstance(),t.componentData[n].rGBTransferFunction.addRGBPoint(0,0,0,0),t.componentData[n].rGBTransferFunction.addRGBPoint(1024,1,1,1),3!==t.componentData[n].colorChannels&&(t.componentData[n].colorChannels=3),e.modified()),t.componentData[n].rGBTransferFunction},e.setScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return t.componentData[n].scalarOpacity!==r&&(t.componentData[n].scalarOpacity=r,e.modified(),!0)},e.getScalarOpacity=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return null===t.componentData[n].scalarOpacity&&(t.componentData[n].scalarOpacity=wC.newInstance(),t.componentData[n].scalarOpacity.addPoint(0,1),t.componentData[n].scalarOpacity.addPoint(1024,1),e.modified()),t.componentData[n].scalarOpacity},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return RC("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?(RC("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(PC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(PC.LINEAR),e.setInterpolationTypeToFastLinear=()=>e.setInterpolationType(PC.FAST_LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(PC,t.interpolationType),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`set${r}`]=(r,o)=>t.componentData[r][`${n}`]!==o&&(t.componentData[r][`${n}`]=o,e.modified(),!0)})),["useGradientOpacity","scalarOpacityUnitDistance","gradientOpacityMinimumValue","gradientOpacityMinimumOpacity","gradientOpacityMaximumValue","gradientOpacityMaximumOpacity","opacityMode","forceNearestInterpolation"].forEach((n=>{const r=jt.capitalize(n);e[`get${r}`]=e=>t.componentData[e][`${n}`]}))}const EC={colorMixPreset:null,independentComponents:!0,interpolationType:PC.FAST_LINEAR,shade:!1,ambient:.1,diffuse:.7,specular:.2,specularPower:10,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function DC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,EC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;++e)t.componentData.push({colorChannels:1,grayTransferFunction:null,rGBTransferFunction:null,scalarOpacity:null,scalarOpacityUnitDistance:1,opacityMode:OC.FRACTIONAL,gradientOpacityMinimumValue:0,gradientOpacityMinimumOpacity:0,gradientOpacityMaximumValue:1,gradientOpacityMaximumOpacity:1,useGradientOpacity:!1,componentWeight:1,forceNearestInterpolation:!1})}jt.setGet(e,t,["colorMixPreset","independentComponents","interpolationType","shade","ambient","diffuse","specular","specularPower","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),MC(e,t)}var VC={newInstance:jt.newInstance(DC,"vtkVolumeProperty"),extend:DC,..._g};const{vtkDebugMacro:LC}=jt;function BC(e,t){t.classHierarchy.push("vtkVolume"),e.getVolumes=()=>e,e.makeProperty=VC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){LC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e));const r=[];ki.getCorners(n,r),e.computeMatrix();const o=new Float64Array(16);m(o,t.matrix),r.forEach((e=>Vn(e,e,o))),t.bounds[0]=t.bounds[2]=t.bounds[4]=Number.MAX_VALUE,t.bounds[1]=t.bounds[3]=t.bounds[5]=-Number.MAX_VALUE,t.bounds=t.bounds.map(((e,t)=>t%2==0?r.reduce(((e,n)=>e>n[t/2]?n[t/2]:e),e):r.reduce(((e,n)=>e<n[(t-1)/2]?n[(t-1)/2]:e),e))),t.boundsMTime.modified()}var r;return t.bounds},e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}return e}}const NC={mapper:null,property:null,bounds:[1,-1,1,-1,1,-1]};function _C(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,NC,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),BC(e,t)}var FC={newInstance:jt.newInstance(_C,"vtkVolume"),extend:_C};const{BlendMode:kC,FilterMode:GC}=kg,UC={createRadonTransferFunction:function(e,t,n,r,o){let a=null;return o?(a=o,a.removeAllPoints()):a=wC.newInstance(),a.addPointLong(-1024,0,1,1),a.addPoint(e,t),a.addPoint(n,r),a}};function zC(e,t){t.classHierarchy.push("vtkVolumeMapper");const n={...e};e.getBounds=()=>{const n=e.getInputData();return n?(t.static||e.update(),t.bounds=n.getBounds()):t.bounds=Fa(),t.bounds},e.update=()=>{e.getInputData()},e.setBlendModeToComposite=()=>{e.setBlendMode(kC.COMPOSITE_BLEND)},e.setBlendModeToMaximumIntensity=()=>{e.setBlendMode(kC.MAXIMUM_INTENSITY_BLEND)},e.setBlendModeToMinimumIntensity=()=>{e.setBlendMode(kC.MINIMUM_INTENSITY_BLEND)},e.setBlendModeToAverageIntensity=()=>{e.setBlendMode(kC.AVERAGE_INTENSITY_BLEND)},e.setBlendModeToAdditiveIntensity=()=>{e.setBlendMode(kC.ADDITIVE_INTENSITY_BLEND)},e.setBlendModeToRadonTransform=()=>{e.setBlendMode(kC.RADON_TRANSFORM_BLEND)},e.getBlendModeAsString=()=>jt.enumToString(kC,t.blendMode),e.setAverageIPScalarRange=(t,n)=>{console.warn("setAverageIPScalarRange is deprecated use setIpScalarRange"),e.setIpScalarRange(t,n)},e.getFilterModeAsString=()=>jt.enumToString(GC,t.filterMode),e.setFilterModeToOff=()=>{e.setFilterMode(GC.OFF)},e.setFilterModeToNormalized=()=>{e.setFilterMode(GC.NORMALIZED)},e.setFilterModeToRaw=()=>{e.setFilterMode(GC.RAW)},e.setGlobalIlluminationReach=e=>n.setGlobalIlluminationReach(Ma(e,0,1)),e.setVolumetricScatteringBlending=e=>n.setVolumetricScatteringBlending(Ma(e,0,1)),e.setVolumeShadowSamplingDistFactor=e=>n.setVolumeShadowSamplingDistFactor(e>=1?e:1),e.setAnisotropy=e=>n.setAnisotropy(Ma(e,-.99,.99)),e.setLAOKernelSize=e=>n.setLAOKernelSize(Eo(Ma(e,1,32))),e.setLAOKernelRadius=e=>n.setLAOKernelRadius(e>=1?e:1)}const WC={bounds:[1,-1,1,-1,1,-1],sampleDistance:1,imageSampleDistance:1,maximumSamplesPerRay:1e3,autoAdjustSampleDistances:!0,initialInteractionScale:1,interactionSampleDistanceFactor:1,blendMode:kC.COMPOSITE_BLEND,ipScalarRange:[-1e6,1e6],filterMode:GC.OFF,preferSizeOverAccuracy:!1,computeNormalFromOpacity:!1,volumetricScatteringBlending:0,globalIlluminationReach:0,volumeShadowSamplingDistFactor:5,anisotropy:0,localAmbientOcclusion:!1,LAOKernelSize:15,LAOKernelRadius:7};function HC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,WC,n),Fs(e,t,n),jt.setGet(e,t,["sampleDistance","imageSampleDistance","maximumSamplesPerRay","autoAdjustSampleDistances","initialInteractionScale","interactionSampleDistanceFactor","blendMode","filterMode","preferSizeOverAccuracy","computeNormalFromOpacity","volumetricScatteringBlending","globalIlluminationReach","volumeShadowSamplingDistFactor","anisotropy","localAmbientOcclusion","LAOKernelSize","LAOKernelRadius"]),jt.setGetArray(e,t,["ipScalarRange"],2),jt.event(e,t,"lightingActivated"),zC(e,t)}var jC={newInstance:jt.newInstance(HC,"vtkVolumeMapper"),extend:HC,...UC};const{InterpolationType:KC}=ag,{vtkErrorMacro:$C}=jt;function qC(e,t){t.classHierarchy.push("vtkImageProperty"),e.getMTime=()=>{let e,n=t.mtime;for(let r=0;r<4;r++)t.componentData[r].rGBTransferFunction&&(e=t.componentData[r].rGBTransferFunction.getMTime(),n=n>e?n:e),t.componentData[r].piecewiseFunction&&(e=t.componentData[r].piecewiseFunction.getMTime(),n=n>e?n:e);return n},e.setRGBTransferFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].rGBTransferFunction!==a&&(t.componentData[o].rGBTransferFunction=a,e.modified(),!0)},e.getRGBTransferFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].rGBTransferFunction},e.setPiecewiseFunction=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,o=n,a=r;return Number.isInteger(n)||(a=n,o=0),t.componentData[o].piecewiseFunction!==a&&(t.componentData[o].piecewiseFunction=a,e.modified(),!0)},e.getPiecewiseFunction=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return t.componentData[e].piecewiseFunction},e.setScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,r=t,o=n;return Number.isInteger(t)||(o=t,r=0),e.setPiecewiseFunction(r,o)},e.getScalarOpacity=function(){let t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e.getPiecewiseFunction(t)},e.setComponentWeight=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1;if(n<0||n>=4)return $C("Invalid index"),!1;const o=Math.min(1,Math.max(0,r));return t.componentData[n].componentWeight!==o&&(t.componentData[n].componentWeight=o,e.modified(),!0)},e.getComponentWeight=function(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;return e<0||e>=4?($C("Invalid index"),0):t.componentData[e].componentWeight},e.setInterpolationTypeToNearest=()=>e.setInterpolationType(KC.NEAREST),e.setInterpolationTypeToLinear=()=>e.setInterpolationType(KC.LINEAR),e.getInterpolationTypeAsString=()=>jt.enumToString(KC,t.interpolationType)}const XC={independentComponents:!1,interpolationType:KC.LINEAR,colorWindow:255,colorLevel:127.5,ambient:1,diffuse:0,opacity:1,useLookupTableScalarRange:!1,useLabelOutline:!1,labelOutlineThickness:[1],labelOutlineOpacity:1};function YC(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};if(Object.assign(t,XC,n),jt.obj(e,t),!t.componentData){t.componentData=[];for(let e=0;e<4;e++)t.componentData.push({rGBTransferFunction:null,piecewiseFunction:null,componentWeight:1})}jt.setGet(e,t,["independentComponents","interpolationType","colorWindow","colorLevel","ambient","diffuse","opacity","useLookupTableScalarRange","useLabelOutline","labelOutlineOpacity"]),jt.setGetArray(e,t,["labelOutlineThickness"]),qC(e,t)}var ZC={newInstance:jt.newInstance(YC,"vtkImageProperty"),extend:YC};const{vtkDebugMacro:QC}=jt;function JC(e,t){t.classHierarchy.push("vtkImageSlice"),e.getActors=()=>e,e.getImages=()=>e,e.getIsOpaque=()=>{if(t.forceOpaque)return!0;if(t.forceTranslucent)return!1;t.property||e.getProperty();let n=t.property.getOpacity()>=1;return n=n&&(!t.mapper||t.mapper.getIsOpaque()),n},e.hasTranslucentPolygonalGeometry=()=>!1,e.makeProperty=ZC.newInstance,e.getProperty=()=>(null===t.property&&(t.property=e.makeProperty()),t.property),e.getBounds=()=>{if(null===t.mapper)return t.bounds;const n=t.mapper.getBounds();if(!n||6!==n.length)return n;if(n[0]>n[1])return t.mapperBounds=n.concat(),t.bounds=[1,-1,1,-1,1,-1],t.boundsMTime.modified(),n;if(!t.mapperBounds||!(r=[n,t.mapperBounds],r[0].map(((e,t)=>r.map((e=>e[t]))))).reduce(((e,t)=>e&&t[0]===t[1]),!0)||e.getMTime()>t.boundsMTime.getMTime()){QC("Recomputing bounds..."),t.mapperBounds=n.map((e=>e)),e.computeMatrix();const r=new Float64Array(16);m(r,t.matrix),ki.transformBounds(n,r,t.bounds),t.boundsMTime.modified()}var r;return t.bounds},e.getBoundsForSlice=(n,r)=>{const o=t.mapper.getBoundsForSlice(n,r);if(!ki.isValid(o))return o;e.computeMatrix();const a=new Float64Array(16);return m(a,t.matrix),ki.transformBounds(o,a)},e.getMinXBound=()=>e.getBounds()[0],e.getMaxXBound=()=>e.getBounds()[1],e.getMinYBound=()=>e.getBounds()[2],e.getMaxYBound=()=>e.getBounds()[3],e.getMinZBound=()=>e.getBounds()[4],e.getMaxZBound=()=>e.getBounds()[5],e.getMTime=()=>{let e=t.mtime;if(null!==t.property){const n=t.property.getMTime();e=n>e?n:e}return e},e.getRedrawMTime=()=>{let e=t.mtime;if(null!==t.mapper){let n=t.mapper.getMTime();e=n>e?n:e,null!==t.mapper.getInput()&&(t.mapper.getInputAlgorithm().update(),n=t.mapper.getInput().getMTime(),e=n>e?n:e)}if(null!==t.property){let n=t.property.getMTime();e=n>e?n:e,null!==t.property.getRGBTransferFunction()&&(n=t.property.getRGBTransferFunction().getMTime(),e=n>e?n:e)}return e},e.getSupportsSelection=()=>!!t.mapper&&t.mapper.getSupportsSelection()}const eS={mapper:null,property:null,bounds:[...ki.INIT_BOUNDS]};function tS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,eS,n),ts.extend(e,t,n),t.boundsMTime={},jt.obj(t.boundsMTime),jt.set(e,t,["property"]),jt.setGet(e,t,["mapper"]),jt.getArray(e,t,["bounds"],6),JC(e,t)}var nS={newInstance:jt.newInstance(tS,"vtkImageSlice"),extend:tS};function rS(e,t){t.classHierarchy.push("vtkAbstractImageMapper"),e.getIsOpaque=()=>!0,e.getCurrentImage=()=>null,e.getBoundsForSlice=()=>(jt.vtkErrorMacro("vtkAbstractImageMapper.getBoundsForSlice - NOT IMPLEMENTED"),Fa())}const oS={slice:0,customDisplayExtent:[0,0,0,0,0,0],useCustomExtents:!1,backgroundColor:[0,0,0,1]};var aS=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,oS,n),Fs(e,t,n),jt.setGet(e,t,["slice","useCustomExtents"]),jt.setGetArray(e,t,["customDisplayExtent"],6),jt.setGetArray(e,t,["backgroundColor"],4),rS(e,t)};function iS(e,t,n){const r=n.getCurrentImage(),o=r.getExtent(),a=[o[0],o[2],o[4]],{ijkMode:i}=n.getClosestIJKAxis();let s=n.isA("vtkImageArrayMapper")?n.getSubSlice():n.getSlice();i!==n.getSlicingMode()&&(s=n.getSliceAtPosition(s)),a[i]+=s;const l=[0,0,0];r.indexToWorld(a,l),a[i]+=1;const c=[0,0,0];r.indexToWorld(a,c),c[0]-=l[0],c[1]-=l[1],c[2]-=l[2],Mn(c,c);const u=ni.intersectWithLine(e,t,l,c);if(u.intersection){const e=u.x,t=[0,0,0];return r.worldToIndex(e,t),{t:u.t,absoluteIJK:t}}return null}const{staticOffsetAPI:sS,otherStaticMethods:lS}=zl,{SlicingMode:cS}=pg;function uS(e,t){function n(){let n;switch(t.slicingMode){case cS.X:n=0;break;case cS.Y:n=1;break;case cS.Z:n=2;break;default:return void(t.closestIJKAxis={ijkMode:t.slicingMode,flip:!1})}const r=ka(e.getCurrentImage().getDirection());let o=0;for(;o<3&&0===r[n+3*o];++o);const a=r[n+3*o]<0;t.closestIJKAxis={ijkMode:o,flip:a}}t.classHierarchy.push("vtkImageMapper"),e.getSliceAtPosition=n=>{const r=e.getCurrentImage();let o;if(3===n.length)o=n;else if(Number.isFinite(n)){const e=r.getBounds();switch(t.slicingMode){case cS.X:o=[n,(e[3]+e[2])/2,(e[5]+e[4])/2];break;case cS.Y:o=[(e[1]+e[0])/2,n,(e[5]+e[4])/2];break;case cS.Z:o=[(e[1]+e[0])/2,(e[3]+e[2])/2,n]}}const a=[0,0,0];r.worldToIndex(o,a);const i=r.getExtent(),{ijkMode:s}=e.getClosestIJKAxis();let l=0;switch(s){case cS.I:l=Ma(a[0],i[0],i[1]);break;case cS.J:l=Ma(a[1],i[2],i[3]);break;case cS.K:l=Ma(a[2],i[4],i[5]);break;default:return 0}return l},e.setSliceFromCamera=n=>{const r=n.getFocalPoint();switch(t.slicingMode){case cS.I:case cS.J:case cS.K:{const t=e.getSliceAtPosition(r);e.setSlice(t)}break;case cS.X:e.setSlice(r[0]);break;case cS.Y:e.setSlice(r[1]);break;case cS.Z:e.setSlice(r[2])}},e.setXSlice=t=>{e.setSlicingMode(cS.X),e.setSlice(t)},e.setYSlice=t=>{e.setSlicingMode(cS.Y),e.setSlice(t)},e.setZSlice=t=>{e.setSlicingMode(cS.Z),e.setSlice(t)},e.setISlice=t=>{e.setSlicingMode(cS.I),e.setSlice(t)},e.setJSlice=t=>{e.setSlicingMode(cS.J),e.setSlice(t)},e.setKSlice=t=>{e.setSlicingMode(cS.K),e.setSlice(t)},e.getSlicingModeNormal=()=>{const n=[0,0,0],r=e.getCurrentImage().getDirection();switch(t.slicingMode){case cS.X:n[0]=1;break;case cS.Y:n[1]=1;break;case cS.Z:n[2]=1;break;case cS.I:ea(r,[1,0,0],n);break;case cS.J:ea(r,[0,1,0],n);break;case cS.K:ea(r,[0,0,1],n)}return n},e.setSlicingMode=r=>{t.slicingMode!==r&&(t.slicingMode=r,e.getCurrentImage()&&n(),e.modified())},e.getClosestIJKAxis=()=>(void 0!==t.closestIJKAxis&&t.closestIJKAxis.ijkMode!==cS.NONE||!e.getCurrentImage()||n(),t.closestIJKAxis),e.getBounds=()=>{const n=e.getCurrentImage();if(!n)return Fa();if(!t.useCustomExtents)return n.getBounds();const r=t.customDisplayExtent.slice(),{ijkMode:o}=e.getClosestIJKAxis();let a=t.slice;switch(o!==t.slicingMode&&(a=e.getSliceAtPosition(t.slice)),o){case cS.I:r[0]=a,r[1]=a;break;case cS.J:r[2]=a,r[3]=a;break;case cS.K:r[4]=a,r[5]=a}return n.extentToBounds(r)},e.getBoundsForSlice=function(){let n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:t.slice,r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;const o=e.getCurrentImage();if(!o)return Fa();const a=o.getSpatialExtent(),{ijkMode:i}=e.getClosestIJKAxis();let s=n;switch(i!==t.slicingMode&&(s=e.getSliceAtPosition(n)),i){case cS.I:a[0]=s-r,a[1]=s+r;break;case cS.J:a[2]=s-r,a[3]=s+r;break;case cS.K:a[4]=s-r,a[5]=s+r}return o.extentToBounds(a)},e.intersectWithLineForPointPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=[Math.round(r.absoluteIJK[0]),Math.round(r.absoluteIJK[1]),Math.round(r.absoluteIJK[2])];return t[0]<e[0]||t[0]>e[1]||t[1]<e[2]||t[1]>e[3]||t[2]<e[4]||t[2]>e[5]?null:{t:r.t,ijk:t}}return null}(t,n,e),e.intersectWithLineForCellPicking=(t,n)=>function(e,t,n){const r=iS(e,t,n);if(r){const e=n.getCurrentImage().getExtent(),t=r.absoluteIJK,o=[Math.floor(t[0]),Math.floor(t[1]),Math.floor(t[2])];if(o[0]<e[0]||o[0]>e[1]-1||o[1]<e[2]||o[1]>e[3]-1||o[2]<e[4]||o[2]>(e[5]?e[5]-1:e[5]))return null;const a=[t[0]-o[0],t[1]-o[1],t[2]-o[2]];return{t:r.t,ijk:o,pCoords:a}}return null}(t,n,e),e.getCurrentImage=()=>e.getInputData()}const dS={slicingMode:cS.NONE,closestIJKAxis:{ijkMode:cS.NONE,flip:!1},renderToRectangle:!1,sliceAtFocalPoint:!1,preferSizeOverAccuracy:!1};function pS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,dS,n),aS(e,t,n),jt.get(e,t,["slicingMode"]),jt.setGet(e,t,["closestIJKAxis","renderToRectangle","sliceAtFocalPoint","preferSizeOverAccuracy"]),zl.implementCoincidentTopologyMethods(e,t),uS(e,t)}var fS={newInstance:jt.newInstance(pS,"vtkImageMapper"),extend:pS,...sS,...lS,...pg};const{vtkErrorMacro:gS}=jt;function mS(e,t,n,r){e.set(function(e,t){let n=0;return e.map(((e,r)=>r===n?(n+=e+1,e):e+t))}(t,n),r)}function hS(e,t){t.classHierarchy.push("vtkAppendPolyData"),e.requestData=(n,r)=>{const o=e.getNumberOfInputPorts();if(!o)return void gS("No input specified.");if(1===o)return void(r[0]=n[0]);const a=yu.newInstance();let i=0,s=0,l=1,c=1,u=0,d=0,p=0,f=0,g=!0,m=!0,h=!0;for(let e=0;e<o;e++){const t=n[e];if(!t)continue;const r=t.getPoints().getNumberOfPoints();i+=r,u+=t.getVerts().getNumberOfValues(),d+=t.getLines().getNumberOfValues(),p+=t.getStrips().getNumberOfValues(),f+=t.getPolys().getNumberOfValues(),r&&(c&&(c=0,s=t.getPoints().getDataType()),l=t.getPoints().getDataType(),s=s>l?s:l);const o=t.getPointData();o?(g=g&&null!==o.getNormals(),m=m&&null!==o.getTCoords(),h=h&&null!==o.getScalars()):(g=!1,m=!1,h=!1)}t.outputPointsPrecision===js.SINGLE?s=ys.FLOAT:t.outputPointsPrecision===js.DOUBLE&&(s=ys.DOUBLE);const v=Tc.newInstance({dataType:s});v.setNumberOfPoints(i);const y=v.getData(),T=new Uint32Array(u),b=new Uint32Array(d),x=new Uint32Array(p),C=new Uint32Array(f);let S=null,A=null,I=null;const w=n[o-1];if(g){const e=w.getPointData().getNormals();S=Ds.newInstance({numberOfComponents:3,numberOfTuples:i,size:3*i,dataType:e.getDataType(),name:e.getName()})}if(m){const e=w.getPointData().getTCoords();A=Ds.newInstance({numberOfComponents:2,numberOfTuples:i,size:2*i,dataType:e.getDataType(),name:e.getName()})}if(h){const e=w.getPointData().getScalars();I=Ds.newInstance({numberOfComponents:e.getNumberOfComponents(),numberOfTuples:i,size:i*e.getNumberOfComponents(),dataType:e.getDataType(),name:e.getName()})}i=0,u=0,d=0,p=0,f=0;for(let e=0;e<o;e++){const t=n[e];y.set(t.getPoints().getData(),3*i),mS(T,t.getVerts().getData(),i,u),u+=t.getVerts().getNumberOfValues(),mS(b,t.getLines().getData(),i,d),d+=t.getLines().getNumberOfValues(),mS(x,t.getStrips().getData(),i,p),p+=t.getStrips().getNumberOfValues(),mS(C,t.getPolys().getData(),i,f),f+=t.getPolys().getNumberOfValues();const r=t.getPointData();if(g){const e=r.getNormals();S.getData().set(e.getData(),3*i)}if(m){const e=r.getTCoords();A.getData().set(e.getData(),2*i)}if(h){const e=r.getScalars();I.getData().set(e.getData(),i*I.getNumberOfComponents())}i+=t.getPoints().getNumberOfPoints()}a.setPoints(v),a.getVerts().setData(T),a.getLines().setData(b),a.getStrips().setData(x),a.getPolys().setData(C),S&&a.getPointData().setNormals(S),A&&a.getPointData().setTCoords(A),I&&a.getPointData().setScalars(I),r[0]=a}}const vS={outputPointsPrecision:js.DEFAULT};function yS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,vS,n),jt.setGet(e,t,["outputPointsPrecision"]),jt.obj(e,t),jt.algo(e,t,1,1),hS(e,t)}var TS={newInstance:jt.newInstance(yS,"vtkAppendPolyData"),extend:yS};function bS(e,t){t.classHierarchy.push("vtkConeSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution,a=-t.height/2,i=t.resolution+1,s=4*t.resolution+1+t.resolution;let l=0;const c=jt.newTypedArray(t.pointType,3*i);let u=0;const d=new Uint32Array(s);c[0]=t.height/2,c[1]=0,c[2]=0,t.capping&&(d[u++]=t.resolution);for(let e=0;e<t.resolution;e++)l++,c[3*l+0]=a,c[3*l+1]=t.radius*Math.cos(e*o),c[3*l+2]=t.radius*Math.sin(e*o),t.capping&&(d[t.resolution-u+++1]=l);for(let e=0;e<t.resolution;e++)d[u++]=3,d[u++]=0,d[u++]=e+1,d[u++]=e+2>t.resolution?1:e+2;Uf().translate(...t.center).rotateFromDirections([1,0,0],t.direction).apply(c),r=yu.newInstance(),r.getPoints().setData(c,3),r.getPolys().setData(d,1),n[0]=r}}const xS={height:1,radius:.5,resolution:6,center:[0,0,0],direction:[1,0,0],capping:!0,pointType:"Float64Array"};function CS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,xS,n),jt.obj(e,t),jt.setGet(e,t,["height","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),bS(e,t)}var SS={newInstance:jt.newInstance(CS,"vtkConeSource"),extend:CS};function AS(e,t){t.classHierarchy.push("vtkCylinderSource"),e.requestData=function(e,n){if(t.deleted)return;let r=n[0];const o=2*Math.PI/t.resolution;let a=2*t.resolution,i=5*t.resolution;t.capping&&(a=4*t.resolution,i=7*t.resolution+2);const s=jt.newTypedArray(t.pointType,3*a);let l=0;const c=new Uint32Array(i),u=new Float32Array(3*a),d=Ds.newInstance({numberOfComponents:3,values:u,name:"Normals"}),p=new Float32Array(2*a),f=Ds.newInstance({numberOfComponents:2,values:p,name:"TCoords"}),g=[0,0,0],m=[0,0,0],h=[0,0,0],v=[0,0,0],y=[0,0],T=[0,0],b=null==t.otherRadius?t.radius:t.otherRadius;for(let e=0;e<t.resolution;e++){g[0]=Math.cos(e*o+t.initAngle),m[0]=g[0],h[0]=t.radius*g[0]+t.center[0],v[0]=h[0],y[0]=Math.abs(2*e/t.resolution-1),T[0]=y[0],h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],y[1]=0,T[1]=1,g[2]=-Math.sin(e*o+t.initAngle),m[2]=g[2],h[2]=b*g[2]+t.center[2],v[2]=h[2];const n=2*e;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*(n+1)+e]=m[e],s[3*n+e]=h[e],s[3*(n+1)+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*(n+1)+e]=T[e])}for(let e=0;e<t.resolution;e++){c[l++]=4,c[l++]=2*e,c[l++]=2*e+1;const n=(2*e+3)%(2*t.resolution);c[l++]=n,c[l++]=n-1}if(t.capping){for(let e=0;e<t.resolution;e++){h[0]=t.radius*Math.cos(e*o+t.initAngle),v[0]=h[0],y[0]=h[0],T[0]=h[0],h[0]+=t.center[0],v[0]+=t.center[0],g[1]=1,m[1]=-1,h[1]=.5*t.height+t.center[1],v[1]=-.5*t.height+t.center[1],h[2]=-b*Math.sin(e*o+t.initAngle),v[2]=h[2],y[1]=h[2],T[1]=h[2],h[2]+=t.center[2],v[2]+=t.center[2];const n=2*t.resolution+e,r=3*t.resolution+t.resolution-e-1;for(let e=0;e<3;e++)u[3*n+e]=g[e],u[3*r+e]=m[e],s[3*n+e]=h[e],s[3*r+e]=v[e],e<2&&(p[2*n+e]=y[e],p[2*r+e]=T[e])}c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=2*t.resolution+e;c[l++]=t.resolution;for(let e=0;e<t.resolution;e++)c[l++]=3*t.resolution+e}Uf().translate(...t.center).rotateFromDirections([0,1,0],t.direction).translate(...t.center.map((e=>-1*e))).apply(s),r=yu.newInstance(),r.getPoints().setData(s,3),r.getPolys().setData(c,1),r.getPointData().setNormals(d),r.getPointData().setTCoords(f),n[0]=r}}const IS={height:1,initAngle:0,radius:1,resolution:6,center:[0,0,0],direction:[0,1,0],capping:!0,pointType:"Float64Array"};function wS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,IS,n),jt.obj(e,t),jt.setGet(e,t,["height","initAngle","otherRadius","radius","resolution","capping"]),jt.setGetArray(e,t,["center","direction"],3),jt.algo(e,t,0,1),AS(e,t)}var PS={newInstance:jt.newInstance(wS,"vtkCylinderSource"),extend:wS};function OS(e,t){t.classHierarchy.push("vtkArrowSource"),e.requestData=function(e,n){if(t.deleted)return;const r=PS.newInstance({capping:!0});r.setResolution(t.shaftResolution),r.setRadius(t.shaftRadius),r.setHeight(1-t.tipLength),r.setCenter(0,.5*(1-t.tipLength),0);const o=r.getOutputData(),a=o.getPoints().getData(),i=o.getPointData().getNormals().getData();Gf().rotateZ(-90).apply(a).apply(i);const s=SS.newInstance();s.setResolution(t.tipResolution),s.setHeight(t.tipLength),s.setRadius(t.tipRadius);const l=s.getOutputData(),c=l.getPoints().getData();Uf().translate(1-.5*t.tipLength,0,0).apply(c);const u=TS.newInstance();u.setInputData(o),u.addInputData(l);const d=u.getOutputData(),p=d.getPoints().getData();Uf().translate(.5*t.tipLength-.5,0,0).apply(p),t.invert?(Uf().rotateFromDirections([1,0,0],t.direction).scale(-1,-1,-1).apply(p),n[0]=d):(Uf().rotateFromDirections([1,0,0],t.direction).scale(1,1,1).apply(p),n[0]=u.getOutputData())}}const RS={tipResolution:6,tipRadius:.1,tipLength:.35,shaftResolution:6,shaftRadius:.03,invert:!1,direction:[1,0,0],pointType:"Float64Array"};function MS(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};Object.assign(t,RS,n),jt.obj(e,t),jt.setGet(e,t,["tipResolution","tipRadius","tipLength","shaftResolution","shaftRadius","invert"]),jt.setGetArray(e,t,["direction"],3),jt.algo(e,t,0,1),OS(e,t)}var ES={newInstance:jt.newInstance(MS,"vtkArrowSource"),extend:MS};function DS(e){const t=e.getPoints().getBounds(),n=[.5*-(t[0]+t[1]),.5*-(t[2]+t[3]),.5*-(t[4]+t[5])];Gf().translate(...n).apply(e.getPoints().getData())}function VS(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];const r=e.getPoints().getBounds(),o=[0,0,0];o[t]=n?-r[2*t+1]:-r[2*t],Gf().translate(...o).apply(e.getPoints().getData())}function LS(e,t,n,r){const o=e.getPoints().getData().length,a=new Uint8ClampedArray(o);let i=0;for(;i<o;)a[i++]=t,a[i++]=n,a[i++]=r;e.getPointData().setScalars(Ds.newInstance({name:"color",numberOfComponents:3,values:a}))}function BS(e,t){t.classHierarchy.push("vtkAxesActor");const n=ic.newInstance();e.setMapper(n),e.update=()=>{let e={...t.config,...t.xConfig};const r=ES.newInstance({direction:[1,0,0],...e}).getOutputData();t.config.recenter?DS(r):VS(r,0,e.invert),LS(r,...e.color),e={...t.config,...t.yConfig};const 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