From 580e180530c7db489c01b195e872fce25d697753 Mon Sep 17 00:00:00 2001 From: "github-merge-queue[bot]" Date: Fri, 22 Nov 2024 10:57:22 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20pdoc=20from=20@=20open-atmos/P?= =?UTF-8?q?yMPDATA@a8f04f7e4fb2c41a98c6c23c1d9c6b531d6bb499=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- PyMPDATA/solver.html | 4 ++-- search.js | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/PyMPDATA/solver.html b/PyMPDATA/solver.html index 76d75015..d4643427 100644 --- a/PyMPDATA/solver.html +++ b/PyMPDATA/solver.html @@ -333,7 +333,7 @@

class_type = -jitclass.PostStepNull#7f5ff9ba8d00<> +jitclass.PostStepNull#7fd9662f0d00<>
@@ -395,7 +395,7 @@

class_type = -jitclass.PostIterNull#7f5ff274b640<> +jitclass.PostIterNull#7fd95ee5b640<>
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\n\n

Introduction

\n\n

PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA\n algorithm of Smolarkiewicz et al.\n used in geophysical fluid dynamics and beyond.\nMPDATA numerically solves generalised transport equations -\n partial differential equations used to model conservation/balance laws, scalar-transport problems,\n convection-diffusion phenomena.\nAs of the current version, PyMPDATA supports homogeneous transport\n in 1D, 2D and 3D using structured meshes, optionally\n generalised by employment of a Jacobian of coordinate transformation or a fluid density profile.\nPyMPDATA includes implementation of a set of MPDATA variants including\n the non-oscillatory option, infinite-gauge, divergent-flow, double-pass donor cell (DPDC) and\n third-order-terms options.\nIt also features support for integration of Fickian-terms in advection-diffusion\n problems using the pseudo-transport velocity approach.\nIn 2D and 3D simulations, domain-decomposition is used for multi-threaded parallelism.

\n\n

PyMPDATA is engineered purely in Python targeting both performance and usability,\n the latter encompassing research users', developers' and maintainers' perspectives.\nFrom researcher's perspective, PyMPDATA offers hassle-free installation on multitude\n of platforms including Linux, OSX and Windows, and eliminates compilation stage\n from the perspective of the user.\nFrom developers' and maintainers' perspective, PyMPDATA offers a suite of unit tests,\n multi-platform continuous integration setup,\n seamless integration with Python development aids including debuggers and profilers.

\n\n

PyMPDATA design features\n a custom-built multi-dimensional Arakawa-C grid layer allowing\n to concisely represent multi-dimensional stencil operations on both\n scalar and vector fields.\nThe grid layer is built on top of NumPy's ndarrays (using \"C\" ordering)\n using the Numba's @jit(nopython=True) functionality for high-performance array traversals.\nIt enables one to code once for multiple dimensions, and automatically\n handles (and hides from the user) any halo-filling logic related with boundary conditions.\nNumba prange() functionality is used for implementing multi-threading\n (it offers analogous functionality to OpenMP parallel loop execution directives).\nThe Numba's deviation from Python semantics rendering closure variables\n as compile-time constants\n is extensively exploited within PyMPDATA\n code base enabling the just-in-time compilation to benefit from\n information on domain extents, algorithm variant used and problem\n characteristics (e.g., coordinate transformation used, or lack thereof).

\n\n

Tutorial (in Python, Julia, Rust and Matlab)

\n\n

Options class

\n\n

The Options class\ngroups both algorithm variant options as well as some implementation-related\nflags that need to be set at the first place. All are set at the time\nof instantiation using the following keyword arguments of the constructor\n(all having default values indicated below):

\n\n
    \n
  • n_iters: int = 2: number of iterations (2 means upwind + one corrective iteration)
    • \n
  • infinite_gauge: bool = False: flag enabling the infinite-gauge option (does not maintain sign of the advected field, thus in practice implies switching flux corrected transport on)
    • \n
  • divergent_flow: bool = False: flag enabling divergent-flow terms when calculating antidiffusive velocity
    • \n
  • nonoscillatory: bool = False: flag enabling the non-oscillatory or monotone variant (a.k.a flux-corrected transport option, FCT)
    • \n
  • third_order_terms: bool = False: flag enabling third-order terms
    • \n
  • epsilon: float = 1e-15: value added to potentially zero-valued denominators
    • \n
  • non_zero_mu_coeff: bool = False: flag indicating if code for handling the Fickian term is to be optimised out
    • \n
  • DPDC: bool = False: flag enabling double-pass donor cell option (recursive pseudovelocities)
    • \n
  • dimensionally_split: bool = False: flag disabling cross-dimensional terms in antidiffusive velocity
    • \n
  • dtype: np.floating = np.float64: floating point precision
    • \n
\n\n

For a discussion of the above options, see e.g., Smolarkiewicz & Margolin 1998,\nJaruga, Arabas et al. 2015 and Olesik, Arabas et al. 2020\n(the last with examples using PyMPDATA).

\n\n

In most use cases of PyMPDATA, the first thing to do is to instantiate the\nOptions class\nwith arguments suiting the problem at hand, e.g.:

\n\n

\nJulia code (click to expand)

\n\n
\n
using Pkg\nPkg.add("PyCall")\nusing PyCall\nOptions = pyimport("PyMPDATA").Options\noptions = Options(n_iters=2)\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
Options = py.importlib.import_module('PyMPDATA').Options;\noptions = Options(pyargs('n_iters', 2));\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
use pyo3::prelude::*;\nuse pyo3::types::{IntoPyDict, PyDict, PyTuple};\n\nfn main() -> PyResult<()> {\n  Python::with_gil(|py| {\n    let options_args = [("n_iters", 2)].into_py_dict_bound(py);\n    let options = py.import_bound("PyMPDATA")?.getattr("Options")?.call((), Some(&options_args))?;\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
from PyMPDATA import Options\noptions = Options(n_iters=2)\n
\n
\n\n

\n\n

Arakawa-C grid layer and boundary conditions

\n\n

In PyMPDATA, the solution domain is assumed to extend from the\nfirst cell's boundary to the last cell's boundary (thus the\nfirst scalar field value is at $[\\Delta x/2, \\Delta y/2]$.\nThe ScalarField\nand VectorField classes implement the\nArakawa-C staggered grid logic\nin which:

\n\n
    \n
  • scalar fields are discretised onto cell centres (one value per cell),
    • \n
  • vector field components are discretised onto cell walls.
    • \n
\n\n

The schematic of the employed grid/domain layout in two dimensions is given below\n(with the Python code snippet generating the figure as a part of CI workflow):\n\"plot\"

\n\n

\nPython code (click to expand)

\n\n
\n
import numpy as np\nfrom matplotlib import pyplot\n\ndx, dy = .2, .3\ngrid = (10, 5)\n\npyplot.scatter(*np.mgrid[\n        dx / 2 : grid[0] * dx : dx,\n        dy / 2 : grid[1] * dy : dy\n    ], color='red',\n    label='scalar-field values at cell centres'\n)\npyplot.quiver(*np.mgrid[\n        0 : (grid[0]+1) * dx : dx,\n        dy / 2 : grid[1] * dy : dy\n    ], 1, 0, pivot='mid', color='green', width=.005,\n    label='vector-field x-component values at cell walls'\n)\npyplot.quiver(*np.mgrid[\n        dx / 2 : grid[0] * dx : dx,\n        0: (grid[1] + 1) * dy : dy\n    ], 0, 1, pivot='mid', color='blue', width=.005,\n    label='vector-field y-component values at cell walls'\n)\npyplot.xticks(np.linspace(0, grid[0]*dx, grid[0]+1))\npyplot.yticks(np.linspace(0, grid[1]*dy, grid[1]+1))\npyplot.title(f'staggered grid layout (grid={grid}, dx={dx}, dy={dy})')\npyplot.xlabel('x')\npyplot.ylabel('y')\npyplot.legend(bbox_to_anchor=(.1, -.1), loc='upper left', ncol=1)\npyplot.grid()\npyplot.savefig('readme_grid.png')\n
\n
\n\n

\n\n

The __init__ methods of\nScalarField\nand VectorField\nhave the following signatures:

\n\n\n\n

As an example, the code below shows how to instantiate a scalar\nand a vector field given a 2D constant-velocity problem,\nusing a grid of 24x24 points, Courant numbers of -0.5 and -0.25\nin \"x\" and \"y\" directions, respectively, with periodic boundary\nconditions and with an initial Gaussian signal in the scalar field\n(settings as in Fig. 5 in Arabas et al. 2014):

\n\n

\nJulia code (click to expand)

\n\n
\n
ScalarField = pyimport("PyMPDATA").ScalarField\nVectorField = pyimport("PyMPDATA").VectorField\nPeriodic = pyimport("PyMPDATA.boundary_conditions").Periodic\n\nnx, ny = 24, 24\nCx, Cy = -.5, -.25\nidx = CartesianIndices((nx, ny))\nhalo = options.n_halo\nadvectee = ScalarField(\n    data=exp.(\n        -(getindex.(idx, 1) .- .5 .- nx/2).^2 / (2*(nx/10)^2)\n        -(getindex.(idx, 2) .- .5 .- ny/2).^2 / (2*(ny/10)^2)\n    ),\n    halo=halo,\n    boundary_conditions=(Periodic(), Periodic())\n)\nadvector = VectorField(\n    data=(fill(Cx, (nx+1, ny)), fill(Cy, (nx, ny+1))),\n    halo=halo,\n    boundary_conditions=(Periodic(), Periodic())\n)\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
ScalarField = py.importlib.import_module('PyMPDATA').ScalarField;\nVectorField = py.importlib.import_module('PyMPDATA').VectorField;\nPeriodic = py.importlib.import_module('PyMPDATA.boundary_conditions').Periodic;\n\nnx = int32(24);\nny = int32(24);\n\nCx = -.5;\nCy = -.25;\n\n[xi, yi] = meshgrid(double(0:1:nx-1), double(0:1:ny-1));\n\nhalo = options.n_halo;\nadvectee = ScalarField(pyargs(...\n    'data', py.numpy.array(exp( ...\n        -(xi+.5-double(nx)/2).^2 / (2*(double(nx)/10)^2) ...\n        -(yi+.5-double(ny)/2).^2 / (2*(double(ny)/10)^2) ...\n    )), ...\n    'halo', halo, ...\n    'boundary_conditions', py.tuple({Periodic(), Periodic()}) ...\n));\nadvector = VectorField(pyargs(...\n    'data', py.tuple({ ...\n        Cx * py.numpy.ones(int32([nx+1 ny])), ...\n        Cy * py.numpy.ones(int32([nx ny+1])) ...\n     }), ...\n    'halo', halo, ...\n    'boundary_conditions', py.tuple({Periodic(), Periodic()}) ...\n));\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
    let vector_field = py.import_bound("PyMPDATA")?.getattr("VectorField")?;\n    let scalar_field = py.import_bound("PyMPDATA")?.getattr("ScalarField")?;\n    let periodic = py.import_bound("PyMPDATA.boundary_conditions")?.getattr("Periodic")?;\n\n    let nx_ny = [24, 24];\n    let cx_cy = [-0.5, -0.25];\n    let boundary_con = PyTuple::new_bound(py, [periodic.call0()?, periodic.call0()?]).into_any();\n    let halo = options.getattr("n_halo")?;\n\n    let indices = PyDict::new_bound(py);\n    Python::run_bound(py, &format!(r#"\nimport numpy as np\nnx, ny = {}, {}\nxi, yi = np.indices((nx, ny), dtype=float)\ndata=np.exp(\n  -(xi+.5-nx/2)**2 / (2*(ny/10)**2)\n  -(yi+.5-nx/2)**2 / (2*(ny/10)**2)\n)\n    "#, nx_ny[0], nx_ny[1]), None, Some(&indices)).unwrap();\n\n    let advectee_arg = vec![("data", indices.get_item("data")?), ("halo", Some(halo.clone())), ("boundary_conditions", Some(boundary_con))].into_py_dict_bound(py);\n    let advectee = scalar_field.call((), Some(&advectee_arg))?;\n    let full = PyDict::new_bound(py);\n    Python::run_bound(py, &format!(r#"\nimport numpy as np\nnx, ny = {}, {}\nCx, Cy = {}, {}\ndata = (np.full((nx + 1, ny), Cx), np.full((nx, ny + 1), Cy))\n    "#, nx_ny[0], nx_ny[1], cx_cy[0], cx_cy[1]), None, Some(&full)).unwrap();\n    let boundary_con = PyTuple::new_bound(py, [periodic.call0()?, periodic.call0()?]).into_any();\n    let advector_arg = vec![("data", full.get_item("data")?), ("halo", Some(halo.clone())), ("boundary_conditions", Some(boundary_con))].into_py_dict_bound(py);\n    let advector = vector_field.call((), Some(&advector_arg))?;\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
from PyMPDATA import ScalarField\nfrom PyMPDATA import VectorField\nfrom PyMPDATA.boundary_conditions import Periodic\nimport numpy as np\n\nnx, ny = 24, 24\nCx, Cy = -.5, -.25\nhalo = options.n_halo\n\nxi, yi = np.indices((nx, ny), dtype=float)\nadvectee = ScalarField(\n  data=np.exp(\n    -(xi+.5-nx/2)**2 / (2*(nx/10)**2)\n    -(yi+.5-ny/2)**2 / (2*(ny/10)**2)\n  ),\n  halo=halo,\n  boundary_conditions=(Periodic(), Periodic())\n)\nadvector = VectorField(\n  data=(np.full((nx + 1, ny), Cx), np.full((nx, ny + 1), Cy)),\n  halo=halo,\n  boundary_conditions=(Periodic(), Periodic())\n)\n
\n
\n\n

\n\n

Note that the shapes of arrays representing components\nof the velocity field are different than the shape of\nthe scalar field array due to employment of the staggered grid.

\n\n

Besides the exemplified Periodic class representing\nperiodic boundary conditions, PyMPDATA supports\nExtrapolated,\nConstant and\nPolar\nboundary conditions.

\n\n

Stepper

\n\n

The logic of the MPDATA iterative solver is represented\nin PyMPDATA by the Stepper class.

\n\n

When instantiating the Stepper, the user has a choice\nof either supplying just the number of dimensions or specialising the stepper for a given grid:\n

\nJulia code (click to expand)

\n\n
\n
Stepper = pyimport("PyMPDATA").Stepper\n\nstepper = Stepper(options=options, n_dims=2)\n
\n
\n\n

\n
\nMatlab code (click to expand)

\n\n
\n
Stepper = py.importlib.import_module('PyMPDATA').Stepper;\n\nstepper = Stepper(pyargs(...\n  'options', options, ...\n  'n_dims', int32(2) ...\n));\n
\n
\n\n

\n

\n\n

\nRust code (click to expand)

\n\n
\n
let n_dims: i32 = 2;\nlet stepper_arg = PyDict::new_bound(py);\nlet _ = PyDictMethods::set_item(&stepper_arg, "options", &options);\nlet _ = PyDictMethods::set_item(&stepper_arg, "n_dims", &n_dims);\n
\n
\n\n

\n\n

Python code (click to expand)

\n\n
\n
from PyMPDATA import Stepper\n\nstepper = Stepper(options=options, n_dims=2)\n
\n
\n\n

\nor\n
\nJulia code (click to expand)

\n\n
\n
stepper = Stepper(options=options, grid=(nx, ny))\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
stepper = Stepper(pyargs(...\n  'options', options, ...\n  'grid', py.tuple({nx, ny}) ...\n));\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
 let _stepper_arg_alternative = vec![("options", &options), ("grid", &PyTuple::new_bound(py, nx_ny).into_any())].into_py_dict_bound(py);\n let stepper_ = py.import_bound("PyMPDATA")?.getattr("Stepper")?;\n let stepper = stepper_.call((), Some(&stepper_arg))?; //or use stepper args alternative\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
stepper = Stepper(options=options, grid=(nx, ny))\n
\n
\n\n

\n\n

In the latter case, noticeably\nfaster execution can be expected, however the resultant\nstepper is less versatile as bound to the given grid size.\nIf number of dimensions is supplied only, the integration\nmight take longer, yet same instance of the\nstepper can be used for different grids.

\n\n

Since creating an instance of the Stepper class\ninvolves time-consuming compilation of the algorithm code,\nthe class is equipped with a cache logic - subsequent\ncalls with same arguments return references to previously\ninstantiated objects. Instances of Stepper contain no\nmutable data and are (thread-)safe to be reused.

\n\n

The init method of Stepper has an optional\nnon_unit_g_factor argument which is a Boolean flag\nenabling handling of the G factor term which can be used to\nrepresent coordinate transformations and/or variable fluid density.

\n\n

Optionally, the number of threads to use for domain decomposition\nin the first (non-contiguous) dimension during 2D and 3D calculations\nmay be specified using the optional n_threads argument with a\ndefault value of numba.get_num_threads(). The multi-threaded\nlogic of PyMPDATA depends thus on settings of numba, namely on the\nselected threading layer (either via NUMBA_THREADING_LAYER env\nvar or via numba.config.THREADING_LAYER) and the selected size of the\nthread pool (NUMBA_NUM_THREADS env var or numba.config.NUMBA_NUM_THREADS).

\n\n

Solver

\n\n

Instances of the Solver class are used to control\nthe integration and access solution data. During instantiation,\nadditional memory required by the solver is\nallocated according to the options provided.

\n\n

The only method of the Solver class besides the\ninit is advance(n_steps, mu_coeff, ...)\nwhich advances the solution by n_steps timesteps, optionally\ntaking into account a given diffusion coefficient mu_coeff.

\n\n

Solution state is accessible through the Solver.advectee property.\nMultiple solver[s] can share a single stepper, e.g., as exemplified in the shallow-water system solution in the examples package.

\n\n

Continuing with the above code snippets, instantiating\na solver and making 75 integration steps looks as follows:\n

\nJulia code (click to expand)

\n\n
\n
Solver = pyimport("PyMPDATA").Solver\nsolver = Solver(stepper=stepper, advectee=advectee, advector=advector)\nsolver.advance(n_steps=75)\nstate = solver.advectee.get()\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
Solver = py.importlib.import_module('PyMPDATA').Solver;\nsolver = Solver(pyargs('stepper', stepper, 'advectee', advectee, 'advector', advector));\nsolver.advance(pyargs('n_steps', 75));\nstate = solver.advectee.get();\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
    let solver_ = py.import_bound("PyMPDATA")?.getattr("Solver")?;\n    let solver = solver_.call((), Some(&vec![("stepper", stepper), ("advectee", advectee), ("advector", advector)].into_py_dict_bound(py)))?;\n    let _state_0 = solver.getattr("advectee")?.getattr("get")?.call0()?.getattr("copy")?.call0()?;\n    solver.getattr("advance")?.call((), Some(&vec![("n_steps", 75)].into_py_dict_bound(py)))?;\n    let _state = solver.getattr("advectee")?.getattr("get")?.call0()?;\n    Ok(())\n  })\n}\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
from PyMPDATA import Solver\n\nsolver = Solver(stepper=stepper, advectee=advectee, advector=advector)\nstate_0 = solver.advectee.get().copy()\nsolver.advance(n_steps=75)\nstate = solver.advectee.get()\n
\n
\n\n

\n\n

Now let's plot the results using matplotlib roughly as in Fig. 5 in Arabas et al. 2014:\n

\nPython code (click to expand)

\n\n
\n
def plot(psi, zlim, norm=None):\n    xi, yi = np.indices(psi.shape)\n    fig, ax = pyplot.subplots(subplot_kw={"projection": "3d"})\n    pyplot.gca().plot_wireframe(\n        xi+.5, yi+.5,\n        psi, color='red', linewidth=.5\n    )\n    ax.set_zlim(zlim)\n    for axis in (ax.xaxis, ax.yaxis, ax.zaxis):\n        axis.pane.fill = False\n        axis.pane.set_edgecolor('black')\n        axis.pane.set_alpha(1)\n    ax.grid(False)\n    ax.set_zticks([])\n    ax.set_xlabel('x/dx')\n    ax.set_ylabel('y/dy')\n    ax.set_proj_type('ortho')\n    cnt = ax.contourf(xi+.5, yi+.5, psi, zdir='z', offset=-1, norm=norm)\n    cbar = pyplot.colorbar(cnt, pad=.1, aspect=10, fraction=.04)\n    return cbar.norm\n\nzlim = (-1, 1)\nnorm = plot(state_0, zlim)\npyplot.savefig('readme_gauss_0.png')\nplot(state, zlim, norm)\npyplot.savefig('readme_gauss.png')\n
\n
\n\n

\n\n

\"plot\"\n\"plot\"

\n\n

Debugging

\n\n

PyMPDATA relies heavily on Numba to provide high-performance\nnumber crunching operations. Arguably, one of the key advantage\nof embracing Numba is that it can be easily switched off. This\nbrings multiple-order-of-magnitude drop in performance, yet\nit also make the entire code of the library susceptible to\ninteractive debugging, one way of enabling it is by setting the\nfollowing environment variable before importing PyMPDATA:\n

\nJulia code (click to expand)

\n\n
\n
ENV["NUMBA_DISABLE_JIT"] = "1"\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
setenv('NUMBA_DISABLE_JIT', '1');\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
import os\nos.environ["NUMBA_DISABLE_JIT"] = "1"\n
\n
\n\n

\n\n

Contributing, reporting issues, seeking support

\n\n

See README.md.

\n\n

Other open-source MPDATA implementations:

\n\n\n\n

Other Python packages for solving hyperbolic transport equations

\n\n\n"}, "PyMPDATA.boundary_conditions": {"fullname": "PyMPDATA.boundary_conditions", "modulename": "PyMPDATA.boundary_conditions", "kind": "module", "doc": "

classes representing boundary conditions

\n"}, "PyMPDATA.boundary_conditions.constant": {"fullname": "PyMPDATA.boundary_conditions.constant", "modulename": "PyMPDATA.boundary_conditions.constant", "kind": "module", "doc": "

boundary condition filling halos with a constant value

\n"}, "PyMPDATA.boundary_conditions.constant.Constant": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nPyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.constant.Constant.__init__": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.__init__", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.__init__", "kind": "function", "doc": "

\n", "signature": "(value)"}, "PyMPDATA.boundary_conditions.constant.Constant.value": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.value", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.value", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.constant.Constant.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.make_scalar", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(self, indexers, _, __, jit_flags, ___):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.constant.Constant.make_vector": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.make_vector", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(self, indexers, _, __, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.extrapolated": {"fullname": "PyMPDATA.boundary_conditions.extrapolated", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "kind": "module", "doc": "

boundary condition extrapolating values from the edge to the halo for scalars\nand returning edge-of-the-domain value for vectors (with all negative scalar values\nset to zero)

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nPyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.__init__": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.__init__", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.__init__", "kind": "function", "doc": "

\n", "signature": "(dim=-1, eps=1e-10)"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.eps": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.eps", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.eps", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.dim": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.dim", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.dim", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_scalar", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(self, indexers, halo, dtype, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_vector": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_vector", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(indexers, _, __, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.periodic": {"fullname": "PyMPDATA.boundary_conditions.periodic", "modulename": "PyMPDATA.boundary_conditions.periodic", "kind": "module", "doc": "

periodic/cyclic boundary condition logic

\n"}, "PyMPDATA.boundary_conditions.periodic.Periodic": {"fullname": "PyMPDATA.boundary_conditions.periodic.Periodic", "modulename": "PyMPDATA.boundary_conditions.periodic", "qualname": "Periodic", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nPyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.periodic.Periodic.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.periodic.Periodic.make_scalar", "modulename": "PyMPDATA.boundary_conditions.periodic", "qualname": "Periodic.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(indexers, __, ___, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.periodic.Periodic.make_vector": {"fullname": "PyMPDATA.boundary_conditions.periodic.Periodic.make_vector", "modulename": "PyMPDATA.boundary_conditions.periodic", "qualname": "Periodic.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(indexers, __, ___, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.polar": {"fullname": "PyMPDATA.boundary_conditions.polar", "modulename": "PyMPDATA.boundary_conditions.polar", "kind": "module", "doc": "

polar boundary condition for use in with spherical coordinates

\n"}, "PyMPDATA.boundary_conditions.polar.Polar": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nScalarField and VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.__init__": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.__init__", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.__init__", "kind": "function", "doc": "

\n", "signature": "(grid, longitude_idx, latitude_idx)"}, "PyMPDATA.boundary_conditions.polar.Polar.nlon": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.nlon", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.nlon", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.nlat": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.nlat", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.nlat", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.nlon_half": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.nlon_half", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.nlon_half", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.lon_idx": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.lon_idx", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.lon_idx", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.lat_idx": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.lat_idx", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.lat_idx", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.make_scalar", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(self, indexers, halo, _, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.polar.Polar.make_vector": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.make_vector", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(indexers, _, __, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.impl": {"fullname": "PyMPDATA.impl", "modulename": "PyMPDATA.impl", "kind": "module", "doc": "

package internals, if anything from within is needed to be referenced\nfrom user code, please report implementation leak into public API as an issue

\n"}, "PyMPDATA.impl.clock": {"fullname": "PyMPDATA.impl.clock", "modulename": "PyMPDATA.impl.clock", "kind": "module", "doc": "

CPU-time returning clock() function which works from within njit-ted code,\nno time unit guaranteed, returned value only for relative time measurements

\n"}, "PyMPDATA.impl.clock.clock": {"fullname": "PyMPDATA.impl.clock.clock", "modulename": "PyMPDATA.impl.clock", "qualname": "clock", "kind": "variable", "doc": "

\n", "default_value": "<_FuncPtr object>"}, "PyMPDATA.impl.domain_decomposition": {"fullname": "PyMPDATA.impl.domain_decomposition", "modulename": "PyMPDATA.impl.domain_decomposition", "kind": "module", "doc": "

logic defining domain decomposition scheme for multi-threading

\n"}, "PyMPDATA.impl.domain_decomposition.make_subdomain": {"fullname": "PyMPDATA.impl.domain_decomposition.make_subdomain", "modulename": "PyMPDATA.impl.domain_decomposition", "qualname": "make_subdomain", "kind": "function", "doc": "

returns an njit-ted function returning start-stop index tuple\nfor a given domain span, thread rank and thread-pool size

\n", "signature": "(jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.enumerations": {"fullname": "PyMPDATA.impl.enumerations", "modulename": "PyMPDATA.impl.enumerations", "kind": "module", "doc": "

common constants (mostly integer indices used for indexing tuples)

\n"}, "PyMPDATA.impl.enumerations.MAX_DIM_NUM": {"fullname": "PyMPDATA.impl.enumerations.MAX_DIM_NUM", "modulename": "PyMPDATA.impl.enumerations", "qualname": "MAX_DIM_NUM", "kind": "variable", "doc": "

\n", "default_value": "3"}, "PyMPDATA.impl.enumerations.IMPL_META_AND_DATA": {"fullname": "PyMPDATA.impl.enumerations.IMPL_META_AND_DATA", "modulename": "PyMPDATA.impl.enumerations", "qualname": "IMPL_META_AND_DATA", "kind": "variable", "doc": "

\n", "default_value": "0"}, "PyMPDATA.impl.enumerations.IMPL_BC": {"fullname": "PyMPDATA.impl.enumerations.IMPL_BC", "modulename": "PyMPDATA.impl.enumerations", "qualname": "IMPL_BC", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.enumerations.META_AND_DATA_META": {"fullname": "PyMPDATA.impl.enumerations.META_AND_DATA_META", "modulename": "PyMPDATA.impl.enumerations", "qualname": "META_AND_DATA_META", "kind": "variable", "doc": "

\n", "default_value": "0"}, "PyMPDATA.impl.enumerations.META_AND_DATA_DATA": {"fullname": "PyMPDATA.impl.enumerations.META_AND_DATA_DATA", "modulename": "PyMPDATA.impl.enumerations", "qualname": "META_AND_DATA_DATA", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.enumerations.INVALID_INDEX": {"fullname": "PyMPDATA.impl.enumerations.INVALID_INDEX", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_INDEX", "kind": "variable", "doc": "

\n", "default_value": "-44"}, "PyMPDATA.impl.enumerations.INVALID_INIT_VALUE": {"fullname": "PyMPDATA.impl.enumerations.INVALID_INIT_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_INIT_VALUE", "kind": "variable", "doc": "

\n", "default_value": "nan"}, "PyMPDATA.impl.enumerations.BUFFER_DEFAULT_VALUE": {"fullname": "PyMPDATA.impl.enumerations.BUFFER_DEFAULT_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "BUFFER_DEFAULT_VALUE", "kind": "variable", "doc": "

\n", "default_value": "nan"}, "PyMPDATA.impl.enumerations.INVALID_HALO_VALUE": {"fullname": "PyMPDATA.impl.enumerations.INVALID_HALO_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_HALO_VALUE", "kind": "variable", "doc": "

\n", "default_value": "666"}, "PyMPDATA.impl.enumerations.INVALID_NULL_VALUE": {"fullname": "PyMPDATA.impl.enumerations.INVALID_NULL_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_NULL_VALUE", "kind": "variable", "doc": "

\n", "default_value": "0.0"}, "PyMPDATA.impl.enumerations.ONE_FOR_STAGGERED_GRID": {"fullname": "PyMPDATA.impl.enumerations.ONE_FOR_STAGGERED_GRID", "modulename": "PyMPDATA.impl.enumerations", "qualname": "ONE_FOR_STAGGERED_GRID", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.field": {"fullname": "PyMPDATA.impl.field", "modulename": "PyMPDATA.impl.field", "kind": "module", "doc": "

common logic for PyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField classes

\n"}, "PyMPDATA.impl.field.Field": {"fullname": "PyMPDATA.impl.field.Field", "modulename": "PyMPDATA.impl.field", "qualname": "Field", "kind": "class", "doc": "

abstract base class for scalar and vector fields

\n"}, "PyMPDATA.impl.field.Field.__init__": {"fullname": "PyMPDATA.impl.field.Field.__init__", "modulename": "PyMPDATA.impl.field", "qualname": "Field.__init__", "kind": "function", "doc": "

\n", "signature": "(*, grid: tuple, boundary_conditions: tuple, halo: int, dtype)"}, "PyMPDATA.impl.field.Field.fill_halos": {"fullname": "PyMPDATA.impl.field.Field.fill_halos", "modulename": "PyMPDATA.impl.field", "qualname": "Field.fill_halos", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.field.Field.n_dims": {"fullname": "PyMPDATA.impl.field.Field.n_dims", "modulename": "PyMPDATA.impl.field", "qualname": "Field.n_dims", "kind": "variable", "doc": "

dimensionality

\n"}, "PyMPDATA.impl.field.Field.halo": {"fullname": "PyMPDATA.impl.field.Field.halo", "modulename": "PyMPDATA.impl.field", "qualname": "Field.halo", "kind": "variable", "doc": "

halo extent (in each dimension), for vector fields the staggered dimension\nof each component has the extent equal to halo-1

\n"}, "PyMPDATA.impl.field.Field.dtype": {"fullname": "PyMPDATA.impl.field.Field.dtype", "modulename": "PyMPDATA.impl.field", "qualname": "Field.dtype", "kind": "variable", "doc": "

data type (e.g., np.float64)

\n"}, "PyMPDATA.impl.field.Field.grid": {"fullname": "PyMPDATA.impl.field.Field.grid", "modulename": "PyMPDATA.impl.field", "qualname": "Field.grid", "kind": "variable", "doc": "

tuple defining grid geometry without halo (same interpretation as np.ndarray.shape)

\n"}, "PyMPDATA.impl.field.Field.meta": {"fullname": "PyMPDATA.impl.field.Field.meta", "modulename": "PyMPDATA.impl.field", "qualname": "Field.meta", "kind": "variable", "doc": "

tuple encoding meta data abount the scalar field (e.g., if halo was filled, ...)

\n"}, "PyMPDATA.impl.field.Field.impl": {"fullname": "PyMPDATA.impl.field.Field.impl", "modulename": "PyMPDATA.impl.field", "qualname": "Field.impl", "kind": "variable", "doc": "

tuple combining meta, data and boundary conditions - for passing to njit-ted code

\n"}, "PyMPDATA.impl.field.Field.boundary_conditions": {"fullname": "PyMPDATA.impl.field.Field.boundary_conditions", "modulename": "PyMPDATA.impl.field", "qualname": "Field.boundary_conditions", "kind": "variable", "doc": "

tuple of boundary conditions as passed to the __init__() (plus Constant(NaN) in\ndimensions higher than grid diemensionality)

\n"}, "PyMPDATA.impl.field.Field.jit_flags": {"fullname": "PyMPDATA.impl.field.Field.jit_flags", "modulename": "PyMPDATA.impl.field", "qualname": "Field.jit_flags", "kind": "variable", "doc": "

jit_flags used in the last call to assemble()

\n"}, "PyMPDATA.impl.field.Field.assemble": {"fullname": "PyMPDATA.impl.field.Field.assemble", "modulename": "PyMPDATA.impl.field", "qualname": "Field.assemble", "kind": "function", "doc": "

initialises what can be later accessed through PyMPDATA.impl.field.Field.impl property\nwith halo-filling logic njit-ted using the given traversals

\n", "signature": "(self, traversals):", "funcdef": "def"}, "PyMPDATA.impl.field.Field.make_null": {"fullname": "PyMPDATA.impl.field.Field.make_null", "modulename": "PyMPDATA.impl.field", "qualname": "Field.make_null", "kind": "function", "doc": "

\n", "signature": "(n_dims: int, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_antidiff": {"fullname": "PyMPDATA.impl.formulae_antidiff", "modulename": "PyMPDATA.impl.formulae_antidiff", "kind": "module", "doc": "

antidiffusive velocity formulae incl. divergent-flow,\nthird-order-terms, DPDC and partially also infinite-gauge logic

\n"}, "PyMPDATA.impl.formulae_antidiff.make_antidiff": {"fullname": "PyMPDATA.impl.formulae_antidiff.make_antidiff", "modulename": "PyMPDATA.impl.formulae_antidiff", "qualname": "make_antidiff", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(non_unit_g_factor, options, traversals, last_pass=False):", "funcdef": "def"}, "PyMPDATA.impl.formulae_axpy": {"fullname": "PyMPDATA.impl.formulae_axpy", "modulename": "PyMPDATA.impl.formulae_axpy", "kind": "module", "doc": "

basic a*x+y operation logic for use in Fickian term handling

\n"}, "PyMPDATA.impl.formulae_axpy.make_axpy": {"fullname": "PyMPDATA.impl.formulae_axpy.make_axpy", "modulename": "PyMPDATA.impl.formulae_axpy", "qualname": "make_axpy", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_flux": {"fullname": "PyMPDATA.impl.formulae_flux", "modulename": "PyMPDATA.impl.formulae_flux", "kind": "module", "doc": "

staggered-grid flux logic including infinite-gauge logic handling

\n"}, "PyMPDATA.impl.formulae_flux.make_flux_first_pass": {"fullname": "PyMPDATA.impl.formulae_flux.make_flux_first_pass", "modulename": "PyMPDATA.impl.formulae_flux", "qualname": "make_flux_first_pass", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_flux.make_flux_subsequent": {"fullname": "PyMPDATA.impl.formulae_flux.make_flux_subsequent", "modulename": "PyMPDATA.impl.formulae_flux", "qualname": "make_flux_subsequent", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_laplacian": {"fullname": "PyMPDATA.impl.formulae_laplacian", "modulename": "PyMPDATA.impl.formulae_laplacian", "kind": "module", "doc": "

logic for handling the Fickian term by modifying physical velocity

\n"}, "PyMPDATA.impl.formulae_laplacian.make_laplacian": {"fullname": "PyMPDATA.impl.formulae_laplacian.make_laplacian", "modulename": "PyMPDATA.impl.formulae_laplacian", "qualname": "make_laplacian", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(\tnon_unit_g_factor: bool,\toptions: PyMPDATA.options.Options,\ttraversals: PyMPDATA.impl.traversals.Traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_nonoscillatory": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "kind": "module", "doc": "

the nonoscillatory option for MPDATA as introduced in\nSmolarkiewicz & Grabowski 1990

\n"}, "PyMPDATA.impl.formulae_nonoscillatory.make_psi_extrema": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory.make_psi_extrema", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "qualname": "make_psi_extrema", "kind": "function", "doc": "

returns an njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_nonoscillatory.make_beta": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory.make_beta", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "qualname": "make_beta", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(non_unit_g_factor, options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_nonoscillatory.make_correction": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory.make_correction", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "qualname": "make_correction", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_upwind": {"fullname": "PyMPDATA.impl.formulae_upwind", "modulename": "PyMPDATA.impl.formulae_upwind", "kind": "module", "doc": "

upwind/donor-cell formula logic including G-factor handling

\n"}, "PyMPDATA.impl.formulae_upwind.make_upwind": {"fullname": "PyMPDATA.impl.formulae_upwind.make_upwind", "modulename": "PyMPDATA.impl.formulae_upwind", "qualname": "make_upwind", "kind": "function", "doc": "

returns an njit-ted function for use with given traversals

\n", "signature": "(options, non_unit_g_factor, traversals):", "funcdef": "def"}, "PyMPDATA.impl.grid": {"fullname": "PyMPDATA.impl.grid", "modulename": "PyMPDATA.impl.grid", "kind": "module", "doc": "

static (extents known to JIT) and dynamic (run-time extents) grid handling logic

\n"}, "PyMPDATA.impl.grid.make_chunk": {"fullname": "PyMPDATA.impl.grid.make_chunk", "modulename": "PyMPDATA.impl.grid", "qualname": "make_chunk", "kind": "function", "doc": "

returns an njit-ted function which returns the subdomain extent for a\ngiven thread, for static grid size no calculations are done at run-time

\n", "signature": "(span, n_threads, jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.grid.make_domain": {"fullname": "PyMPDATA.impl.grid.make_domain", "modulename": "PyMPDATA.impl.grid", "qualname": "make_domain", "kind": "function", "doc": "

returns an njit-ted function which, for static grids, returns a compile-time-constant\ngrid size, and otherwise returns the grid size encoded in the meta tuple

\n", "signature": "(grid, jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.indexers": {"fullname": "PyMPDATA.impl.indexers", "modulename": "PyMPDATA.impl.indexers", "kind": "module", "doc": "

array indexing logic for 1D, 2D and 3D staggered grids

\n"}, "PyMPDATA.impl.indexers.make_indexers": {"fullname": "PyMPDATA.impl.indexers.make_indexers", "modulename": "PyMPDATA.impl.indexers", "qualname": "make_indexers", "kind": "function", "doc": "

returns a tuple indexed by dimension (0: None, 1: 1D, ...)\nwith each element set to a namedtuple with 'at', 'atv', 'set' and 'get' functions

\n", "signature": "(jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.meta": {"fullname": "PyMPDATA.impl.meta", "modulename": "PyMPDATA.impl.meta", "kind": "module", "doc": "

constants for indexing and a factory for creating the \"meta\" tuples

\n"}, "PyMPDATA.impl.meta.META_HALO_VALID": {"fullname": "PyMPDATA.impl.meta.META_HALO_VALID", "modulename": "PyMPDATA.impl.meta", "qualname": "META_HALO_VALID", "kind": "variable", "doc": "

\n", "default_value": "0"}, "PyMPDATA.impl.meta.META_N_OUTER": {"fullname": "PyMPDATA.impl.meta.META_N_OUTER", "modulename": "PyMPDATA.impl.meta", "qualname": "META_N_OUTER", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.meta.META_N_MID3D": {"fullname": "PyMPDATA.impl.meta.META_N_MID3D", "modulename": "PyMPDATA.impl.meta", "qualname": "META_N_MID3D", "kind": "variable", "doc": "

\n", "default_value": "2"}, "PyMPDATA.impl.meta.META_N_INNER": {"fullname": "PyMPDATA.impl.meta.META_N_INNER", "modulename": "PyMPDATA.impl.meta", "qualname": "META_N_INNER", "kind": "variable", "doc": "

\n", "default_value": "3"}, "PyMPDATA.impl.meta.META_IS_NULL": {"fullname": "PyMPDATA.impl.meta.META_IS_NULL", "modulename": "PyMPDATA.impl.meta", "qualname": "META_IS_NULL", "kind": "variable", "doc": "

\n", "default_value": "4"}, "PyMPDATA.impl.meta.META_SIZE": {"fullname": "PyMPDATA.impl.meta.META_SIZE", "modulename": "PyMPDATA.impl.meta", "qualname": "META_SIZE", "kind": "variable", "doc": "

\n", "default_value": "5"}, "PyMPDATA.impl.meta.make_meta": {"fullname": "PyMPDATA.impl.meta.make_meta", "modulename": "PyMPDATA.impl.meta", "qualname": "make_meta", "kind": "function", "doc": "

returns a \"meta\" tuple for a given grid size and halo status

\n", "signature": "(halo_valid: bool, grid):", "funcdef": "def"}, "PyMPDATA.impl.traversals": {"fullname": "PyMPDATA.impl.traversals", "modulename": "PyMPDATA.impl.traversals", "kind": "module", "doc": "

staggered-grid traversals orchestration

\n"}, "PyMPDATA.impl.traversals.Traversals": {"fullname": "PyMPDATA.impl.traversals.Traversals", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals", "kind": "class", "doc": "

groups njit-ted traversals for a given grid, halo, jit_flags and threading settings

\n"}, "PyMPDATA.impl.traversals.Traversals.__init__": {"fullname": "PyMPDATA.impl.traversals.Traversals.__init__", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.__init__", "kind": "function", "doc": "

\n", "signature": "(*, grid, halo, jit_flags, n_threads, left_first: tuple, buffer_size)"}, "PyMPDATA.impl.traversals.Traversals.n_dims": {"fullname": "PyMPDATA.impl.traversals.Traversals.n_dims", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.n_dims", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.jit_flags": {"fullname": "PyMPDATA.impl.traversals.Traversals.jit_flags", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.jit_flags", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.indexers": {"fullname": "PyMPDATA.impl.traversals.Traversals.indexers", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.indexers", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.data": {"fullname": "PyMPDATA.impl.traversals.Traversals.data", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.data", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.apply_scalar": {"fullname": "PyMPDATA.impl.traversals.Traversals.apply_scalar", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.apply_scalar", "kind": "function", "doc": "

returns scalar field traversal function in two flavours:

\n\n
    \n
  • loop=True sums contributions over dimensions (used in summing upwind fluxes only)
    • \n
  • loop=False does no summing
    • \n
\n", "signature": "(self, *, loop):", "funcdef": "def"}, "PyMPDATA.impl.traversals.Traversals.apply_vector": {"fullname": "PyMPDATA.impl.traversals.Traversals.apply_vector", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.apply_vector", "kind": "function", "doc": "

returns vector field traversal function

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA.impl.traversals_common": {"fullname": "PyMPDATA.impl.traversals_common", "modulename": "PyMPDATA.impl.traversals_common", "kind": "module", "doc": "

commons for scalar and vector field traversals

\n"}, "PyMPDATA.impl.traversals_common.make_common": {"fullname": "PyMPDATA.impl.traversals_common.make_common", "modulename": "PyMPDATA.impl.traversals_common", "qualname": "make_common", "kind": "function", "doc": "

returns Numba-compiled callable producing common parameters

\n", "signature": "(jit_flags, spanner, chunker):", "funcdef": "def"}, "PyMPDATA.impl.traversals_common.make_fill_halos_loop": {"fullname": "PyMPDATA.impl.traversals_common.make_fill_halos_loop", "modulename": "PyMPDATA.impl.traversals_common", "qualname": "make_fill_halos_loop", "kind": "function", "doc": "

returns Numba-compiled halo-filling callable

\n", "signature": "(jit_flags, set_value, fill_halos):", "funcdef": "def"}, "PyMPDATA.impl.traversals_common.make_fill_halos_loop_vector": {"fullname": "PyMPDATA.impl.traversals_common.make_fill_halos_loop_vector", "modulename": "PyMPDATA.impl.traversals_common", "qualname": "make_fill_halos_loop_vector", "kind": "function", "doc": "

returns Numba-compiled halo-filling callable

\n", "signature": "(\tjit_flags,\tset_value,\tfill_halos_parallel,\tfill_halos_normal,\tdimension_index):", "funcdef": "def"}, "PyMPDATA.impl.traversals_halos_scalar": {"fullname": "PyMPDATA.impl.traversals_halos_scalar", "modulename": "PyMPDATA.impl.traversals_halos_scalar", "kind": "module", "doc": "

halo-filling logic for scalar field traversals (incl. multi-threading)

\n"}, "PyMPDATA.impl.traversals_halos_vector": {"fullname": "PyMPDATA.impl.traversals_halos_vector", "modulename": "PyMPDATA.impl.traversals_halos_vector", "kind": "module", "doc": "

halo-filling logic for vector field traversals (incl. multi-threading)

\n"}, "PyMPDATA.impl.traversals_scalar": {"fullname": "PyMPDATA.impl.traversals_scalar", "modulename": "PyMPDATA.impl.traversals_scalar", "kind": "module", "doc": "

scalar field traversals (incl. multi-threading)

\n"}, "PyMPDATA.impl.traversals_vector": {"fullname": "PyMPDATA.impl.traversals_vector", "modulename": "PyMPDATA.impl.traversals_vector", "kind": "module", "doc": "

vector field traversals (incl. multi-threading)

\n"}, "PyMPDATA.options": {"fullname": "PyMPDATA.options", "modulename": "PyMPDATA.options", "kind": "module", "doc": "

MPDATA variants, iterations, data-type and jit-flags settings

\n"}, "PyMPDATA.options.HashableDict": {"fullname": "PyMPDATA.options.HashableDict", "modulename": "PyMPDATA.options", "qualname": "HashableDict", "kind": "class", "doc": "

serialization enabler

\n", "bases": "builtins.dict"}, "PyMPDATA.options.Options": {"fullname": "PyMPDATA.options.Options", "modulename": "PyMPDATA.options", "qualname": "Options", "kind": "class", "doc": "

representation of MPDATA algorithm variant choice, for an overview of\nMPDATA options carried out using PyMPDATA, see\nOlesik et al. 2020;\nequipped with meaningful __str__ __hash__, __eq__.

\n"}, "PyMPDATA.options.Options.__init__": {"fullname": "PyMPDATA.options.Options.__init__", "modulename": "PyMPDATA.options", "qualname": "Options.__init__", "kind": "function", "doc": "

\n", "signature": "(\t*,\tn_iters: int = 2,\tinfinite_gauge: bool = False,\tdivergent_flow: bool = False,\tnonoscillatory: bool = False,\tthird_order_terms: bool = False,\tDPDC: bool = False,\tepsilon: float = 1e-15,\tnon_zero_mu_coeff: bool = False,\tdimensionally_split: bool = False,\tdtype: [<class 'numpy.float32'>, <class 'numpy.float64'>] = <class 'numpy.float64'>)"}, "PyMPDATA.options.Options.dtype": {"fullname": "PyMPDATA.options.Options.dtype", "modulename": "PyMPDATA.options", "qualname": "Options.dtype", "kind": "variable", "doc": "

data type (e.g., np.float64)

\n"}, "PyMPDATA.options.Options.n_iters": {"fullname": "PyMPDATA.options.Options.n_iters", "modulename": "PyMPDATA.options", "qualname": "Options.n_iters", "kind": "variable", "doc": "

Number of corrective iterations in the MPDATA algorithm + 1\ne.g. (1: upwind, 2: upwind + one corrective iteration, ...).\nBigger values mean smaller error, but more computational cost.\nIt does not change the order of the method.\nThe order of the method depends on the variant of antidiffusive\nvelocity used, see for example third_order_terms option.\nNote: not to confuse with n_steps in the Stepper.

\n", "annotation": ": int"}, "PyMPDATA.options.Options.infinite_gauge": {"fullname": "PyMPDATA.options.Options.infinite_gauge", "modulename": "PyMPDATA.options", "qualname": "Options.infinite_gauge", "kind": "variable", "doc": "

flag enabling the infinite-gauge option, see e.g.:\nMargolin & Shashkov, 2006,\nSmolarkiewicz & Clark, 1986

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.epsilon": {"fullname": "PyMPDATA.options.Options.epsilon", "modulename": "PyMPDATA.options", "qualname": "Options.epsilon", "kind": "variable", "doc": "

value of constant used to prevent from divisions by zero\nin statements such as (a - b)/(a + b + eps)

\n", "annotation": ": float"}, "PyMPDATA.options.Options.divergent_flow": {"fullname": "PyMPDATA.options.Options.divergent_flow", "modulename": "PyMPDATA.options", "qualname": "Options.divergent_flow", "kind": "variable", "doc": "

flag enabling the divergent-flow option, see e.g.:\nSmolarkiewicz, 1984,\nMargolin & Smolarkiewicz, 1998

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.nonoscillatory": {"fullname": "PyMPDATA.options.Options.nonoscillatory", "modulename": "PyMPDATA.options", "qualname": "Options.nonoscillatory", "kind": "variable", "doc": "

flag enabling the nonoscillatory option, see\nSmolarkiewicz & Grabowski 1990

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.third_order_terms": {"fullname": "PyMPDATA.options.Options.third_order_terms", "modulename": "PyMPDATA.options", "qualname": "Options.third_order_terms", "kind": "variable", "doc": "

flag enabling the third-order-terms option, see\nMargolin & Smolarkiewicz 1998

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.DPDC": {"fullname": "PyMPDATA.options.Options.DPDC", "modulename": "PyMPDATA.options", "qualname": "Options.DPDC", "kind": "variable", "doc": "

flag enabling the double-pass donor-cell option, see:\nBeason & Margolin, 1988,\nMargolin & Shashkov, 2006,\nMargolin & Smolarkiewicz, 1998

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.non_zero_mu_coeff": {"fullname": "PyMPDATA.options.Options.non_zero_mu_coeff", "modulename": "PyMPDATA.options", "qualname": "Options.non_zero_mu_coeff", "kind": "variable", "doc": "

flag enabling handling of Fickian diffusion term

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.dimensionally_split": {"fullname": "PyMPDATA.options.Options.dimensionally_split", "modulename": "PyMPDATA.options", "qualname": "Options.dimensionally_split", "kind": "variable", "doc": "

flag disabling cross-dimensional terms in antidiffusive velocities

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.n_halo": {"fullname": "PyMPDATA.options.Options.n_halo", "modulename": "PyMPDATA.options", "qualname": "Options.n_halo", "kind": "variable", "doc": "

Halo extent for a given options set.\nThe halo extent is the number of 'ghost layers' that need to be added\nto the outside of the domain to ensure that the MPDATA stencil operations can be\napplied to the edges of the domain.\nIt is similar to\narray padding.\nThe halo extent is determined by the options set.

\n", "annotation": ": int"}, "PyMPDATA.options.Options.jit_flags": {"fullname": "PyMPDATA.options.Options.jit_flags", "modulename": "PyMPDATA.options", "qualname": "Options.jit_flags", "kind": "variable", "doc": "

options passed to numba.njit()

\n", "annotation": ": PyMPDATA.options.HashableDict"}, "PyMPDATA.scalar_field": {"fullname": "PyMPDATA.scalar_field", "modulename": "PyMPDATA.scalar_field", "kind": "module", "doc": "

scalar field abstractions for the staggered grid

\n"}, "PyMPDATA.scalar_field.ScalarField": {"fullname": "PyMPDATA.scalar_field.ScalarField", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField", "kind": "class", "doc": "

n-dimensional scalar field including halo data, used to represent advectee, g_factor, etc.

\n", "bases": "PyMPDATA.impl.field.Field"}, "PyMPDATA.scalar_field.ScalarField.__init__": {"fullname": "PyMPDATA.scalar_field.ScalarField.__init__", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.__init__", "kind": "function", "doc": "

\n", "signature": "(data: numpy.ndarray, halo: int, boundary_conditions: tuple)"}, "PyMPDATA.scalar_field.ScalarField.data": {"fullname": "PyMPDATA.scalar_field.ScalarField.data", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.data", "kind": "variable", "doc": "

\n"}, "PyMPDATA.scalar_field.ScalarField.domain": {"fullname": "PyMPDATA.scalar_field.ScalarField.domain", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.domain", "kind": "variable", "doc": "

\n"}, "PyMPDATA.scalar_field.ScalarField.clone": {"fullname": "PyMPDATA.scalar_field.ScalarField.clone", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.clone", "kind": "function", "doc": "

returns an instance with the same dimensionality and same halo size as the argument\noptionally with a different data type

\n", "signature": "(field, dtype=None):", "funcdef": "def"}, "PyMPDATA.scalar_field.ScalarField.get": {"fullname": "PyMPDATA.scalar_field.ScalarField.get", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.get", "kind": "function", "doc": "

returns a view (not a copy) of the field data excluding halo

\n", "signature": "(self) -> numpy.ndarray:", "funcdef": "def"}, "PyMPDATA.scalar_field.ScalarField.make_null": {"fullname": "PyMPDATA.scalar_field.ScalarField.make_null", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.make_null", "kind": "function", "doc": "

returns a scalar field instance with no allocated data storage,\nsee Field._make_null other properties of the returned field

\n", "signature": "(n_dims, traversals):", "funcdef": "def"}, "PyMPDATA.solver": {"fullname": "PyMPDATA.solver", "modulename": "PyMPDATA.solver", "kind": "module", "doc": "

class grouping user-supplied stepper, fields and post-step/post-iter hooks,\nas well as self-initialised temporary storage

\n"}, "PyMPDATA.solver.PostStepNull": {"fullname": "PyMPDATA.solver.PostStepNull", "modulename": "PyMPDATA.solver", "qualname": "PostStepNull", "kind": "class", "doc": "

do-nothing version of the post-step hook

\n", "bases": "PostStepNull"}, "PyMPDATA.solver.PostStepNull.call": {"fullname": "PyMPDATA.solver.PostStepNull.call", "modulename": "PyMPDATA.solver", "qualname": "PostStepNull.call", "kind": "function", "doc": "

think of it as a __call__ method (which Numba does not allow)

\n", "signature": "(self, psi, step):", "funcdef": "def"}, "PyMPDATA.solver.PostStepNull.class_type": {"fullname": "PyMPDATA.solver.PostStepNull.class_type", "modulename": "PyMPDATA.solver", "qualname": "PostStepNull.class_type", "kind": "variable", "doc": "

\n", "default_value": "jitclass.PostStepNull#7f5ff9ba8d00<>"}, "PyMPDATA.solver.PostIterNull": {"fullname": "PyMPDATA.solver.PostIterNull", "modulename": "PyMPDATA.solver", "qualname": "PostIterNull", "kind": "class", "doc": "

do-nothing version of the post-iter hook

\n", "bases": "PostIterNull"}, "PyMPDATA.solver.PostIterNull.call": {"fullname": "PyMPDATA.solver.PostIterNull.call", "modulename": "PyMPDATA.solver", "qualname": "PostIterNull.call", "kind": "function", "doc": "

think of it as a __call__ method (which Numba does not allow)

\n", "signature": "(self, flux, g_factor, step, iteration):", "funcdef": "def"}, "PyMPDATA.solver.PostIterNull.class_type": {"fullname": "PyMPDATA.solver.PostIterNull.class_type", "modulename": "PyMPDATA.solver", "qualname": "PostIterNull.class_type", "kind": "variable", "doc": "

\n", "default_value": "jitclass.PostIterNull#7f5ff274b640<>"}, "PyMPDATA.solver.Solver": {"fullname": "PyMPDATA.solver.Solver", "modulename": "PyMPDATA.solver", "qualname": "Solver", "kind": "class", "doc": "

Solution orchestrator requiring prior instantiation of: a Stepper,\na scalar advectee field (that which is advected),\na vector advector field (that which advects),\nand optionally a scalar g_factor field (used in some cases of the advection equation).\nNote: in some cases of advection, i.e. momentum advection,\nthe advectee can act upon the advector.\nSee PyMPDATA_examples.Jarecka_et_al_2015 for an example of this.

\n"}, "PyMPDATA.solver.Solver.__init__": {"fullname": "PyMPDATA.solver.Solver.__init__", "modulename": "PyMPDATA.solver", "qualname": "Solver.__init__", "kind": "function", "doc": "

\n", "signature": "(\tstepper: PyMPDATA.stepper.Stepper,\tadvectee: PyMPDATA.scalar_field.ScalarField,\tadvector: PyMPDATA.vector_field.VectorField,\tg_factor: [<class 'PyMPDATA.scalar_field.ScalarField'>, None] = None)"}, "PyMPDATA.solver.Solver.advectee": {"fullname": "PyMPDATA.solver.Solver.advectee", "modulename": "PyMPDATA.solver", "qualname": "Solver.advectee", "kind": "variable", "doc": "

advectee scalar field (with halo), modified by advance(),\nmay be modified from user code (e.g., source-term handling)

\n", "annotation": ": PyMPDATA.scalar_field.ScalarField"}, "PyMPDATA.solver.Solver.advector": {"fullname": "PyMPDATA.solver.Solver.advector", "modulename": "PyMPDATA.solver", "qualname": "Solver.advector", "kind": "variable", "doc": "

advector vector field (with halo), unmodified by advance(),\nmay be modified from user code

\n", "annotation": ": PyMPDATA.vector_field.VectorField"}, "PyMPDATA.solver.Solver.g_factor": {"fullname": "PyMPDATA.solver.Solver.g_factor", "modulename": "PyMPDATA.solver", "qualname": "Solver.g_factor", "kind": "variable", "doc": "

G_factor field (with halo), unmodified by advance(), assumed to be constant-in-time.\nCan be used as a Jacobian for coordinate transformations,\ne.g. into spherical coordinates.\nFor this type of usage, see\nPyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14.\nCan also be used to account for spatial variability of fluid density, see\nPyMPDATA_examples.Shipway_and_Hill_2012.\ne.g. the changing density of a fluid.

\n", "annotation": ": PyMPDATA.scalar_field.ScalarField"}, "PyMPDATA.solver.Solver.advance": {"fullname": "PyMPDATA.solver.Solver.advance", "modulename": "PyMPDATA.solver", "qualname": "Solver.advance", "kind": "function", "doc": "

advances solution by n_steps steps, optionally accepts: a tuple of diffusion\ncoefficients (one value per dimension) as well as post_iter and post_step\ncallbacks expected to be numba.jitclasses with a call method, for\nsignature see PostStepNull and PostIterNull;\nreturns CPU time per timestep (units as returned by clock.clock())

\n", "signature": "(\tself,\tn_steps: int,\tmu_coeff: Optional[tuple] = None,\tpost_step=None,\tpost_iter=None):", "funcdef": "def"}, "PyMPDATA.stepper": {"fullname": "PyMPDATA.stepper", "modulename": "PyMPDATA.stepper", "kind": "module", "doc": "

MPDATA iteration logic

\n"}, "PyMPDATA.stepper.Stepper": {"fullname": "PyMPDATA.stepper.Stepper", "modulename": "PyMPDATA.stepper", "qualname": "Stepper", "kind": "class", "doc": "

MPDATA stepper specialised for given options, dimensionality and optionally grid\n(instances of Stepper can be shared among Solvers)

\n"}, "PyMPDATA.stepper.Stepper.__init__": {"fullname": "PyMPDATA.stepper.Stepper.__init__", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.__init__", "kind": "function", "doc": "

\n", "signature": "(\t*,\toptions: PyMPDATA.options.Options,\tn_dims: (<class 'int'>, None) = None,\tnon_unit_g_factor: bool = False,\tgrid: (<class 'tuple'>, None) = None,\tn_threads: (<class 'int'>, None) = None,\tleft_first: (<class 'tuple'>, None) = None,\tbuffer_size: int = 0)"}, "PyMPDATA.stepper.Stepper.options": {"fullname": "PyMPDATA.stepper.Stepper.options", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.options", "kind": "variable", "doc": "

Options instance used

\n", "annotation": ": PyMPDATA.options.Options"}, "PyMPDATA.stepper.Stepper.n_threads": {"fullname": "PyMPDATA.stepper.Stepper.n_threads", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.n_threads", "kind": "variable", "doc": "

actual n_threads used (may be different than passed to __init__ if n_dims==1\nor if on a platform where Numba does not support threading)

\n", "annotation": ": int"}, "PyMPDATA.stepper.Stepper.n_dims": {"fullname": "PyMPDATA.stepper.Stepper.n_dims", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.n_dims", "kind": "variable", "doc": "

dimensionality (1, 2 or 3)

\n", "annotation": ": int"}, "PyMPDATA.stepper.make_step_impl": {"fullname": "PyMPDATA.stepper.make_step_impl", "modulename": "PyMPDATA.stepper", "qualname": "make_step_impl", "kind": "function", "doc": "

returns (and caches) an njit-ted stepping function and a traversals pair

\n", "signature": "(\toptions,\tnon_unit_g_factor,\tgrid,\tn_threads,\tleft_first: tuple,\tbuffer_size):", "funcdef": "def"}, "PyMPDATA.vector_field": {"fullname": "PyMPDATA.vector_field", "modulename": "PyMPDATA.vector_field", "kind": "module", "doc": "

vector field abstractions for the staggered grid

\n"}, "PyMPDATA.vector_field.VectorField": {"fullname": "PyMPDATA.vector_field.VectorField", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField", "kind": "class", "doc": "

n-component n-dimensional vector field including halo data,\nused to represent the advector field

\n", "bases": "PyMPDATA.impl.field.Field"}, "PyMPDATA.vector_field.VectorField.__init__": {"fullname": "PyMPDATA.vector_field.VectorField.__init__", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.__init__", "kind": "function", "doc": "

\n", "signature": "(data: tuple, halo: int, boundary_conditions: tuple)"}, "PyMPDATA.vector_field.VectorField.data": {"fullname": "PyMPDATA.vector_field.VectorField.data", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.data", "kind": "variable", "doc": "

\n"}, "PyMPDATA.vector_field.VectorField.domain": {"fullname": "PyMPDATA.vector_field.VectorField.domain", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.domain", "kind": "variable", "doc": "

\n"}, "PyMPDATA.vector_field.VectorField.clone": {"fullname": "PyMPDATA.vector_field.VectorField.clone", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.clone", "kind": "function", "doc": "

returns a newly allocated field of the same shape, halo and boundary conditions

\n", "signature": "(field):", "funcdef": "def"}, "PyMPDATA.vector_field.VectorField.get_component": {"fullname": "PyMPDATA.vector_field.VectorField.get_component", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.get_component", "kind": "function", "doc": "

returns a view over given component of the field excluding halo

\n", "signature": "(self, i: int) -> numpy.ndarray:", "funcdef": "def"}, "PyMPDATA.vector_field.VectorField.div": {"fullname": "PyMPDATA.vector_field.VectorField.div", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.div", "kind": "function", "doc": "

returns a newly allocated scalar field (with no halo) containing the\ndivergence of the vector field computed using minimal stencil

\n", "signature": "(self, grid_step: tuple) -> PyMPDATA.scalar_field.ScalarField:", "funcdef": "def"}, "PyMPDATA.vector_field.VectorField.make_null": {"fullname": "PyMPDATA.vector_field.VectorField.make_null", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.make_null", "kind": "function", "doc": "

returns a vector field instance with no allocated data storage,\nsee Field._make_null other properties of the returned field

\n", "signature": "(n_dims, traversals):", "funcdef": "def"}, "PyMPDATA_examples": {"fullname": "PyMPDATA_examples", "modulename": "PyMPDATA_examples", "kind": "module", "doc": "

Introduction

\n\n

PyMPDATA examples are bundled with PyMPDATA and located in the examples subfolder.\nThey constitute a separate PyMPDATA_examples Python package which is also available at PyPI.\nThe examples have additional dependencies listed in PyMPDATA_examples package setup.py file.\nRunning the examples requires the PyMPDATA_examples package to be installed.

\n\n

We recommend you look through the example gallery below to see the examples in action.

\n\n

Example gallery

\n\n

Unless stated otherwise the following examples solve the basic advection equation:\n$$ \\partial_t (\\psi) + \\nabla \\cdot (u \\psi) = 0 $$

\n\n

The examples are grouped by the dimensionality of the computational grid.

\n\n

in 1D

\n\n\n\n\n \n \n\n\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n
tagslink
advection-diffusion equation
$$ \\partial_t (\\psi) + \\nabla \\cdot (u \\psi) + \\mu \\Delta (\\psi) = 0 $$		PyMPDATA_examples.advection_diffusion_1d*
Black-Scholes equation, option pricing
$$ \\frac{\\partial f}{\\partial t} + rS \\frac{\\partial f}{\\partial S} + \\frac{\\sigma^2}{2} S^2 \\frac{\\partial^2 f}{\\partial S^2} - rf = 0$$		PyMPDATA_examples.Arabas_and_Farhat_2020*
advection equation, homogeneous, several algorithm variants comparison: infinite-gauge, flux-corrected,..PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12
Size-spectral advection, particle population condensational growth, coordinate transformation
$$ \\partial_t (G \\psi) + \\nabla \\cdot (Gu \\psi) = 0 $$		PyMPDATA_examples.Olesik_et_al_2022*
advection equation, double-pass donor-cell optionPyMPDATA_examples.DPDC
\n\n

in 2D

\n\n\n\n\n \n \n\n\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n
tagslink
advection-diffusion equation
$$ \\partial_t (\\psi) + \\nabla \\cdot (u \\psi) + \\mu \\Delta (\\psi) = 0 $$		PyMPDATA_examples.advection_diffusion_2d*
\"adv-diff\"
Spectral-spatial advection, particle population condensational growth in a vertical column of air, time dependent flowPyMPDATA_examples.Shipway_and_Hill_2012
\"spectral-spatial\"
shallow-water equations
$$\\begin{eqnarray} \\partial_t h + \\partial_x (uh) + \\partial_y (vh) &=& 0~ \\\\ \\partial_t (uh) + \\partial_x ( uuh) + \\partial_y (vuh) &=& - h \\partial_x h~ \\\\ \\partial_t (vh) + \\partial_x ( uvh) + \\partial_y (vvh) &=& - h \\partial_y h~ \\end{eqnarray}$$		PyMPDATA_examples.Jarecka_et_al_2015*
advection equation, solid body rotationPyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12*
advection equation, coordinate transformation, spherical coordinates, see also examples in PyMPDATA-MPI $$ \\partial_t (G \\psi) + \\nabla \\cdot (Gu \\psi) = 0 $$PyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14
\"mpi-gif\"
advection equation, comparison against DG solution using Trixi.jlPyMPDATA_examples.trixi_comparison
\n\n

in 3D

\n\n\n\n\n \n \n\n\n\n\n \n \n\n\n \n \n\n\n
tagslink
homogeneous advection equationPyMPDATA_examples.Smolarkiewicz_1984
homogeneous advection equation, performance comparison against libmpdata++, scalability analysis in respect to threadsPyMPDATA_examples.Bartman_et_al_2022
\n\n

* - with comparison against analytic solution

\n\n

Installation

\n\n

Since the examples package includes Jupyter notebooks (and their execution requires write access), the suggested install and launch steps are:

\n\n
git clone https://github.com/open-atmos/PyMPDATA-examples.git\ncd PyMPDATA-examples\npip install -e .\njupyter-notebook\n
\n\n

Alternatively, one can also install the examples package from pypi.org by using

\n\n
pip install PyMPDATA-examples.\n
\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020", "kind": "module", "doc": "

This example implements simulations presented in the\n Arabas and Farhat 2020\n study on pricing of European and American options using MPDATA.

\n\n

Each notebook in this directory corresponds to a figure or a table in the paper.

\n\n

fig_1.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

fig_2.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

fig_3.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

tab_1.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993.c_amer": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993.c_amer", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993", "qualname": "c_amer", "kind": "function", "doc": "

\n", "signature": "(\tS: [<class 'numpy.ndarray'>, <class 'float'>],\tK: [<class 'float'>, <class 'numpy.ndarray'>],\tT: float,\tr: float,\tb: float,\tsgma: float):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993.p_amer": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993.p_amer", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Bjerksund_and_Stensland_1993", "qualname": "p_amer", "kind": "function", "doc": "

\n", "signature": "(\tS: [<class 'numpy.ndarray'>, <class 'float'>],\tK: float,\tT: float,\tr: float,\tb: float,\tsgma: float):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973.N": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973.N", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973", "qualname": "N", "kind": "function", "doc": "

\n", "signature": "(x: float):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973.c_euro": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973.c_euro", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973", "qualname": "c_euro", "kind": "function", "doc": "

\n", "signature": "(\tS: numpy.ndarray,\tK: float,\tT: float,\tr: float,\tb: float,\tsgma: float):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973.p_euro": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973.p_euro", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.Black_Scholes_1973", "qualname": "p_euro", "kind": "function", "doc": "

\n", "signature": "(\tS: numpy.ndarray,\tK: float,\tT: float,\tr: float,\tb: float,\tsgma: float):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.compute": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.compute", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3", "qualname": "compute", "kind": "function", "doc": "

\n", "signature": "(simulation):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.convergence_in_space": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.convergence_in_space", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3", "qualname": "convergence_in_space", "kind": "function", "doc": "

\n", "signature": "(num=8):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.convergence_in_time": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.convergence_in_time", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3", "qualname": "convergence_in_time", "kind": "function", "doc": "

\n", "signature": "(num=13):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.error_L2_norm": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3.error_L2_norm", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_figures_2_and_3", "qualname": "error_L2_norm", "kind": "function", "doc": "

\n", "signature": "(solvers, settings, S, nt, n_iters: int):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1.compute_row": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1.compute_row", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1", "qualname": "compute_row", "kind": "function", "doc": "

\n", "signature": "(simulations):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1.table_1_data": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1.table_1_data", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.analysis_table_1", "qualname": "table_1_data", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.colors": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.colors", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.colors", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.colors.colors": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.colors.colors", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.colors", "qualname": "colors", "kind": "variable", "doc": "

\n", "default_value": "('purple', 'teal', 'turquoise')"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.options": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.options", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.options", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.options.OPTIONS": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.options.OPTIONS", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.options", "qualname": "OPTIONS", "kind": "variable", "doc": "

\n", "default_value": "{'n_iters': 2, 'infinite_gauge': True, 'nonoscillatory': True, 'divergent_flow': True, 'third_order_terms': True, 'non_zero_mu_coeff': True}"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.__init__": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.__init__", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.__init__", "kind": "function", "doc": "

\n", "signature": "(*, n_iters: int = 2, l2_opt: int = 2, C_opt: float = 0.034)"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.S0": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.S0", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.S0", "kind": "variable", "doc": "

\n", "default_value": "55"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.T": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.T", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.T", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.amer": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.amer", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.amer", "kind": "variable", "doc": "

\n", "default_value": "False"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.S_min": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.S_min", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.S_min", "kind": "variable", "doc": "

\n", "default_value": "10"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.S_max": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.S_max", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.S_max", "kind": "variable", "doc": "

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\n", "signature": "(self, S: numpy.ndarray):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.analytical_solution": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor.Settings.analytical_solution", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup1_european_corridor", "qualname": "Settings.analytical_solution", "kind": "function", "doc": "

\n", "signature": "(self, S: numpy.ndarray):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put", "kind": "module", "doc": "

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\n", "default_value": "2"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put.Settings.T": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put.Settings.T", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.setup2_american_put", "qualname": "Settings.T", "kind": "variable", "doc": "

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\n", "signature": "(self, S: [<class 'numpy.ndarray'>, <class 'float'>], amer=True):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "kind": "module", "doc": "

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\n", "signature": "(settings)"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.settings": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.settings", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.settings", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.dt": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.dt", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.dt", "kind": "variable", "doc": "

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\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.S": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.S", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.S", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.mu_coeff": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.mu_coeff", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.mu_coeff", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.solvers": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.solvers", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.solvers", "kind": "variable", "doc": "

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\n", "signature": "(self, n_iters: int):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.terminal_value": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.terminal_value", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.terminal_value", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA_examples.Bartman_et_al_2022": {"fullname": "PyMPDATA_examples.Bartman_et_al_2022", "modulename": "PyMPDATA_examples.Bartman_et_al_2022", "kind": "module", "doc": "

This example is based on the paper:\nBartman et al. 2022.

\n\n

fig_X.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.DPDC": {"fullname": "PyMPDATA_examples.DPDC", "modulename": "PyMPDATA_examples.DPDC", "kind": "module", "doc": "

This example demonstrates the use of the Double-Pass Donor-Cell option in PyMPDATA.options.

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015", "kind": "module", "doc": "

This module showcases the PyMPDATA implementation of an MPDATA-based shallow-water equations\nsolver discussed and bencharked against analytical solutions in\nJarecka_et_al_2015.

\n\n

fig_6.ipynb:\n\"preview\n\"launch\n\"launch

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\n", "signature": "(x, y, lx, ly):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.formulae.deriv": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.formulae.deriv", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "qualname": "deriv", "kind": "function", "doc": "

return derivatives of [lambda_x, dlambda_x/dt, lambda_y, dlambda_y/dt\nfour first-order ODEs based on eq. 7 (Jarecka, Jaruga, Smolarkiewicz)

\n", "signature": "(y, _):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.formulae.d2_el_lamb_lamb_t_evol": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.formulae.d2_el_lamb_lamb_t_evol", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "qualname": "d2_el_lamb_lamb_t_evol", "kind": "function", "doc": "

solving coupled nonlinear second-order ODEs - eq. 7 (Jarecka, Jaruga, Smolarkiewicz)\nreturning array with first dim denoting time, second dim:\n[lambda_x, dot{lambda_x}, lambda_y, dot{lambda_y}

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\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.plot_output.plot_output": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.plot_output.plot_output", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.plot_output", "qualname": "plot_output", "kind": "function", "doc": "

\n", "signature": "(times, output, settings, return_data=True):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "kind": "module", "doc": "

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\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dx": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dx", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.dx", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dy": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dy", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.dy", "kind": "variable", "doc": "

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\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.ly0": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.ly0", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.ly0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.options": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.options", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.options", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.nt", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

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\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

\n", "signature": "(settings)"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.settings": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.settings", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.settings", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.advector": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.advector", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.advector", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.solvers": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.solvers", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.solvers", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.interpolate": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.interpolate", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.interpolate", "kind": "function", "doc": "

\n", "signature": "(psi, axis):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12", "kind": "module", "doc": "

This is an example based on the solid-body rotation test from\nMolenkap 1968\n(as in Fig. 12 in Jaruga et al. 2015).

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.options": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.options", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "qualname": "options", "kind": "variable", "doc": "

\n", "default_value": "{'upwind': <PyMPDATA.options.Options object>, '2+fct': <PyMPDATA.options.Options object>, '3+fct+tot': <PyMPDATA.options.Options object>, '2+fct+iga': <PyMPDATA.options.Options object>}"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.compute_panel": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.compute_panel", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "qualname": "compute_panel", "kind": "function", "doc": "

\n", "signature": "(panel):", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.fig_12_data": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.fig_12_data", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "qualname": "fig_12_data", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.grid": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.grid", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "grid", "kind": "variable", "doc": "

\n", "default_value": "(100, 100)"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "dt", "kind": "variable", "doc": "

\n", "default_value": "0.1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dx": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dx", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "dx", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dy": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dy", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "dy", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.omega": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.omega", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "omega", "kind": "variable", "doc": "

\n", "default_value": "0.1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "h", "kind": "variable", "doc": "

\n", "default_value": "4.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h0": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h0", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "h0", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.r": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.r", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "r", "kind": "variable", "doc": "

\n", "default_value": "15.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.x0": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.x0", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "x0", "kind": "variable", "doc": "

\n", "default_value": "50"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.y0": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.y0", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "y0", "kind": "variable", "doc": "

\n", "default_value": "75"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.xc": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.xc", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "xc", "kind": "variable", "doc": "

\n", "default_value": "50.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.yc": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.yc", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "yc", "kind": "variable", "doc": "

\n", "default_value": "50.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.__init__": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.__init__", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.__init__", "kind": "function", "doc": "

\n", "signature": "(n_rotations: int = 6)"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.n_rotations": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.n_rotations", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.n_rotations", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.dt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.nt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.size": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.size", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.size", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.xrange": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.xrange", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.xrange", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.yrange": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.yrange", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.yrange", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.grid": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.grid", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.grid", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.pdf": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.pdf", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.pdf", "kind": "function", "doc": "

\n", "signature": "(x, y):", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.stream_function": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.stream_function", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.stream_function", "kind": "function", "doc": "

\n", "signature": "(xX, yY):", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "qualname": "Simulation", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

\n", "signature": "(\tsettings: PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings,\toptions: PyMPDATA.options.Options)"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.mpdata": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.mpdata", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "qualname": "Simulation.mpdata", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.nt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.nt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "qualname": "Simulation.nt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.state": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.state", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "qualname": "Simulation.state", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022", "modulename": "PyMPDATA_examples.Olesik_et_al_2022", "kind": "module", "doc": "

This is a PyMPDATA implementation of 1D particle population condensational growth\nproblem with coordinate transformations, as presented in\nOlesik et al. 2022.

\n\n

demo_analytical_solution.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_make_convergences.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_make_dispersion_ratio.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_make_plots.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_wall_times.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.__init__": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.__init__", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.__init__", "kind": "function", "doc": "

\n", "signature": "(si)"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.si": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.si", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.si", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.n0": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.n0", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.n0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.kappa": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.kappa", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.kappa", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.pdf": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.pdf", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.pdf", "kind": "function", "doc": "

\n", "signature": "(self, r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis.analysis": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis.analysis", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "qualname": "analysis", "kind": "function", "doc": "

\n", "signature": "(settings, grid_layout, psi_coord, options_dict, GC_max):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis.compute_figure_data": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis.compute_figure_data", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "qualname": "compute_figure_data", "kind": "function", "doc": "

\n", "signature": "(\t*,\tnr,\tGC_max,\tpsi_coord=<PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_id object>,\tgrid_layouts=(<PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_id object>, <PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2 object>, <PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn object>),\topt_set=({'n_iters': 1},),\tmixing_ratios_g_kg=array([ 1, 2, 4, 6, 8, 10])):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis.Result": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis.Result", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "qualname": "Result", "kind": "class", "doc": "

Result(dt, out_steps, grid_layout_str, option_str, result)

\n", "bases": "Result"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis.Result.__init__": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis.Result.__init__", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "qualname": "Result.__init__", "kind": "function", "doc": "

Create new instance of Result(dt, out_steps, grid_layout_str, option_str, result)

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Alias for field number 0

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eq. 7.20 in Rogers and Yau 1989

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eq. 7.32 in Rogers and Yau 1989

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\n", "signature": "(p, qv):", "funcdef": "def"}, "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "qualname": "MPDATA", "kind": "class", "doc": "

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\n", "signature": "(nz, dt, qv_of_zZ_at_t0, g_factor_of_zZ, nr, options, activation_bc)"}, "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.t": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.t", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "qualname": "MPDATA.t", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.dt": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.dt", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "qualname": "MPDATA.dt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.fields": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.fields", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "qualname": "MPDATA.fields", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.options": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata.MPDATA.options", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.mpdata", "qualname": "MPDATA.options", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.plot": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.plot", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.plot", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.plot.plot": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.plot.plot", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.plot", "qualname": "plot", "kind": "function", "doc": "

\n", "signature": "(\tvar,\tmult,\tlabel,\toutput,\trng=None,\tthreshold=None,\tcmap='copper',\trasterized=False,\tfigsize=None):", "funcdef": "def"}, "PyMPDATA_examples.Shipway_and_Hill_2012.plot.plot_3d": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.plot.plot_3d", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.plot", "qualname": "plot_3d", "kind": "function", "doc": "

\n", "signature": "(psi, settings, options, r_min, r_max, max_height):", "funcdef": "def"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.__init__": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.__init__", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.__init__", "kind": "function", "doc": "

\n", "signature": "(\tdt: float,\tdz: float,\trhod_w_const: float,\tt_max: float = 900.0,\tnr: int = 1,\tr_min: float = nan,\tr_max: float = nan,\tp0: Optional[float] = None,\tksi_1: float = 1e-10,\tz_max: float = 3000.0,\tapprx_drhod_dz: bool = True)"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.dt", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.dz": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.dz", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.dz", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.nr": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.nr", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.nr", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.ksi_1": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.ksi_1", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.ksi_1", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.z_max": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.z_max", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.z_max", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_max": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_max", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.t_max", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.qv": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.qv", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.qv", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.rhod0": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.rhod0", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.rhod0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.thd": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.thd", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.thd", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.rhod": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.rhod", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.rhod", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_1": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_1", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.t_1", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.rhod_w": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.rhod_w", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.rhod_w", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.r_min": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.r_min", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.r_min", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.r_max": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.r_max", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.r_max", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.dr_power": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.dr_power", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.dr_power", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.z_vec": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.z_vec", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.z_vec", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.nz": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.nz", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.nz", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.nt", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984", "kind": "module", "doc": "

This is an example of 3D advection, a spherical signal revolving in a box, based on\nSmolarkiewicz, 1984.

\n\n

figs_13-14.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.__init__": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.__init__", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.__init__", "kind": "function", "doc": "

\n", "signature": "(n: int, dt: float)"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.grid": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.grid", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.grid", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dt", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.L": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.L", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.L", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dx": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dx", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.dx", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dy": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dy", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.dy", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dz": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.dz", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.dz", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.h": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.h", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.h", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.r": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.r", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.r", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.x0": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.x0", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.x0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.y0": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.y0", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.y0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.z0": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.z0", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.z0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.omega": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.omega", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.omega", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.xc": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.xc", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.xc", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.yc": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.yc", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.yc", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.zc": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.zc", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.zc", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advector": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advector", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.advector", "kind": "variable", "doc": "

constant angular velocity rotational field

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advectee": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advectee", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.advectee", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

\n", "signature": "(settings, options, static=True, n_threads=None)"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.solver": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.solver", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.solver", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self, nt):", "funcdef": "def"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.advectee": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.advectee", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.advectee", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12", "kind": "module", "doc": "

This is an example of 1D homogeneous advection test from\nSmolarkiewicz 2006, depicting infinite-gauge and\nflux-corrected transport case.

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

\"plot\"

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.__init__": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.__init__", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.__init__", "kind": "function", "doc": "

\n", "signature": "(shape: str)"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nt", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

\n", "default_value": "1600"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.dt", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nx": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nx", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.nx", "kind": "variable", "doc": "

\n", "default_value": "500"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.C": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.C", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.C", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_min": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_min", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.x_min", "kind": "variable", "doc": "

\n", "default_value": "-250"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_max": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_max", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.x_max", "kind": "variable", "doc": "

\n", "default_value": "250"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_cosine": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_cosine", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.cdf_cosine", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_rect": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_rect", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.cdf_rect", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

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This example is based on\nWilliamson and Rasch 1989.\nIt demonstrates the use of PyMPDATA.solver.g_factor\nto transform the advection problem onto a sphere.

\n\n

demo_over_the_pole.ipynb:\n\"preview\n\"launch\n\"launch

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PyMPDATA 1D advection-diffusion example with error analysis for different initial parameters.

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

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PyMPDATA 2D advection-diffusion example with gif creation.

\n\n

advection-diffusion-2d.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.trixi_comparison": {"fullname": "PyMPDATA_examples.trixi_comparison", "modulename": "PyMPDATA_examples.trixi_comparison", "kind": "module", "doc": "

This example uses a basic 2D advection test case to compare PyMPDATA\nsolution against Trixi.jl (Julia DG code)

\n\n

advection_comparison.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.utils": {"fullname": "PyMPDATA_examples.utils", "modulename": "PyMPDATA_examples.utils", "kind": "module", "doc": "

Various utility functions for PyMPDATA examples.

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\"pympdata

\n\n

Introduction

\n\n

PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA\n algorithm of Smolarkiewicz et al.\n used in geophysical fluid dynamics and beyond.\nMPDATA numerically solves generalised transport equations -\n partial differential equations used to model conservation/balance laws, scalar-transport problems,\n convection-diffusion phenomena.\nAs of the current version, PyMPDATA supports homogeneous transport\n in 1D, 2D and 3D using structured meshes, optionally\n generalised by employment of a Jacobian of coordinate transformation or a fluid density profile.\nPyMPDATA includes implementation of a set of MPDATA variants including\n the non-oscillatory option, infinite-gauge, divergent-flow, double-pass donor cell (DPDC) and\n third-order-terms options.\nIt also features support for integration of Fickian-terms in advection-diffusion\n problems using the pseudo-transport velocity approach.\nIn 2D and 3D simulations, domain-decomposition is used for multi-threaded parallelism.

\n\n

PyMPDATA is engineered purely in Python targeting both performance and usability,\n the latter encompassing research users', developers' and maintainers' perspectives.\nFrom researcher's perspective, PyMPDATA offers hassle-free installation on multitude\n of platforms including Linux, OSX and Windows, and eliminates compilation stage\n from the perspective of the user.\nFrom developers' and maintainers' perspective, PyMPDATA offers a suite of unit tests,\n multi-platform continuous integration setup,\n seamless integration with Python development aids including debuggers and profilers.

\n\n

PyMPDATA design features\n a custom-built multi-dimensional Arakawa-C grid layer allowing\n to concisely represent multi-dimensional stencil operations on both\n scalar and vector fields.\nThe grid layer is built on top of NumPy's ndarrays (using \"C\" ordering)\n using the Numba's @jit(nopython=True) functionality for high-performance array traversals.\nIt enables one to code once for multiple dimensions, and automatically\n handles (and hides from the user) any halo-filling logic related with boundary conditions.\nNumba prange() functionality is used for implementing multi-threading\n (it offers analogous functionality to OpenMP parallel loop execution directives).\nThe Numba's deviation from Python semantics rendering closure variables\n as compile-time constants\n is extensively exploited within PyMPDATA\n code base enabling the just-in-time compilation to benefit from\n information on domain extents, algorithm variant used and problem\n characteristics (e.g., coordinate transformation used, or lack thereof).

\n\n

Tutorial (in Python, Julia, Rust and Matlab)

\n\n

Options class

\n\n

The Options class\ngroups both algorithm variant options as well as some implementation-related\nflags that need to be set at the first place. All are set at the time\nof instantiation using the following keyword arguments of the constructor\n(all having default values indicated below):

\n\n
    \n
  • n_iters: int = 2: number of iterations (2 means upwind + one corrective iteration)
    • \n
  • infinite_gauge: bool = False: flag enabling the infinite-gauge option (does not maintain sign of the advected field, thus in practice implies switching flux corrected transport on)
    • \n
  • divergent_flow: bool = False: flag enabling divergent-flow terms when calculating antidiffusive velocity
    • \n
  • nonoscillatory: bool = False: flag enabling the non-oscillatory or monotone variant (a.k.a flux-corrected transport option, FCT)
    • \n
  • third_order_terms: bool = False: flag enabling third-order terms
    • \n
  • epsilon: float = 1e-15: value added to potentially zero-valued denominators
    • \n
  • non_zero_mu_coeff: bool = False: flag indicating if code for handling the Fickian term is to be optimised out
    • \n
  • DPDC: bool = False: flag enabling double-pass donor cell option (recursive pseudovelocities)
    • \n
  • dimensionally_split: bool = False: flag disabling cross-dimensional terms in antidiffusive velocity
    • \n
  • dtype: np.floating = np.float64: floating point precision
    • \n
\n\n

For a discussion of the above options, see e.g., Smolarkiewicz & Margolin 1998,\nJaruga, Arabas et al. 2015 and Olesik, Arabas et al. 2020\n(the last with examples using PyMPDATA).

\n\n

In most use cases of PyMPDATA, the first thing to do is to instantiate the\nOptions class\nwith arguments suiting the problem at hand, e.g.:

\n\n

\nJulia code (click to expand)

\n\n
\n
using Pkg\nPkg.add("PyCall")\nusing PyCall\nOptions = pyimport("PyMPDATA").Options\noptions = Options(n_iters=2)\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
Options = py.importlib.import_module('PyMPDATA').Options;\noptions = Options(pyargs('n_iters', 2));\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
use pyo3::prelude::*;\nuse pyo3::types::{IntoPyDict, PyDict, PyTuple};\n\nfn main() -> PyResult<()> {\n  Python::with_gil(|py| {\n    let options_args = [("n_iters", 2)].into_py_dict_bound(py);\n    let options = py.import_bound("PyMPDATA")?.getattr("Options")?.call((), Some(&options_args))?;\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
from PyMPDATA import Options\noptions = Options(n_iters=2)\n
\n
\n\n

\n\n

Arakawa-C grid layer and boundary conditions

\n\n

In PyMPDATA, the solution domain is assumed to extend from the\nfirst cell's boundary to the last cell's boundary (thus the\nfirst scalar field value is at $[\\Delta x/2, \\Delta y/2]$.\nThe ScalarField\nand VectorField classes implement the\nArakawa-C staggered grid logic\nin which:

\n\n
    \n
  • scalar fields are discretised onto cell centres (one value per cell),
    • \n
  • vector field components are discretised onto cell walls.
    • \n
\n\n

The schematic of the employed grid/domain layout in two dimensions is given below\n(with the Python code snippet generating the figure as a part of CI workflow):\n\"plot\"

\n\n

\nPython code (click to expand)

\n\n
\n
import numpy as np\nfrom matplotlib import pyplot\n\ndx, dy = .2, .3\ngrid = (10, 5)\n\npyplot.scatter(*np.mgrid[\n        dx / 2 : grid[0] * dx : dx,\n        dy / 2 : grid[1] * dy : dy\n    ], color='red',\n    label='scalar-field values at cell centres'\n)\npyplot.quiver(*np.mgrid[\n        0 : (grid[0]+1) * dx : dx,\n        dy / 2 : grid[1] * dy : dy\n    ], 1, 0, pivot='mid', color='green', width=.005,\n    label='vector-field x-component values at cell walls'\n)\npyplot.quiver(*np.mgrid[\n        dx / 2 : grid[0] * dx : dx,\n        0: (grid[1] + 1) * dy : dy\n    ], 0, 1, pivot='mid', color='blue', width=.005,\n    label='vector-field y-component values at cell walls'\n)\npyplot.xticks(np.linspace(0, grid[0]*dx, grid[0]+1))\npyplot.yticks(np.linspace(0, grid[1]*dy, grid[1]+1))\npyplot.title(f'staggered grid layout (grid={grid}, dx={dx}, dy={dy})')\npyplot.xlabel('x')\npyplot.ylabel('y')\npyplot.legend(bbox_to_anchor=(.1, -.1), loc='upper left', ncol=1)\npyplot.grid()\npyplot.savefig('readme_grid.png')\n
\n
\n\n

\n\n

The __init__ methods of\nScalarField\nand VectorField\nhave the following signatures:

\n\n\n\n

As an example, the code below shows how to instantiate a scalar\nand a vector field given a 2D constant-velocity problem,\nusing a grid of 24x24 points, Courant numbers of -0.5 and -0.25\nin \"x\" and \"y\" directions, respectively, with periodic boundary\nconditions and with an initial Gaussian signal in the scalar field\n(settings as in Fig. 5 in Arabas et al. 2014):

\n\n

\nJulia code (click to expand)

\n\n
\n
ScalarField = pyimport("PyMPDATA").ScalarField\nVectorField = pyimport("PyMPDATA").VectorField\nPeriodic = pyimport("PyMPDATA.boundary_conditions").Periodic\n\nnx, ny = 24, 24\nCx, Cy = -.5, -.25\nidx = CartesianIndices((nx, ny))\nhalo = options.n_halo\nadvectee = ScalarField(\n    data=exp.(\n        -(getindex.(idx, 1) .- .5 .- nx/2).^2 / (2*(nx/10)^2)\n        -(getindex.(idx, 2) .- .5 .- ny/2).^2 / (2*(ny/10)^2)\n    ),\n    halo=halo,\n    boundary_conditions=(Periodic(), Periodic())\n)\nadvector = VectorField(\n    data=(fill(Cx, (nx+1, ny)), fill(Cy, (nx, ny+1))),\n    halo=halo,\n    boundary_conditions=(Periodic(), Periodic())\n)\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
ScalarField = py.importlib.import_module('PyMPDATA').ScalarField;\nVectorField = py.importlib.import_module('PyMPDATA').VectorField;\nPeriodic = py.importlib.import_module('PyMPDATA.boundary_conditions').Periodic;\n\nnx = int32(24);\nny = int32(24);\n\nCx = -.5;\nCy = -.25;\n\n[xi, yi] = meshgrid(double(0:1:nx-1), double(0:1:ny-1));\n\nhalo = options.n_halo;\nadvectee = ScalarField(pyargs(...\n    'data', py.numpy.array(exp( ...\n        -(xi+.5-double(nx)/2).^2 / (2*(double(nx)/10)^2) ...\n        -(yi+.5-double(ny)/2).^2 / (2*(double(ny)/10)^2) ...\n    )), ...\n    'halo', halo, ...\n    'boundary_conditions', py.tuple({Periodic(), Periodic()}) ...\n));\nadvector = VectorField(pyargs(...\n    'data', py.tuple({ ...\n        Cx * py.numpy.ones(int32([nx+1 ny])), ...\n        Cy * py.numpy.ones(int32([nx ny+1])) ...\n     }), ...\n    'halo', halo, ...\n    'boundary_conditions', py.tuple({Periodic(), Periodic()}) ...\n));\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
    let vector_field = py.import_bound("PyMPDATA")?.getattr("VectorField")?;\n    let scalar_field = py.import_bound("PyMPDATA")?.getattr("ScalarField")?;\n    let periodic = py.import_bound("PyMPDATA.boundary_conditions")?.getattr("Periodic")?;\n\n    let nx_ny = [24, 24];\n    let cx_cy = [-0.5, -0.25];\n    let boundary_con = PyTuple::new_bound(py, [periodic.call0()?, periodic.call0()?]).into_any();\n    let halo = options.getattr("n_halo")?;\n\n    let indices = PyDict::new_bound(py);\n    Python::run_bound(py, &format!(r#"\nimport numpy as np\nnx, ny = {}, {}\nxi, yi = np.indices((nx, ny), dtype=float)\ndata=np.exp(\n  -(xi+.5-nx/2)**2 / (2*(ny/10)**2)\n  -(yi+.5-nx/2)**2 / (2*(ny/10)**2)\n)\n    "#, nx_ny[0], nx_ny[1]), None, Some(&indices)).unwrap();\n\n    let advectee_arg = vec![("data", indices.get_item("data")?), ("halo", Some(halo.clone())), ("boundary_conditions", Some(boundary_con))].into_py_dict_bound(py);\n    let advectee = scalar_field.call((), Some(&advectee_arg))?;\n    let full = PyDict::new_bound(py);\n    Python::run_bound(py, &format!(r#"\nimport numpy as np\nnx, ny = {}, {}\nCx, Cy = {}, {}\ndata = (np.full((nx + 1, ny), Cx), np.full((nx, ny + 1), Cy))\n    "#, nx_ny[0], nx_ny[1], cx_cy[0], cx_cy[1]), None, Some(&full)).unwrap();\n    let boundary_con = PyTuple::new_bound(py, [periodic.call0()?, periodic.call0()?]).into_any();\n    let advector_arg = vec![("data", full.get_item("data")?), ("halo", Some(halo.clone())), ("boundary_conditions", Some(boundary_con))].into_py_dict_bound(py);\n    let advector = vector_field.call((), Some(&advector_arg))?;\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
from PyMPDATA import ScalarField\nfrom PyMPDATA import VectorField\nfrom PyMPDATA.boundary_conditions import Periodic\nimport numpy as np\n\nnx, ny = 24, 24\nCx, Cy = -.5, -.25\nhalo = options.n_halo\n\nxi, yi = np.indices((nx, ny), dtype=float)\nadvectee = ScalarField(\n  data=np.exp(\n    -(xi+.5-nx/2)**2 / (2*(nx/10)**2)\n    -(yi+.5-ny/2)**2 / (2*(ny/10)**2)\n  ),\n  halo=halo,\n  boundary_conditions=(Periodic(), Periodic())\n)\nadvector = VectorField(\n  data=(np.full((nx + 1, ny), Cx), np.full((nx, ny + 1), Cy)),\n  halo=halo,\n  boundary_conditions=(Periodic(), Periodic())\n)\n
\n
\n\n

\n\n

Note that the shapes of arrays representing components\nof the velocity field are different than the shape of\nthe scalar field array due to employment of the staggered grid.

\n\n

Besides the exemplified Periodic class representing\nperiodic boundary conditions, PyMPDATA supports\nExtrapolated,\nConstant and\nPolar\nboundary conditions.

\n\n

Stepper

\n\n

The logic of the MPDATA iterative solver is represented\nin PyMPDATA by the Stepper class.

\n\n

When instantiating the Stepper, the user has a choice\nof either supplying just the number of dimensions or specialising the stepper for a given grid:\n

\nJulia code (click to expand)

\n\n
\n
Stepper = pyimport("PyMPDATA").Stepper\n\nstepper = Stepper(options=options, n_dims=2)\n
\n
\n\n

\n
\nMatlab code (click to expand)

\n\n
\n
Stepper = py.importlib.import_module('PyMPDATA').Stepper;\n\nstepper = Stepper(pyargs(...\n  'options', options, ...\n  'n_dims', int32(2) ...\n));\n
\n
\n\n

\n

\n\n

\nRust code (click to expand)

\n\n
\n
let n_dims: i32 = 2;\nlet stepper_arg = PyDict::new_bound(py);\nlet _ = PyDictMethods::set_item(&stepper_arg, "options", &options);\nlet _ = PyDictMethods::set_item(&stepper_arg, "n_dims", &n_dims);\n
\n
\n\n

\n\n

Python code (click to expand)

\n\n
\n
from PyMPDATA import Stepper\n\nstepper = Stepper(options=options, n_dims=2)\n
\n
\n\n

\nor\n
\nJulia code (click to expand)

\n\n
\n
stepper = Stepper(options=options, grid=(nx, ny))\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
stepper = Stepper(pyargs(...\n  'options', options, ...\n  'grid', py.tuple({nx, ny}) ...\n));\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
 let _stepper_arg_alternative = vec![("options", &options), ("grid", &PyTuple::new_bound(py, nx_ny).into_any())].into_py_dict_bound(py);\n let stepper_ = py.import_bound("PyMPDATA")?.getattr("Stepper")?;\n let stepper = stepper_.call((), Some(&stepper_arg))?; //or use stepper args alternative\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
stepper = Stepper(options=options, grid=(nx, ny))\n
\n
\n\n

\n\n

In the latter case, noticeably\nfaster execution can be expected, however the resultant\nstepper is less versatile as bound to the given grid size.\nIf number of dimensions is supplied only, the integration\nmight take longer, yet same instance of the\nstepper can be used for different grids.

\n\n

Since creating an instance of the Stepper class\ninvolves time-consuming compilation of the algorithm code,\nthe class is equipped with a cache logic - subsequent\ncalls with same arguments return references to previously\ninstantiated objects. Instances of Stepper contain no\nmutable data and are (thread-)safe to be reused.

\n\n

The init method of Stepper has an optional\nnon_unit_g_factor argument which is a Boolean flag\nenabling handling of the G factor term which can be used to\nrepresent coordinate transformations and/or variable fluid density.

\n\n

Optionally, the number of threads to use for domain decomposition\nin the first (non-contiguous) dimension during 2D and 3D calculations\nmay be specified using the optional n_threads argument with a\ndefault value of numba.get_num_threads(). The multi-threaded\nlogic of PyMPDATA depends thus on settings of numba, namely on the\nselected threading layer (either via NUMBA_THREADING_LAYER env\nvar or via numba.config.THREADING_LAYER) and the selected size of the\nthread pool (NUMBA_NUM_THREADS env var or numba.config.NUMBA_NUM_THREADS).

\n\n

Solver

\n\n

Instances of the Solver class are used to control\nthe integration and access solution data. During instantiation,\nadditional memory required by the solver is\nallocated according to the options provided.

\n\n

The only method of the Solver class besides the\ninit is advance(n_steps, mu_coeff, ...)\nwhich advances the solution by n_steps timesteps, optionally\ntaking into account a given diffusion coefficient mu_coeff.

\n\n

Solution state is accessible through the Solver.advectee property.\nMultiple solver[s] can share a single stepper, e.g., as exemplified in the shallow-water system solution in the examples package.

\n\n

Continuing with the above code snippets, instantiating\na solver and making 75 integration steps looks as follows:\n

\nJulia code (click to expand)

\n\n
\n
Solver = pyimport("PyMPDATA").Solver\nsolver = Solver(stepper=stepper, advectee=advectee, advector=advector)\nsolver.advance(n_steps=75)\nstate = solver.advectee.get()\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
Solver = py.importlib.import_module('PyMPDATA').Solver;\nsolver = Solver(pyargs('stepper', stepper, 'advectee', advectee, 'advector', advector));\nsolver.advance(pyargs('n_steps', 75));\nstate = solver.advectee.get();\n
\n
\n\n

\n\n

\nRust code (click to expand)

\n\n
\n
    let solver_ = py.import_bound("PyMPDATA")?.getattr("Solver")?;\n    let solver = solver_.call((), Some(&vec![("stepper", stepper), ("advectee", advectee), ("advector", advector)].into_py_dict_bound(py)))?;\n    let _state_0 = solver.getattr("advectee")?.getattr("get")?.call0()?.getattr("copy")?.call0()?;\n    solver.getattr("advance")?.call((), Some(&vec![("n_steps", 75)].into_py_dict_bound(py)))?;\n    let _state = solver.getattr("advectee")?.getattr("get")?.call0()?;\n    Ok(())\n  })\n}\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
from PyMPDATA import Solver\n\nsolver = Solver(stepper=stepper, advectee=advectee, advector=advector)\nstate_0 = solver.advectee.get().copy()\nsolver.advance(n_steps=75)\nstate = solver.advectee.get()\n
\n
\n\n

\n\n

Now let's plot the results using matplotlib roughly as in Fig. 5 in Arabas et al. 2014:\n

\nPython code (click to expand)

\n\n
\n
def plot(psi, zlim, norm=None):\n    xi, yi = np.indices(psi.shape)\n    fig, ax = pyplot.subplots(subplot_kw={"projection": "3d"})\n    pyplot.gca().plot_wireframe(\n        xi+.5, yi+.5,\n        psi, color='red', linewidth=.5\n    )\n    ax.set_zlim(zlim)\n    for axis in (ax.xaxis, ax.yaxis, ax.zaxis):\n        axis.pane.fill = False\n        axis.pane.set_edgecolor('black')\n        axis.pane.set_alpha(1)\n    ax.grid(False)\n    ax.set_zticks([])\n    ax.set_xlabel('x/dx')\n    ax.set_ylabel('y/dy')\n    ax.set_proj_type('ortho')\n    cnt = ax.contourf(xi+.5, yi+.5, psi, zdir='z', offset=-1, norm=norm)\n    cbar = pyplot.colorbar(cnt, pad=.1, aspect=10, fraction=.04)\n    return cbar.norm\n\nzlim = (-1, 1)\nnorm = plot(state_0, zlim)\npyplot.savefig('readme_gauss_0.png')\nplot(state, zlim, norm)\npyplot.savefig('readme_gauss.png')\n
\n
\n\n

\n\n

\"plot\"\n\"plot\"

\n\n

Debugging

\n\n

PyMPDATA relies heavily on Numba to provide high-performance\nnumber crunching operations. Arguably, one of the key advantage\nof embracing Numba is that it can be easily switched off. This\nbrings multiple-order-of-magnitude drop in performance, yet\nit also make the entire code of the library susceptible to\ninteractive debugging, one way of enabling it is by setting the\nfollowing environment variable before importing PyMPDATA:\n

\nJulia code (click to expand)

\n\n
\n
ENV["NUMBA_DISABLE_JIT"] = "1"\n
\n
\n\n

\n\n

\nMatlab code (click to expand)

\n\n
\n
setenv('NUMBA_DISABLE_JIT', '1');\n
\n
\n\n

\n\n

\nPython code (click to expand)

\n\n
\n
import os\nos.environ["NUMBA_DISABLE_JIT"] = "1"\n
\n
\n\n

\n\n

Contributing, reporting issues, seeking support

\n\n

See README.md.

\n\n

Other open-source MPDATA implementations:

\n\n\n\n

Other Python packages for solving hyperbolic transport equations

\n\n\n"}, "PyMPDATA.boundary_conditions": {"fullname": "PyMPDATA.boundary_conditions", "modulename": "PyMPDATA.boundary_conditions", "kind": "module", "doc": "

classes representing boundary conditions

\n"}, "PyMPDATA.boundary_conditions.constant": {"fullname": "PyMPDATA.boundary_conditions.constant", "modulename": "PyMPDATA.boundary_conditions.constant", "kind": "module", "doc": "

boundary condition filling halos with a constant value

\n"}, "PyMPDATA.boundary_conditions.constant.Constant": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nPyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.constant.Constant.__init__": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.__init__", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.__init__", "kind": "function", "doc": "

\n", "signature": "(value)"}, "PyMPDATA.boundary_conditions.constant.Constant.value": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.value", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.value", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.constant.Constant.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.make_scalar", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(self, indexers, _, __, jit_flags, ___):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.constant.Constant.make_vector": {"fullname": "PyMPDATA.boundary_conditions.constant.Constant.make_vector", "modulename": "PyMPDATA.boundary_conditions.constant", "qualname": "Constant.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(self, indexers, _, __, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.extrapolated": {"fullname": "PyMPDATA.boundary_conditions.extrapolated", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "kind": "module", "doc": "

boundary condition extrapolating values from the edge to the halo for scalars\nand returning edge-of-the-domain value for vectors (with all negative scalar values\nset to zero)

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nPyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.__init__": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.__init__", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.__init__", "kind": "function", "doc": "

\n", "signature": "(dim=-1, eps=1e-10)"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.eps": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.eps", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.eps", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.dim": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.dim", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.dim", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_scalar", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(self, indexers, halo, dtype, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_vector": {"fullname": "PyMPDATA.boundary_conditions.extrapolated.Extrapolated.make_vector", "modulename": "PyMPDATA.boundary_conditions.extrapolated", "qualname": "Extrapolated.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(indexers, _, __, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.periodic": {"fullname": "PyMPDATA.boundary_conditions.periodic", "modulename": "PyMPDATA.boundary_conditions.periodic", "kind": "module", "doc": "

periodic/cyclic boundary condition logic

\n"}, "PyMPDATA.boundary_conditions.periodic.Periodic": {"fullname": "PyMPDATA.boundary_conditions.periodic.Periodic", "modulename": "PyMPDATA.boundary_conditions.periodic", "qualname": "Periodic", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nPyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.periodic.Periodic.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.periodic.Periodic.make_scalar", "modulename": "PyMPDATA.boundary_conditions.periodic", "qualname": "Periodic.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(indexers, __, ___, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.periodic.Periodic.make_vector": {"fullname": "PyMPDATA.boundary_conditions.periodic.Periodic.make_vector", "modulename": "PyMPDATA.boundary_conditions.periodic", "qualname": "Periodic.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(indexers, __, ___, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.polar": {"fullname": "PyMPDATA.boundary_conditions.polar", "modulename": "PyMPDATA.boundary_conditions.polar", "kind": "module", "doc": "

polar boundary condition for use in with spherical coordinates

\n"}, "PyMPDATA.boundary_conditions.polar.Polar": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar", "kind": "class", "doc": "

class which instances are to be passed in boundary_conditions tuple to the\nScalarField and VectorField __init__ methods

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.__init__": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.__init__", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.__init__", "kind": "function", "doc": "

\n", "signature": "(grid, longitude_idx, latitude_idx)"}, "PyMPDATA.boundary_conditions.polar.Polar.nlon": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.nlon", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.nlon", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.nlat": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.nlat", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.nlat", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.nlon_half": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.nlon_half", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.nlon_half", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.lon_idx": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.lon_idx", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.lon_idx", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.lat_idx": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.lat_idx", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.lat_idx", "kind": "variable", "doc": "

\n"}, "PyMPDATA.boundary_conditions.polar.Polar.make_scalar": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.make_scalar", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.make_scalar", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled scalar halo-filling callable

\n", "signature": "(self, indexers, halo, _, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.boundary_conditions.polar.Polar.make_vector": {"fullname": "PyMPDATA.boundary_conditions.polar.Polar.make_vector", "modulename": "PyMPDATA.boundary_conditions.polar", "qualname": "Polar.make_vector", "kind": "function", "doc": "

returns (lru-cached) Numba-compiled vector halo-filling callable

\n", "signature": "(indexers, _, __, jit_flags, dimension_index):", "funcdef": "def"}, "PyMPDATA.impl": {"fullname": "PyMPDATA.impl", "modulename": "PyMPDATA.impl", "kind": "module", "doc": "

package internals, if anything from within is needed to be referenced\nfrom user code, please report implementation leak into public API as an issue

\n"}, "PyMPDATA.impl.clock": {"fullname": "PyMPDATA.impl.clock", "modulename": "PyMPDATA.impl.clock", "kind": "module", "doc": "

CPU-time returning clock() function which works from within njit-ted code,\nno time unit guaranteed, returned value only for relative time measurements

\n"}, "PyMPDATA.impl.clock.clock": {"fullname": "PyMPDATA.impl.clock.clock", "modulename": "PyMPDATA.impl.clock", "qualname": "clock", "kind": "variable", "doc": "

\n", "default_value": "<_FuncPtr object>"}, "PyMPDATA.impl.domain_decomposition": {"fullname": "PyMPDATA.impl.domain_decomposition", "modulename": "PyMPDATA.impl.domain_decomposition", "kind": "module", "doc": "

logic defining domain decomposition scheme for multi-threading

\n"}, "PyMPDATA.impl.domain_decomposition.make_subdomain": {"fullname": "PyMPDATA.impl.domain_decomposition.make_subdomain", "modulename": "PyMPDATA.impl.domain_decomposition", "qualname": "make_subdomain", "kind": "function", "doc": "

returns an njit-ted function returning start-stop index tuple\nfor a given domain span, thread rank and thread-pool size

\n", "signature": "(jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.enumerations": {"fullname": "PyMPDATA.impl.enumerations", "modulename": "PyMPDATA.impl.enumerations", "kind": "module", "doc": "

common constants (mostly integer indices used for indexing tuples)

\n"}, "PyMPDATA.impl.enumerations.MAX_DIM_NUM": {"fullname": "PyMPDATA.impl.enumerations.MAX_DIM_NUM", "modulename": "PyMPDATA.impl.enumerations", "qualname": "MAX_DIM_NUM", "kind": "variable", "doc": "

\n", "default_value": "3"}, "PyMPDATA.impl.enumerations.IMPL_META_AND_DATA": {"fullname": "PyMPDATA.impl.enumerations.IMPL_META_AND_DATA", "modulename": "PyMPDATA.impl.enumerations", "qualname": "IMPL_META_AND_DATA", "kind": "variable", "doc": "

\n", "default_value": "0"}, "PyMPDATA.impl.enumerations.IMPL_BC": {"fullname": "PyMPDATA.impl.enumerations.IMPL_BC", "modulename": "PyMPDATA.impl.enumerations", "qualname": "IMPL_BC", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.enumerations.META_AND_DATA_META": {"fullname": "PyMPDATA.impl.enumerations.META_AND_DATA_META", "modulename": "PyMPDATA.impl.enumerations", "qualname": "META_AND_DATA_META", "kind": "variable", "doc": "

\n", "default_value": "0"}, "PyMPDATA.impl.enumerations.META_AND_DATA_DATA": {"fullname": "PyMPDATA.impl.enumerations.META_AND_DATA_DATA", "modulename": "PyMPDATA.impl.enumerations", "qualname": "META_AND_DATA_DATA", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.enumerations.INVALID_INDEX": {"fullname": "PyMPDATA.impl.enumerations.INVALID_INDEX", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_INDEX", "kind": "variable", "doc": "

\n", "default_value": "-44"}, "PyMPDATA.impl.enumerations.INVALID_INIT_VALUE": {"fullname": "PyMPDATA.impl.enumerations.INVALID_INIT_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_INIT_VALUE", "kind": "variable", "doc": "

\n", "default_value": "nan"}, "PyMPDATA.impl.enumerations.BUFFER_DEFAULT_VALUE": {"fullname": "PyMPDATA.impl.enumerations.BUFFER_DEFAULT_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "BUFFER_DEFAULT_VALUE", "kind": "variable", "doc": "

\n", "default_value": "nan"}, "PyMPDATA.impl.enumerations.INVALID_HALO_VALUE": {"fullname": "PyMPDATA.impl.enumerations.INVALID_HALO_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_HALO_VALUE", "kind": "variable", "doc": "

\n", "default_value": "666"}, "PyMPDATA.impl.enumerations.INVALID_NULL_VALUE": {"fullname": "PyMPDATA.impl.enumerations.INVALID_NULL_VALUE", "modulename": "PyMPDATA.impl.enumerations", "qualname": "INVALID_NULL_VALUE", "kind": "variable", "doc": "

\n", "default_value": "0.0"}, "PyMPDATA.impl.enumerations.ONE_FOR_STAGGERED_GRID": {"fullname": "PyMPDATA.impl.enumerations.ONE_FOR_STAGGERED_GRID", "modulename": "PyMPDATA.impl.enumerations", "qualname": "ONE_FOR_STAGGERED_GRID", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.field": {"fullname": "PyMPDATA.impl.field", "modulename": "PyMPDATA.impl.field", "kind": "module", "doc": "

common logic for PyMPDATA.scalar_field.ScalarField and\nPyMPDATA.vector_field.VectorField classes

\n"}, "PyMPDATA.impl.field.Field": {"fullname": "PyMPDATA.impl.field.Field", "modulename": "PyMPDATA.impl.field", "qualname": "Field", "kind": "class", "doc": "

abstract base class for scalar and vector fields

\n"}, "PyMPDATA.impl.field.Field.__init__": {"fullname": "PyMPDATA.impl.field.Field.__init__", "modulename": "PyMPDATA.impl.field", "qualname": "Field.__init__", "kind": "function", "doc": "

\n", "signature": "(*, grid: tuple, boundary_conditions: tuple, halo: int, dtype)"}, "PyMPDATA.impl.field.Field.fill_halos": {"fullname": "PyMPDATA.impl.field.Field.fill_halos", "modulename": "PyMPDATA.impl.field", "qualname": "Field.fill_halos", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.field.Field.n_dims": {"fullname": "PyMPDATA.impl.field.Field.n_dims", "modulename": "PyMPDATA.impl.field", "qualname": "Field.n_dims", "kind": "variable", "doc": "

dimensionality

\n"}, "PyMPDATA.impl.field.Field.halo": {"fullname": "PyMPDATA.impl.field.Field.halo", "modulename": "PyMPDATA.impl.field", "qualname": "Field.halo", "kind": "variable", "doc": "

halo extent (in each dimension), for vector fields the staggered dimension\nof each component has the extent equal to halo-1

\n"}, "PyMPDATA.impl.field.Field.dtype": {"fullname": "PyMPDATA.impl.field.Field.dtype", "modulename": "PyMPDATA.impl.field", "qualname": "Field.dtype", "kind": "variable", "doc": "

data type (e.g., np.float64)

\n"}, "PyMPDATA.impl.field.Field.grid": {"fullname": "PyMPDATA.impl.field.Field.grid", "modulename": "PyMPDATA.impl.field", "qualname": "Field.grid", "kind": "variable", "doc": "

tuple defining grid geometry without halo (same interpretation as np.ndarray.shape)

\n"}, "PyMPDATA.impl.field.Field.meta": {"fullname": "PyMPDATA.impl.field.Field.meta", "modulename": "PyMPDATA.impl.field", "qualname": "Field.meta", "kind": "variable", "doc": "

tuple encoding meta data abount the scalar field (e.g., if halo was filled, ...)

\n"}, "PyMPDATA.impl.field.Field.impl": {"fullname": "PyMPDATA.impl.field.Field.impl", "modulename": "PyMPDATA.impl.field", "qualname": "Field.impl", "kind": "variable", "doc": "

tuple combining meta, data and boundary conditions - for passing to njit-ted code

\n"}, "PyMPDATA.impl.field.Field.boundary_conditions": {"fullname": "PyMPDATA.impl.field.Field.boundary_conditions", "modulename": "PyMPDATA.impl.field", "qualname": "Field.boundary_conditions", "kind": "variable", "doc": "

tuple of boundary conditions as passed to the __init__() (plus Constant(NaN) in\ndimensions higher than grid diemensionality)

\n"}, "PyMPDATA.impl.field.Field.jit_flags": {"fullname": "PyMPDATA.impl.field.Field.jit_flags", "modulename": "PyMPDATA.impl.field", "qualname": "Field.jit_flags", "kind": "variable", "doc": "

jit_flags used in the last call to assemble()

\n"}, "PyMPDATA.impl.field.Field.assemble": {"fullname": "PyMPDATA.impl.field.Field.assemble", "modulename": "PyMPDATA.impl.field", "qualname": "Field.assemble", "kind": "function", "doc": "

initialises what can be later accessed through PyMPDATA.impl.field.Field.impl property\nwith halo-filling logic njit-ted using the given traversals

\n", "signature": "(self, traversals):", "funcdef": "def"}, "PyMPDATA.impl.field.Field.make_null": {"fullname": "PyMPDATA.impl.field.Field.make_null", "modulename": "PyMPDATA.impl.field", "qualname": "Field.make_null", "kind": "function", "doc": "

\n", "signature": "(n_dims: int, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_antidiff": {"fullname": "PyMPDATA.impl.formulae_antidiff", "modulename": "PyMPDATA.impl.formulae_antidiff", "kind": "module", "doc": "

antidiffusive velocity formulae incl. divergent-flow,\nthird-order-terms, DPDC and partially also infinite-gauge logic

\n"}, "PyMPDATA.impl.formulae_antidiff.make_antidiff": {"fullname": "PyMPDATA.impl.formulae_antidiff.make_antidiff", "modulename": "PyMPDATA.impl.formulae_antidiff", "qualname": "make_antidiff", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(non_unit_g_factor, options, traversals, last_pass=False):", "funcdef": "def"}, "PyMPDATA.impl.formulae_axpy": {"fullname": "PyMPDATA.impl.formulae_axpy", "modulename": "PyMPDATA.impl.formulae_axpy", "kind": "module", "doc": "

basic a*x+y operation logic for use in Fickian term handling

\n"}, "PyMPDATA.impl.formulae_axpy.make_axpy": {"fullname": "PyMPDATA.impl.formulae_axpy.make_axpy", "modulename": "PyMPDATA.impl.formulae_axpy", "qualname": "make_axpy", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_flux": {"fullname": "PyMPDATA.impl.formulae_flux", "modulename": "PyMPDATA.impl.formulae_flux", "kind": "module", "doc": "

staggered-grid flux logic including infinite-gauge logic handling

\n"}, "PyMPDATA.impl.formulae_flux.make_flux_first_pass": {"fullname": "PyMPDATA.impl.formulae_flux.make_flux_first_pass", "modulename": "PyMPDATA.impl.formulae_flux", "qualname": "make_flux_first_pass", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_flux.make_flux_subsequent": {"fullname": "PyMPDATA.impl.formulae_flux.make_flux_subsequent", "modulename": "PyMPDATA.impl.formulae_flux", "qualname": "make_flux_subsequent", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_laplacian": {"fullname": "PyMPDATA.impl.formulae_laplacian", "modulename": "PyMPDATA.impl.formulae_laplacian", "kind": "module", "doc": "

logic for handling the Fickian term by modifying physical velocity

\n"}, "PyMPDATA.impl.formulae_laplacian.make_laplacian": {"fullname": "PyMPDATA.impl.formulae_laplacian.make_laplacian", "modulename": "PyMPDATA.impl.formulae_laplacian", "qualname": "make_laplacian", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(\tnon_unit_g_factor: bool,\toptions: PyMPDATA.options.Options,\ttraversals: PyMPDATA.impl.traversals.Traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_nonoscillatory": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "kind": "module", "doc": "

the nonoscillatory option for MPDATA as introduced in\nSmolarkiewicz & Grabowski 1990

\n"}, "PyMPDATA.impl.formulae_nonoscillatory.make_psi_extrema": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory.make_psi_extrema", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "qualname": "make_psi_extrema", "kind": "function", "doc": "

returns an njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_nonoscillatory.make_beta": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory.make_beta", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "qualname": "make_beta", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(non_unit_g_factor, options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_nonoscillatory.make_correction": {"fullname": "PyMPDATA.impl.formulae_nonoscillatory.make_correction", "modulename": "PyMPDATA.impl.formulae_nonoscillatory", "qualname": "make_correction", "kind": "function", "doc": "

returns njit-ted function for use with given traversals

\n", "signature": "(options, traversals):", "funcdef": "def"}, "PyMPDATA.impl.formulae_upwind": {"fullname": "PyMPDATA.impl.formulae_upwind", "modulename": "PyMPDATA.impl.formulae_upwind", "kind": "module", "doc": "

upwind/donor-cell formula logic including G-factor handling

\n"}, "PyMPDATA.impl.formulae_upwind.make_upwind": {"fullname": "PyMPDATA.impl.formulae_upwind.make_upwind", "modulename": "PyMPDATA.impl.formulae_upwind", "qualname": "make_upwind", "kind": "function", "doc": "

returns an njit-ted function for use with given traversals

\n", "signature": "(options, non_unit_g_factor, traversals):", "funcdef": "def"}, "PyMPDATA.impl.grid": {"fullname": "PyMPDATA.impl.grid", "modulename": "PyMPDATA.impl.grid", "kind": "module", "doc": "

static (extents known to JIT) and dynamic (run-time extents) grid handling logic

\n"}, "PyMPDATA.impl.grid.make_chunk": {"fullname": "PyMPDATA.impl.grid.make_chunk", "modulename": "PyMPDATA.impl.grid", "qualname": "make_chunk", "kind": "function", "doc": "

returns an njit-ted function which returns the subdomain extent for a\ngiven thread, for static grid size no calculations are done at run-time

\n", "signature": "(span, n_threads, jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.grid.make_domain": {"fullname": "PyMPDATA.impl.grid.make_domain", "modulename": "PyMPDATA.impl.grid", "qualname": "make_domain", "kind": "function", "doc": "

returns an njit-ted function which, for static grids, returns a compile-time-constant\ngrid size, and otherwise returns the grid size encoded in the meta tuple

\n", "signature": "(grid, jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.indexers": {"fullname": "PyMPDATA.impl.indexers", "modulename": "PyMPDATA.impl.indexers", "kind": "module", "doc": "

array indexing logic for 1D, 2D and 3D staggered grids

\n"}, "PyMPDATA.impl.indexers.make_indexers": {"fullname": "PyMPDATA.impl.indexers.make_indexers", "modulename": "PyMPDATA.impl.indexers", "qualname": "make_indexers", "kind": "function", "doc": "

returns a tuple indexed by dimension (0: None, 1: 1D, ...)\nwith each element set to a namedtuple with 'at', 'atv', 'set' and 'get' functions

\n", "signature": "(jit_flags):", "funcdef": "def"}, "PyMPDATA.impl.meta": {"fullname": "PyMPDATA.impl.meta", "modulename": "PyMPDATA.impl.meta", "kind": "module", "doc": "

constants for indexing and a factory for creating the \"meta\" tuples

\n"}, "PyMPDATA.impl.meta.META_HALO_VALID": {"fullname": "PyMPDATA.impl.meta.META_HALO_VALID", "modulename": "PyMPDATA.impl.meta", "qualname": "META_HALO_VALID", "kind": "variable", "doc": "

\n", "default_value": "0"}, "PyMPDATA.impl.meta.META_N_OUTER": {"fullname": "PyMPDATA.impl.meta.META_N_OUTER", "modulename": "PyMPDATA.impl.meta", "qualname": "META_N_OUTER", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA.impl.meta.META_N_MID3D": {"fullname": "PyMPDATA.impl.meta.META_N_MID3D", "modulename": "PyMPDATA.impl.meta", "qualname": "META_N_MID3D", "kind": "variable", "doc": "

\n", "default_value": "2"}, "PyMPDATA.impl.meta.META_N_INNER": {"fullname": "PyMPDATA.impl.meta.META_N_INNER", "modulename": "PyMPDATA.impl.meta", "qualname": "META_N_INNER", "kind": "variable", "doc": "

\n", "default_value": "3"}, "PyMPDATA.impl.meta.META_IS_NULL": {"fullname": "PyMPDATA.impl.meta.META_IS_NULL", "modulename": "PyMPDATA.impl.meta", "qualname": "META_IS_NULL", "kind": "variable", "doc": "

\n", "default_value": "4"}, "PyMPDATA.impl.meta.META_SIZE": {"fullname": "PyMPDATA.impl.meta.META_SIZE", "modulename": "PyMPDATA.impl.meta", "qualname": "META_SIZE", "kind": "variable", "doc": "

\n", "default_value": "5"}, "PyMPDATA.impl.meta.make_meta": {"fullname": "PyMPDATA.impl.meta.make_meta", "modulename": "PyMPDATA.impl.meta", "qualname": "make_meta", "kind": "function", "doc": "

returns a \"meta\" tuple for a given grid size and halo status

\n", "signature": "(halo_valid: bool, grid):", "funcdef": "def"}, "PyMPDATA.impl.traversals": {"fullname": "PyMPDATA.impl.traversals", "modulename": "PyMPDATA.impl.traversals", "kind": "module", "doc": "

staggered-grid traversals orchestration

\n"}, "PyMPDATA.impl.traversals.Traversals": {"fullname": "PyMPDATA.impl.traversals.Traversals", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals", "kind": "class", "doc": "

groups njit-ted traversals for a given grid, halo, jit_flags and threading settings

\n"}, "PyMPDATA.impl.traversals.Traversals.__init__": {"fullname": "PyMPDATA.impl.traversals.Traversals.__init__", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.__init__", "kind": "function", "doc": "

\n", "signature": "(*, grid, halo, jit_flags, n_threads, left_first: tuple, buffer_size)"}, "PyMPDATA.impl.traversals.Traversals.n_dims": {"fullname": "PyMPDATA.impl.traversals.Traversals.n_dims", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.n_dims", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.jit_flags": {"fullname": "PyMPDATA.impl.traversals.Traversals.jit_flags", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.jit_flags", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.indexers": {"fullname": "PyMPDATA.impl.traversals.Traversals.indexers", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.indexers", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.data": {"fullname": "PyMPDATA.impl.traversals.Traversals.data", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.data", "kind": "variable", "doc": "

\n"}, "PyMPDATA.impl.traversals.Traversals.apply_scalar": {"fullname": "PyMPDATA.impl.traversals.Traversals.apply_scalar", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.apply_scalar", "kind": "function", "doc": "

returns scalar field traversal function in two flavours:

\n\n
    \n
  • loop=True sums contributions over dimensions (used in summing upwind fluxes only)
    • \n
  • loop=False does no summing
    • \n
\n", "signature": "(self, *, loop):", "funcdef": "def"}, "PyMPDATA.impl.traversals.Traversals.apply_vector": {"fullname": "PyMPDATA.impl.traversals.Traversals.apply_vector", "modulename": "PyMPDATA.impl.traversals", "qualname": "Traversals.apply_vector", "kind": "function", "doc": "

returns vector field traversal function

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA.impl.traversals_common": {"fullname": "PyMPDATA.impl.traversals_common", "modulename": "PyMPDATA.impl.traversals_common", "kind": "module", "doc": "

commons for scalar and vector field traversals

\n"}, "PyMPDATA.impl.traversals_common.make_common": {"fullname": "PyMPDATA.impl.traversals_common.make_common", "modulename": "PyMPDATA.impl.traversals_common", "qualname": "make_common", "kind": "function", "doc": "

returns Numba-compiled callable producing common parameters

\n", "signature": "(jit_flags, spanner, chunker):", "funcdef": "def"}, "PyMPDATA.impl.traversals_common.make_fill_halos_loop": {"fullname": "PyMPDATA.impl.traversals_common.make_fill_halos_loop", "modulename": "PyMPDATA.impl.traversals_common", "qualname": "make_fill_halos_loop", "kind": "function", "doc": "

returns Numba-compiled halo-filling callable

\n", "signature": "(jit_flags, set_value, fill_halos):", "funcdef": "def"}, "PyMPDATA.impl.traversals_common.make_fill_halos_loop_vector": {"fullname": "PyMPDATA.impl.traversals_common.make_fill_halos_loop_vector", "modulename": "PyMPDATA.impl.traversals_common", "qualname": "make_fill_halos_loop_vector", "kind": "function", "doc": "

returns Numba-compiled halo-filling callable

\n", "signature": "(\tjit_flags,\tset_value,\tfill_halos_parallel,\tfill_halos_normal,\tdimension_index):", "funcdef": "def"}, "PyMPDATA.impl.traversals_halos_scalar": {"fullname": "PyMPDATA.impl.traversals_halos_scalar", "modulename": "PyMPDATA.impl.traversals_halos_scalar", "kind": "module", "doc": "

halo-filling logic for scalar field traversals (incl. multi-threading)

\n"}, "PyMPDATA.impl.traversals_halos_vector": {"fullname": "PyMPDATA.impl.traversals_halos_vector", "modulename": "PyMPDATA.impl.traversals_halos_vector", "kind": "module", "doc": "

halo-filling logic for vector field traversals (incl. multi-threading)

\n"}, "PyMPDATA.impl.traversals_scalar": {"fullname": "PyMPDATA.impl.traversals_scalar", "modulename": "PyMPDATA.impl.traversals_scalar", "kind": "module", "doc": "

scalar field traversals (incl. multi-threading)

\n"}, "PyMPDATA.impl.traversals_vector": {"fullname": "PyMPDATA.impl.traversals_vector", "modulename": "PyMPDATA.impl.traversals_vector", "kind": "module", "doc": "

vector field traversals (incl. multi-threading)

\n"}, "PyMPDATA.options": {"fullname": "PyMPDATA.options", "modulename": "PyMPDATA.options", "kind": "module", "doc": "

MPDATA variants, iterations, data-type and jit-flags settings

\n"}, "PyMPDATA.options.HashableDict": {"fullname": "PyMPDATA.options.HashableDict", "modulename": "PyMPDATA.options", "qualname": "HashableDict", "kind": "class", "doc": "

serialization enabler

\n", "bases": "builtins.dict"}, "PyMPDATA.options.Options": {"fullname": "PyMPDATA.options.Options", "modulename": "PyMPDATA.options", "qualname": "Options", "kind": "class", "doc": "

representation of MPDATA algorithm variant choice, for an overview of\nMPDATA options carried out using PyMPDATA, see\nOlesik et al. 2020;\nequipped with meaningful __str__ __hash__, __eq__.

\n"}, "PyMPDATA.options.Options.__init__": {"fullname": "PyMPDATA.options.Options.__init__", "modulename": "PyMPDATA.options", "qualname": "Options.__init__", "kind": "function", "doc": "

\n", "signature": "(\t*,\tn_iters: int = 2,\tinfinite_gauge: bool = False,\tdivergent_flow: bool = False,\tnonoscillatory: bool = False,\tthird_order_terms: bool = False,\tDPDC: bool = False,\tepsilon: float = 1e-15,\tnon_zero_mu_coeff: bool = False,\tdimensionally_split: bool = False,\tdtype: [<class 'numpy.float32'>, <class 'numpy.float64'>] = <class 'numpy.float64'>)"}, "PyMPDATA.options.Options.dtype": {"fullname": "PyMPDATA.options.Options.dtype", "modulename": "PyMPDATA.options", "qualname": "Options.dtype", "kind": "variable", "doc": "

data type (e.g., np.float64)

\n"}, "PyMPDATA.options.Options.n_iters": {"fullname": "PyMPDATA.options.Options.n_iters", "modulename": "PyMPDATA.options", "qualname": "Options.n_iters", "kind": "variable", "doc": "

Number of corrective iterations in the MPDATA algorithm + 1\ne.g. (1: upwind, 2: upwind + one corrective iteration, ...).\nBigger values mean smaller error, but more computational cost.\nIt does not change the order of the method.\nThe order of the method depends on the variant of antidiffusive\nvelocity used, see for example third_order_terms option.\nNote: not to confuse with n_steps in the Stepper.

\n", "annotation": ": int"}, "PyMPDATA.options.Options.infinite_gauge": {"fullname": "PyMPDATA.options.Options.infinite_gauge", "modulename": "PyMPDATA.options", "qualname": "Options.infinite_gauge", "kind": "variable", "doc": "

flag enabling the infinite-gauge option, see e.g.:\nMargolin & Shashkov, 2006,\nSmolarkiewicz & Clark, 1986

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.epsilon": {"fullname": "PyMPDATA.options.Options.epsilon", "modulename": "PyMPDATA.options", "qualname": "Options.epsilon", "kind": "variable", "doc": "

value of constant used to prevent from divisions by zero\nin statements such as (a - b)/(a + b + eps)

\n", "annotation": ": float"}, "PyMPDATA.options.Options.divergent_flow": {"fullname": "PyMPDATA.options.Options.divergent_flow", "modulename": "PyMPDATA.options", "qualname": "Options.divergent_flow", "kind": "variable", "doc": "

flag enabling the divergent-flow option, see e.g.:\nSmolarkiewicz, 1984,\nMargolin & Smolarkiewicz, 1998

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.nonoscillatory": {"fullname": "PyMPDATA.options.Options.nonoscillatory", "modulename": "PyMPDATA.options", "qualname": "Options.nonoscillatory", "kind": "variable", "doc": "

flag enabling the nonoscillatory option, see\nSmolarkiewicz & Grabowski 1990

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.third_order_terms": {"fullname": "PyMPDATA.options.Options.third_order_terms", "modulename": "PyMPDATA.options", "qualname": "Options.third_order_terms", "kind": "variable", "doc": "

flag enabling the third-order-terms option, see\nMargolin & Smolarkiewicz 1998

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.DPDC": {"fullname": "PyMPDATA.options.Options.DPDC", "modulename": "PyMPDATA.options", "qualname": "Options.DPDC", "kind": "variable", "doc": "

flag enabling the double-pass donor-cell option, see:\nBeason & Margolin, 1988,\nMargolin & Shashkov, 2006,\nMargolin & Smolarkiewicz, 1998

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.non_zero_mu_coeff": {"fullname": "PyMPDATA.options.Options.non_zero_mu_coeff", "modulename": "PyMPDATA.options", "qualname": "Options.non_zero_mu_coeff", "kind": "variable", "doc": "

flag enabling handling of Fickian diffusion term

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.dimensionally_split": {"fullname": "PyMPDATA.options.Options.dimensionally_split", "modulename": "PyMPDATA.options", "qualname": "Options.dimensionally_split", "kind": "variable", "doc": "

flag disabling cross-dimensional terms in antidiffusive velocities

\n", "annotation": ": bool"}, "PyMPDATA.options.Options.n_halo": {"fullname": "PyMPDATA.options.Options.n_halo", "modulename": "PyMPDATA.options", "qualname": "Options.n_halo", "kind": "variable", "doc": "

Halo extent for a given options set.\nThe halo extent is the number of 'ghost layers' that need to be added\nto the outside of the domain to ensure that the MPDATA stencil operations can be\napplied to the edges of the domain.\nIt is similar to\narray padding.\nThe halo extent is determined by the options set.

\n", "annotation": ": int"}, "PyMPDATA.options.Options.jit_flags": {"fullname": "PyMPDATA.options.Options.jit_flags", "modulename": "PyMPDATA.options", "qualname": "Options.jit_flags", "kind": "variable", "doc": "

options passed to numba.njit()

\n", "annotation": ": PyMPDATA.options.HashableDict"}, "PyMPDATA.scalar_field": {"fullname": "PyMPDATA.scalar_field", "modulename": "PyMPDATA.scalar_field", "kind": "module", "doc": "

scalar field abstractions for the staggered grid

\n"}, "PyMPDATA.scalar_field.ScalarField": {"fullname": "PyMPDATA.scalar_field.ScalarField", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField", "kind": "class", "doc": "

n-dimensional scalar field including halo data, used to represent advectee, g_factor, etc.

\n", "bases": "PyMPDATA.impl.field.Field"}, "PyMPDATA.scalar_field.ScalarField.__init__": {"fullname": "PyMPDATA.scalar_field.ScalarField.__init__", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.__init__", "kind": "function", "doc": "

\n", "signature": "(data: numpy.ndarray, halo: int, boundary_conditions: tuple)"}, "PyMPDATA.scalar_field.ScalarField.data": {"fullname": "PyMPDATA.scalar_field.ScalarField.data", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.data", "kind": "variable", "doc": "

\n"}, "PyMPDATA.scalar_field.ScalarField.domain": {"fullname": "PyMPDATA.scalar_field.ScalarField.domain", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.domain", "kind": "variable", "doc": "

\n"}, "PyMPDATA.scalar_field.ScalarField.clone": {"fullname": "PyMPDATA.scalar_field.ScalarField.clone", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.clone", "kind": "function", "doc": "

returns an instance with the same dimensionality and same halo size as the argument\noptionally with a different data type

\n", "signature": "(field, dtype=None):", "funcdef": "def"}, "PyMPDATA.scalar_field.ScalarField.get": {"fullname": "PyMPDATA.scalar_field.ScalarField.get", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.get", "kind": "function", "doc": "

returns a view (not a copy) of the field data excluding halo

\n", "signature": "(self) -> numpy.ndarray:", "funcdef": "def"}, "PyMPDATA.scalar_field.ScalarField.make_null": {"fullname": "PyMPDATA.scalar_field.ScalarField.make_null", "modulename": "PyMPDATA.scalar_field", "qualname": "ScalarField.make_null", "kind": "function", "doc": "

returns a scalar field instance with no allocated data storage,\nsee Field._make_null other properties of the returned field

\n", "signature": "(n_dims, traversals):", "funcdef": "def"}, "PyMPDATA.solver": {"fullname": "PyMPDATA.solver", "modulename": "PyMPDATA.solver", "kind": "module", "doc": "

class grouping user-supplied stepper, fields and post-step/post-iter hooks,\nas well as self-initialised temporary storage

\n"}, "PyMPDATA.solver.PostStepNull": {"fullname": "PyMPDATA.solver.PostStepNull", "modulename": "PyMPDATA.solver", "qualname": "PostStepNull", "kind": "class", "doc": "

do-nothing version of the post-step hook

\n", "bases": "PostStepNull"}, "PyMPDATA.solver.PostStepNull.call": {"fullname": "PyMPDATA.solver.PostStepNull.call", "modulename": "PyMPDATA.solver", "qualname": "PostStepNull.call", "kind": "function", "doc": "

think of it as a __call__ method (which Numba does not allow)

\n", "signature": "(self, psi, step):", "funcdef": "def"}, "PyMPDATA.solver.PostStepNull.class_type": {"fullname": "PyMPDATA.solver.PostStepNull.class_type", "modulename": "PyMPDATA.solver", "qualname": "PostStepNull.class_type", "kind": "variable", "doc": "

\n", "default_value": "jitclass.PostStepNull#7fd9662f0d00<>"}, "PyMPDATA.solver.PostIterNull": {"fullname": "PyMPDATA.solver.PostIterNull", "modulename": "PyMPDATA.solver", "qualname": "PostIterNull", "kind": "class", "doc": "

do-nothing version of the post-iter hook

\n", "bases": "PostIterNull"}, "PyMPDATA.solver.PostIterNull.call": {"fullname": "PyMPDATA.solver.PostIterNull.call", "modulename": "PyMPDATA.solver", "qualname": "PostIterNull.call", "kind": "function", "doc": "

think of it as a __call__ method (which Numba does not allow)

\n", "signature": "(self, flux, g_factor, step, iteration):", "funcdef": "def"}, "PyMPDATA.solver.PostIterNull.class_type": {"fullname": "PyMPDATA.solver.PostIterNull.class_type", "modulename": "PyMPDATA.solver", "qualname": "PostIterNull.class_type", "kind": "variable", "doc": "

\n", "default_value": "jitclass.PostIterNull#7fd95ee5b640<>"}, "PyMPDATA.solver.Solver": {"fullname": "PyMPDATA.solver.Solver", "modulename": "PyMPDATA.solver", "qualname": "Solver", "kind": "class", "doc": "

Solution orchestrator requiring prior instantiation of: a Stepper,\na scalar advectee field (that which is advected),\na vector advector field (that which advects),\nand optionally a scalar g_factor field (used in some cases of the advection equation).\nNote: in some cases of advection, i.e. momentum advection,\nthe advectee can act upon the advector.\nSee PyMPDATA_examples.Jarecka_et_al_2015 for an example of this.

\n"}, "PyMPDATA.solver.Solver.__init__": {"fullname": "PyMPDATA.solver.Solver.__init__", "modulename": "PyMPDATA.solver", "qualname": "Solver.__init__", "kind": "function", "doc": "

\n", "signature": "(\tstepper: PyMPDATA.stepper.Stepper,\tadvectee: PyMPDATA.scalar_field.ScalarField,\tadvector: PyMPDATA.vector_field.VectorField,\tg_factor: [<class 'PyMPDATA.scalar_field.ScalarField'>, None] = None)"}, "PyMPDATA.solver.Solver.advectee": {"fullname": "PyMPDATA.solver.Solver.advectee", "modulename": "PyMPDATA.solver", "qualname": "Solver.advectee", "kind": "variable", "doc": "

advectee scalar field (with halo), modified by advance(),\nmay be modified from user code (e.g., source-term handling)

\n", "annotation": ": PyMPDATA.scalar_field.ScalarField"}, "PyMPDATA.solver.Solver.advector": {"fullname": "PyMPDATA.solver.Solver.advector", "modulename": "PyMPDATA.solver", "qualname": "Solver.advector", "kind": "variable", "doc": "

advector vector field (with halo), unmodified by advance(),\nmay be modified from user code

\n", "annotation": ": PyMPDATA.vector_field.VectorField"}, "PyMPDATA.solver.Solver.g_factor": {"fullname": "PyMPDATA.solver.Solver.g_factor", "modulename": "PyMPDATA.solver", "qualname": "Solver.g_factor", "kind": "variable", "doc": "

G_factor field (with halo), unmodified by advance(), assumed to be constant-in-time.\nCan be used as a Jacobian for coordinate transformations,\ne.g. into spherical coordinates.\nFor this type of usage, see\nPyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14.\nCan also be used to account for spatial variability of fluid density, see\nPyMPDATA_examples.Shipway_and_Hill_2012.\ne.g. the changing density of a fluid.

\n", "annotation": ": PyMPDATA.scalar_field.ScalarField"}, "PyMPDATA.solver.Solver.advance": {"fullname": "PyMPDATA.solver.Solver.advance", "modulename": "PyMPDATA.solver", "qualname": "Solver.advance", "kind": "function", "doc": "

advances solution by n_steps steps, optionally accepts: a tuple of diffusion\ncoefficients (one value per dimension) as well as post_iter and post_step\ncallbacks expected to be numba.jitclasses with a call method, for\nsignature see PostStepNull and PostIterNull;\nreturns CPU time per timestep (units as returned by clock.clock())

\n", "signature": "(\tself,\tn_steps: int,\tmu_coeff: Optional[tuple] = None,\tpost_step=None,\tpost_iter=None):", "funcdef": "def"}, "PyMPDATA.stepper": {"fullname": "PyMPDATA.stepper", "modulename": "PyMPDATA.stepper", "kind": "module", "doc": "

MPDATA iteration logic

\n"}, "PyMPDATA.stepper.Stepper": {"fullname": "PyMPDATA.stepper.Stepper", "modulename": "PyMPDATA.stepper", "qualname": "Stepper", "kind": "class", "doc": "

MPDATA stepper specialised for given options, dimensionality and optionally grid\n(instances of Stepper can be shared among Solvers)

\n"}, "PyMPDATA.stepper.Stepper.__init__": {"fullname": "PyMPDATA.stepper.Stepper.__init__", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.__init__", "kind": "function", "doc": "

\n", "signature": "(\t*,\toptions: PyMPDATA.options.Options,\tn_dims: (<class 'int'>, None) = None,\tnon_unit_g_factor: bool = False,\tgrid: (<class 'tuple'>, None) = None,\tn_threads: (<class 'int'>, None) = None,\tleft_first: (<class 'tuple'>, None) = None,\tbuffer_size: int = 0)"}, "PyMPDATA.stepper.Stepper.options": {"fullname": "PyMPDATA.stepper.Stepper.options", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.options", "kind": "variable", "doc": "

Options instance used

\n", "annotation": ": PyMPDATA.options.Options"}, "PyMPDATA.stepper.Stepper.n_threads": {"fullname": "PyMPDATA.stepper.Stepper.n_threads", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.n_threads", "kind": "variable", "doc": "

actual n_threads used (may be different than passed to __init__ if n_dims==1\nor if on a platform where Numba does not support threading)

\n", "annotation": ": int"}, "PyMPDATA.stepper.Stepper.n_dims": {"fullname": "PyMPDATA.stepper.Stepper.n_dims", "modulename": "PyMPDATA.stepper", "qualname": "Stepper.n_dims", "kind": "variable", "doc": "

dimensionality (1, 2 or 3)

\n", "annotation": ": int"}, "PyMPDATA.stepper.make_step_impl": {"fullname": "PyMPDATA.stepper.make_step_impl", "modulename": "PyMPDATA.stepper", "qualname": "make_step_impl", "kind": "function", "doc": "

returns (and caches) an njit-ted stepping function and a traversals pair

\n", "signature": "(\toptions,\tnon_unit_g_factor,\tgrid,\tn_threads,\tleft_first: tuple,\tbuffer_size):", "funcdef": "def"}, "PyMPDATA.vector_field": {"fullname": "PyMPDATA.vector_field", "modulename": "PyMPDATA.vector_field", "kind": "module", "doc": "

vector field abstractions for the staggered grid

\n"}, "PyMPDATA.vector_field.VectorField": {"fullname": "PyMPDATA.vector_field.VectorField", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField", "kind": "class", "doc": "

n-component n-dimensional vector field including halo data,\nused to represent the advector field

\n", "bases": "PyMPDATA.impl.field.Field"}, "PyMPDATA.vector_field.VectorField.__init__": {"fullname": "PyMPDATA.vector_field.VectorField.__init__", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.__init__", "kind": "function", "doc": "

\n", "signature": "(data: tuple, halo: int, boundary_conditions: tuple)"}, "PyMPDATA.vector_field.VectorField.data": {"fullname": "PyMPDATA.vector_field.VectorField.data", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.data", "kind": "variable", "doc": "

\n"}, "PyMPDATA.vector_field.VectorField.domain": {"fullname": "PyMPDATA.vector_field.VectorField.domain", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.domain", "kind": "variable", "doc": "

\n"}, "PyMPDATA.vector_field.VectorField.clone": {"fullname": "PyMPDATA.vector_field.VectorField.clone", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.clone", "kind": "function", "doc": "

returns a newly allocated field of the same shape, halo and boundary conditions

\n", "signature": "(field):", "funcdef": "def"}, "PyMPDATA.vector_field.VectorField.get_component": {"fullname": "PyMPDATA.vector_field.VectorField.get_component", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.get_component", "kind": "function", "doc": "

returns a view over given component of the field excluding halo

\n", "signature": "(self, i: int) -> numpy.ndarray:", "funcdef": "def"}, "PyMPDATA.vector_field.VectorField.div": {"fullname": "PyMPDATA.vector_field.VectorField.div", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.div", "kind": "function", "doc": "

returns a newly allocated scalar field (with no halo) containing the\ndivergence of the vector field computed using minimal stencil

\n", "signature": "(self, grid_step: tuple) -> PyMPDATA.scalar_field.ScalarField:", "funcdef": "def"}, "PyMPDATA.vector_field.VectorField.make_null": {"fullname": "PyMPDATA.vector_field.VectorField.make_null", "modulename": "PyMPDATA.vector_field", "qualname": "VectorField.make_null", "kind": "function", "doc": "

returns a vector field instance with no allocated data storage,\nsee Field._make_null other properties of the returned field

\n", "signature": "(n_dims, traversals):", "funcdef": "def"}, "PyMPDATA_examples": {"fullname": "PyMPDATA_examples", "modulename": "PyMPDATA_examples", "kind": "module", "doc": "

Introduction

\n\n

PyMPDATA examples are bundled with PyMPDATA and located in the examples subfolder.\nThey constitute a separate PyMPDATA_examples Python package which is also available at PyPI.\nThe examples have additional dependencies listed in PyMPDATA_examples package setup.py file.\nRunning the examples requires the PyMPDATA_examples package to be installed.

\n\n

We recommend you look through the example gallery below to see the examples in action.

\n\n

Example gallery

\n\n

Unless stated otherwise the following examples solve the basic advection equation:\n$$ \\partial_t (\\psi) + \\nabla \\cdot (u \\psi) = 0 $$

\n\n

The examples are grouped by the dimensionality of the computational grid.

\n\n

in 1D

\n\n\n\n\n \n \n\n\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n
tagslink
advection-diffusion equation
$$ \\partial_t (\\psi) + \\nabla \\cdot (u \\psi) + \\mu \\Delta (\\psi) = 0 $$		PyMPDATA_examples.advection_diffusion_1d*
Black-Scholes equation, option pricing
$$ \\frac{\\partial f}{\\partial t} + rS \\frac{\\partial f}{\\partial S} + \\frac{\\sigma^2}{2} S^2 \\frac{\\partial^2 f}{\\partial S^2} - rf = 0$$		PyMPDATA_examples.Arabas_and_Farhat_2020*
advection equation, homogeneous, several algorithm variants comparison: infinite-gauge, flux-corrected,..PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12
Size-spectral advection, particle population condensational growth, coordinate transformation
$$ \\partial_t (G \\psi) + \\nabla \\cdot (Gu \\psi) = 0 $$		PyMPDATA_examples.Olesik_et_al_2022*
advection equation, double-pass donor-cell optionPyMPDATA_examples.DPDC
\n\n

in 2D

\n\n\n\n\n \n \n\n\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n
tagslink
advection-diffusion equation
$$ \\partial_t (\\psi) + \\nabla \\cdot (u \\psi) + \\mu \\Delta (\\psi) = 0 $$		PyMPDATA_examples.advection_diffusion_2d*
\"adv-diff\"
Spectral-spatial advection, particle population condensational growth in a vertical column of air, time dependent flowPyMPDATA_examples.Shipway_and_Hill_2012
\"spectral-spatial\"
shallow-water equations
$$\\begin{eqnarray} \\partial_t h + \\partial_x (uh) + \\partial_y (vh) &=& 0~ \\\\ \\partial_t (uh) + \\partial_x ( uuh) + \\partial_y (vuh) &=& - h \\partial_x h~ \\\\ \\partial_t (vh) + \\partial_x ( uvh) + \\partial_y (vvh) &=& - h \\partial_y h~ \\end{eqnarray}$$		PyMPDATA_examples.Jarecka_et_al_2015*
advection equation, solid body rotationPyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12*
advection equation, coordinate transformation, spherical coordinates, see also examples in PyMPDATA-MPI $$ \\partial_t (G \\psi) + \\nabla \\cdot (Gu \\psi) = 0 $$PyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14
\"mpi-gif\"
advection equation, comparison against DG solution using Trixi.jlPyMPDATA_examples.trixi_comparison
\n\n

in 3D

\n\n\n\n\n \n \n\n\n\n\n \n \n\n\n \n \n\n\n
tagslink
homogeneous advection equationPyMPDATA_examples.Smolarkiewicz_1984
homogeneous advection equation, performance comparison against libmpdata++, scalability analysis in respect to threadsPyMPDATA_examples.Bartman_et_al_2022
\n\n

* - with comparison against analytic solution

\n\n

Installation

\n\n

Since the examples package includes Jupyter notebooks (and their execution requires write access), the suggested install and launch steps are:

\n\n
git clone https://github.com/open-atmos/PyMPDATA-examples.git\ncd PyMPDATA-examples\npip install -e .\njupyter-notebook\n
\n\n

Alternatively, one can also install the examples package from pypi.org by using

\n\n
pip install PyMPDATA-examples.\n
\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020", "kind": "module", "doc": "

This example implements simulations presented in the\n Arabas and Farhat 2020\n study on pricing of European and American options using MPDATA.

\n\n

Each notebook in this directory corresponds to a figure or a table in the paper.

\n\n

fig_1.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

fig_2.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

fig_3.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

tab_1.ipynb:\n\"preview\n\"launch\n\"launch

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\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.S": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.S", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.S", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.mu_coeff": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.mu_coeff", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.mu_coeff", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.solvers": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.solvers", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.solvers", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self, n_iters: int):", "funcdef": "def"}, "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.terminal_value": {"fullname": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation.Simulation.terminal_value", "modulename": "PyMPDATA_examples.Arabas_and_Farhat_2020.simulation", "qualname": "Simulation.terminal_value", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA_examples.Bartman_et_al_2022": {"fullname": "PyMPDATA_examples.Bartman_et_al_2022", "modulename": "PyMPDATA_examples.Bartman_et_al_2022", "kind": "module", "doc": "

This example is based on the paper:\nBartman et al. 2022.

\n\n

fig_X.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.DPDC": {"fullname": "PyMPDATA_examples.DPDC", "modulename": "PyMPDATA_examples.DPDC", "kind": "module", "doc": "

This example demonstrates the use of the Double-Pass Donor-Cell option in PyMPDATA.options.

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015", "kind": "module", "doc": "

This module showcases the PyMPDATA implementation of an MPDATA-based shallow-water equations\nsolver discussed and bencharked against analytical solutions in\nJarecka_et_al_2015.

\n\n

fig_6.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.formulae": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.formulae.amplitude": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.formulae.amplitude", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "qualname": "amplitude", "kind": "function", "doc": "

\n", "signature": "(x, y, lx, ly):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.formulae.deriv": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.formulae.deriv", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "qualname": "deriv", "kind": "function", "doc": "

return derivatives of [lambda_x, dlambda_x/dt, lambda_y, dlambda_y/dt\nfour first-order ODEs based on eq. 7 (Jarecka, Jaruga, Smolarkiewicz)

\n", "signature": "(y, _):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.formulae.d2_el_lamb_lamb_t_evol": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.formulae.d2_el_lamb_lamb_t_evol", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.formulae", "qualname": "d2_el_lamb_lamb_t_evol", "kind": "function", "doc": "

solving coupled nonlinear second-order ODEs - eq. 7 (Jarecka, Jaruga, Smolarkiewicz)\nreturning array with first dim denoting time, second dim:\n[lambda_x, dot{lambda_x}, lambda_y, dot{lambda_y}

\n", "signature": "(times, lamb_x0, lamb_y0):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.plot_output": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.plot_output", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.plot_output", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.plot_output.plot_output": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.plot_output.plot_output", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.plot_output", "qualname": "plot_output", "kind": "function", "doc": "

\n", "signature": "(times, output, settings, return_data=True):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dt", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dx": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dx", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.dx", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dy": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.dy", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.dy", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.nx": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.nx", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.nx", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.ny": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.ny", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.ny", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.eps": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.eps", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.eps", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.lx0": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.lx0", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.lx0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.ly0": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.ly0", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.ly0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.options": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.options", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.options", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.nt", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.outfreq": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.settings.Settings.outfreq", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.settings", "qualname": "Settings.outfreq", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

\n", "signature": "(settings)"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.settings": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.settings", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.settings", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.advector": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.advector", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.advector", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.solvers": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.solvers", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.solvers", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.interpolate": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.interpolate", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.interpolate", "kind": "function", "doc": "

\n", "signature": "(psi, axis):", "funcdef": "def"}, "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Jarecka_et_al_2015.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Jarecka_et_al_2015.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12", "kind": "module", "doc": "

This is an example based on the solid-body rotation test from\nMolenkap 1968\n(as in Fig. 12 in Jaruga et al. 2015).

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.options": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.options", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "qualname": "options", "kind": "variable", "doc": "

\n", "default_value": "{'upwind': <PyMPDATA.options.Options object>, '2+fct': <PyMPDATA.options.Options object>, '3+fct+tot': <PyMPDATA.options.Options object>, '2+fct+iga': <PyMPDATA.options.Options object>}"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.compute_panel": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.compute_panel", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "qualname": "compute_panel", "kind": "function", "doc": "

\n", "signature": "(panel):", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.fig_12_data": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis.fig_12_data", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.analysis", "qualname": "fig_12_data", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.grid": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.grid", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "grid", "kind": "variable", "doc": "

\n", "default_value": "(100, 100)"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "dt", "kind": "variable", "doc": "

\n", "default_value": "0.1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dx": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dx", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "dx", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dy": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.dy", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "dy", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.omega": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.omega", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "omega", "kind": "variable", "doc": "

\n", "default_value": "0.1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "h", "kind": "variable", "doc": "

\n", "default_value": "4.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h0": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.h0", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "h0", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.r": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.r", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "r", "kind": "variable", "doc": "

\n", "default_value": "15.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.x0": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.x0", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "x0", "kind": "variable", "doc": "

\n", "default_value": "50"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.y0": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.y0", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "y0", "kind": "variable", "doc": "

\n", "default_value": "75"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.xc": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.xc", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "xc", "kind": "variable", "doc": "

\n", "default_value": "50.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.yc": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.yc", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "yc", "kind": "variable", "doc": "

\n", "default_value": "50.0"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.__init__": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.__init__", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.__init__", "kind": "function", "doc": "

\n", "signature": "(n_rotations: int = 6)"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.n_rotations": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.n_rotations", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.n_rotations", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.dt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.nt", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.size": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.size", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.size", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.xrange": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.xrange", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.xrange", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.yrange": {"fullname": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings.Settings.yrange", "modulename": "PyMPDATA_examples.Molenkamp_test_as_in_Jaruga_et_al_2015_Fig_12.settings", "qualname": "Settings.yrange", "kind": "variable", "doc": "

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This is a PyMPDATA implementation of 1D particle population condensational growth\nproblem with coordinate transformations, as presented in\nOlesik et al. 2022.

\n\n

demo_analytical_solution.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_make_convergences.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_make_dispersion_ratio.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_make_plots.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

demo_wall_times.ipynb:\n\"preview\n\"launch\n\"launch

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\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.kappa": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.kappa", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.kappa", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.pdf": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954.SizeDistribution.pdf", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.East_and_Marshall_1954", "qualname": "SizeDistribution.pdf", "kind": "function", "doc": "

\n", "signature": "(self, r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "kind": "module", "doc": "

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\n", "signature": "(settings, grid_layout, psi_coord, options_dict, GC_max):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis.compute_figure_data": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis.compute_figure_data", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "qualname": "compute_figure_data", "kind": "function", "doc": "

\n", "signature": "(\t*,\tnr,\tGC_max,\tpsi_coord=<PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_id object>,\tgrid_layouts=(<PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_id object>, <PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2 object>, <PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn object>),\topt_set=({'n_iters': 1},),\tmixing_ratios_g_kg=array([ 1, 2, 4, 6, 8, 10])):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.analysis.Result": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.analysis.Result", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.analysis", "qualname": "Result", "kind": "class", "doc": "

Result(dt, out_steps, grid_layout_str, option_str, result)

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Create new instance of Result(dt, out_steps, grid_layout_str, option_str, result)

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Alias for field number 0

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Alias for field number 1

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Alias for field number 2

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Alias for field number 3

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Alias for field number 4

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\n"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2.x": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2.x", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p2.x", "kind": "function", "doc": "

\n", "signature": "(r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2.r": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2.r", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p2.r", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2.dx_dr": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p2.dx_dr", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p2.dx_dr", "kind": "function", "doc": "

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\n", "signature": "(x, k):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p3", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.x": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.x", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p3.x", "kind": "function", "doc": "

\n", "signature": "(r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.r": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.r", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p3.r", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.dx_dr": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.dx_dr", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p3.dx_dr", "kind": "function", "doc": "

\n", "signature": "(r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.moment_of_r_integral": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_p3.moment_of_r_integral", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_p3.moment_of_r_integral", "kind": "function", "doc": "

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\n"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.__init__": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.__init__", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.__init__", "kind": "function", "doc": "

\n", "signature": "(r0=1, base=2.718281828459045, n=3)"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.r0": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.r0", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.r0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.base": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.base", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.base", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.n": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.n", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.n", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.x": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.x", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.x", "kind": "function", "doc": "

\n", "signature": "(self, r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.r": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.r", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.r", "kind": "function", "doc": "

\n", "signature": "(self, x):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.dx_dr": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.dx_dr", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.dx_dr", "kind": "function", "doc": "

\n", "signature": "(self, r):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.moment_of_r_integral": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.coordinates.x_log_of_pn.moment_of_r_integral", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.coordinates", "qualname": "x_log_of_pn.moment_of_r_integral", "kind": "function", "doc": "

\n", "signature": "(self, x, k):", "funcdef": "def"}, "PyMPDATA_examples.Olesik_et_al_2022.equilibrium_drop_growth": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.equilibrium_drop_growth", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.equilibrium_drop_growth", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Olesik_et_al_2022.equilibrium_drop_growth.DrDt": {"fullname": "PyMPDATA_examples.Olesik_et_al_2022.equilibrium_drop_growth.DrDt", "modulename": "PyMPDATA_examples.Olesik_et_al_2022.equilibrium_drop_growth", "qualname": "DrDt", "kind": "class", "doc": "

eq. 7.20 in Rogers and Yau 1989

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eq. 7.32 in Rogers and Yau 1989

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\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_max": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_max", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.t_max", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.qv": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.qv", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.qv", "kind": "variable", "doc": "

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\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.thd": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.thd", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.thd", "kind": "variable", "doc": "

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\n"}, "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_1": {"fullname": "PyMPDATA_examples.Shipway_and_Hill_2012.settings.Settings.t_1", "modulename": "PyMPDATA_examples.Shipway_and_Hill_2012.settings", "qualname": "Settings.t_1", "kind": "variable", "doc": "

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\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.r": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.r", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.r", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.x0": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.x0", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.x0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.y0": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.y0", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.y0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.z0": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.z0", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.z0", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.omega": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.omega", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.omega", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.xc": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.xc", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.xc", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.yc": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.yc", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.yc", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.zc": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.zc", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.zc", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advector": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advector", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.advector", "kind": "variable", "doc": "

constant angular velocity rotational field

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advectee": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.settings.Settings.advectee", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.settings", "qualname": "Settings.advectee", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

\n", "signature": "(settings, options, static=True, n_threads=None)"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.solver": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.solver", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.solver", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self, nt):", "funcdef": "def"}, "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.advectee": {"fullname": "PyMPDATA_examples.Smolarkiewicz_1984.simulation.Simulation.advectee", "modulename": "PyMPDATA_examples.Smolarkiewicz_1984.simulation", "qualname": "Simulation.advectee", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12", "kind": "module", "doc": "

This is an example of 1D homogeneous advection test from\nSmolarkiewicz 2006, depicting infinite-gauge and\nflux-corrected transport case.

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n\n

\"plot\"

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings", "kind": "class", "doc": "

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\n", "signature": "(shape: str)"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nt": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nt", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.nt", "kind": "variable", "doc": "

\n", "default_value": "1600"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.dt": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.dt", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.dt", "kind": "variable", "doc": "

\n", "default_value": "1"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nx": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.nx", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.nx", "kind": "variable", "doc": "

\n", "default_value": "500"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.C": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.C", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.C", "kind": "variable", "doc": "

\n", "default_value": "0.5"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_min": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_min", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.x_min", "kind": "variable", "doc": "

\n", "default_value": "-250"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_max": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.x_max", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.x_max", "kind": "variable", "doc": "

\n", "default_value": "250"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_cosine": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_cosine", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.cdf_cosine", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_rect": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings.cdf_rect", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings", "qualname": "Settings.cdf_rect", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation", "kind": "class", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.__init__": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.__init__", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation.__init__", "kind": "function", "doc": "

\n", "signature": "(\tsettings: PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.settings.Settings,\toptions: PyMPDATA.options.Options)"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.stepper": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.stepper", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation.stepper", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.nt": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.nt", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation.nt", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.state": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.state", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation.state", "kind": "variable", "doc": "

\n"}, "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.run": {"fullname": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation.Simulation.run", "modulename": "PyMPDATA_examples.Smolarkiewicz_2006_Figs_3_4_10_11_12.simulation", "qualname": "Simulation.run", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "PyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14": {"fullname": "PyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14", "modulename": "PyMPDATA_examples.Williamson_and_Rasch_1989_as_in_Jaruga_et_al_2015_Fig_14", "kind": "module", "doc": "

This example is based on\nWilliamson and Rasch 1989.\nIt demonstrates the use of PyMPDATA.solver.g_factor\nto transform the advection problem onto a sphere.

\n\n

demo_over_the_pole.ipynb:\n\"preview\n\"launch\n\"launch

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PyMPDATA 1D advection-diffusion example with error analysis for different initial parameters.

\n\n

demo.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.advection_diffusion_2d": {"fullname": "PyMPDATA_examples.advection_diffusion_2d", "modulename": "PyMPDATA_examples.advection_diffusion_2d", "kind": "module", "doc": "

PyMPDATA 2D advection-diffusion example with gif creation.

\n\n

advection-diffusion-2d.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.trixi_comparison": {"fullname": "PyMPDATA_examples.trixi_comparison", "modulename": "PyMPDATA_examples.trixi_comparison", "kind": "module", "doc": "

This example uses a basic 2D advection test case to compare PyMPDATA\nsolution against Trixi.jl (Julia DG code)

\n\n

advection_comparison.ipynb:\n\"preview\n\"launch\n\"launch

\n"}, "PyMPDATA_examples.utils": {"fullname": "PyMPDATA_examples.utils", "modulename": "PyMPDATA_examples.utils", "kind": "module", "doc": "

Various utility functions for PyMPDATA examples.

\n"}, "PyMPDATA_examples.utils.discretisation": {"fullname": "PyMPDATA_examples.utils.discretisation", "modulename": "PyMPDATA_examples.utils.discretisation", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.utils.discretisation.from_pdf_2d": {"fullname": "PyMPDATA_examples.utils.discretisation.from_pdf_2d", "modulename": "PyMPDATA_examples.utils.discretisation", "qualname": "from_pdf_2d", "kind": "function", "doc": "

\n", "signature": "(\tpdf: <built-in function callable>,\txrange: list,\tyrange: list,\tgridsize: list):", "funcdef": "def"}, "PyMPDATA_examples.utils.discretisation.from_cdf_1d": {"fullname": "PyMPDATA_examples.utils.discretisation.from_cdf_1d", "modulename": "PyMPDATA_examples.utils.discretisation", "qualname": "from_cdf_1d", "kind": "function", "doc": "

\n", "signature": "(\tcdf: <built-in function callable>,\tx_min: float,\tx_max: float,\tnx: int):", "funcdef": "def"}, "PyMPDATA_examples.utils.discretisation.discretised_analytical_solution": {"fullname": "PyMPDATA_examples.utils.discretisation.discretised_analytical_solution", "modulename": "PyMPDATA_examples.utils.discretisation", "qualname": "discretised_analytical_solution", "kind": "function", "doc": "

\n", "signature": "(rh, pdf_t, midpoint_value=False, r=None):", "funcdef": "def"}, "PyMPDATA_examples.utils.error_norms": {"fullname": "PyMPDATA_examples.utils.error_norms", "modulename": "PyMPDATA_examples.utils.error_norms", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.utils.error_norms.L2": {"fullname": "PyMPDATA_examples.utils.error_norms.L2", "modulename": "PyMPDATA_examples.utils.error_norms", "qualname": "L2", "kind": "function", "doc": "

\n", "signature": "(numerical, analytical, nt):", "funcdef": "def"}, "PyMPDATA_examples.utils.error_norms.Smolarkiewicz_Grabowski_1990_eq21": {"fullname": "PyMPDATA_examples.utils.error_norms.Smolarkiewicz_Grabowski_1990_eq21", "modulename": "PyMPDATA_examples.utils.error_norms", "qualname": "Smolarkiewicz_Grabowski_1990_eq21", "kind": "function", "doc": "

\n", "signature": "(numerical, analytical, T):", "funcdef": "def"}, "PyMPDATA_examples.utils.error_norms.modified_Smolarkiewicz_Rasch_r0": {"fullname": "PyMPDATA_examples.utils.error_norms.modified_Smolarkiewicz_Rasch_r0", "modulename": "PyMPDATA_examples.utils.error_norms", "qualname": "modified_Smolarkiewicz_Rasch_r0", "kind": "function", "doc": "

\n", "signature": "(numerical, analytical, T, g_factor):", "funcdef": "def"}, "PyMPDATA_examples.utils.nondivergent_vector_field_2d": {"fullname": "PyMPDATA_examples.utils.nondivergent_vector_field_2d", "modulename": "PyMPDATA_examples.utils.nondivergent_vector_field_2d", "kind": "module", "doc": "

\n"}, "PyMPDATA_examples.utils.nondivergent_vector_field_2d.nondivergent_vector_field_2d": {"fullname": "PyMPDATA_examples.utils.nondivergent_vector_field_2d.nondivergent_vector_field_2d", "modulename": "PyMPDATA_examples.utils.nondivergent_vector_field_2d", "qualname": "nondivergent_vector_field_2d", "kind": "function", "doc": "

\n", "signature": "(grid, size, dt, stream_function: <built-in function callable>, halo):", "funcdef": "def"}, "PyMPDATA_examples.utils.nondivergent_vector_field_2d.x_vec_coord": {"fullname": "PyMPDATA_examples.utils.nondivergent_vector_field_2d.x_vec_coord", "modulename": "PyMPDATA_examples.utils.nondivergent_vector_field_2d", "qualname": "x_vec_coord", "kind": "function", "doc": "

\n", "signature": "(grid):", "funcdef": "def"}, "PyMPDATA_examples.utils.nondivergent_vector_field_2d.z_vec_coord": {"fullname": "PyMPDATA_examples.utils.nondivergent_vector_field_2d.z_vec_coord", "modulename": "PyMPDATA_examples.utils.nondivergent_vector_field_2d", "qualname": "z_vec_coord", "kind": "function", "doc": "

\n", "signature": "(grid):", "funcdef": "def"}}, "docInfo": {"PyMPDATA": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 9696}, "PyMPDATA.boundary_conditions": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 6}, "PyMPDATA.boundary_conditions.constant": {"qualname": 0, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 10}, "PyMPDATA.boundary_conditions.constant.Constant": {"qualname": 1, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 30}, "PyMPDATA.boundary_conditions.constant.Constant.__init__": {"qualname": 3, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 9, "bases": 0, "doc": 3}, "PyMPDATA.boundary_conditions.constant.Constant.value": {"qualname": 2, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, 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