Merge branch 'main' into get_diff_coils

.github/workflows/deployment.yml

@@ -94,7 +94,15 @@ jobs:
         run: |
           VERSION=${{ needs.build-testpypi-package.outputs.version }}
           SUFFIX=${{ needs.build-testpypi-package.outputs.suffix }}
-          python -m pip install mrpro==$VERSION$SUFFIX --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/
+          for i in {1..3}; do
+            if python -m pip install mrpro==$VERSION$SUFFIX --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/; then
+              echo "Package installed successfully."
+              break
+            else
+              echo "Attempt $i failed. Retrying in 10 seconds..."
+              sleep 10
+            fi
+          done
 
   build-pypi-package:
     name: Build Package for PyPI

.pre-commit-config.yaml

@@ -6,7 +6,6 @@ repos:
     rev: v5.0.0
     hooks:
       - id: check-added-large-files
-      - id: check-docstring-first
       - id: check-merge-conflict
       - id: check-yaml
       - id: check-toml
@@ -54,11 +53,11 @@ repos:
           - "--extra-index-url=https://pypi.python.org/simple"
 
 ci:
-    autofix_commit_msg: |
-        [pre-commit] auto fixes from pre-commit hooks
-    autofix_prs: false
-    autoupdate_branch: ''
-    autoupdate_commit_msg: '[pre-commit] pre-commit autoupdate'
-    autoupdate_schedule: monthly
-    skip: [mypy]
-    submodules: false
+  autofix_commit_msg: |
+    [pre-commit] auto fixes from pre-commit hooks
+  autofix_prs: false
+  autoupdate_branch: ""
+  autoupdate_commit_msg: "[pre-commit] pre-commit autoupdate"
+  autoupdate_schedule: monthly
+  skip: [mypy]
+  submodules: false

examples/direct_reconstruction.ipynb

@@ -37,10 +37,10 @@
     "\n",
     "import requests\n",
     "\n",
-    "with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:\n",
-    "    response = requests.get(zenodo_url + fname, timeout=30)\n",
-    "    data_file.write(response.content)\n",
-    "    data_file.flush()"
+    "data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')\n",
+    "response = requests.get(zenodo_url + fname, timeout=30)\n",
+    "data_file.write(response.content)\n",
+    "data_file.flush()"
    ]
   },
   {

examples/direct_reconstruction.py

@@ -11,10 +11,10 @@
 
 import requests
 
-with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:
-    response = requests.get(zenodo_url + fname, timeout=30)
-    data_file.write(response.content)
-    data_file.flush()
+data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')
+response = requests.get(zenodo_url + fname, timeout=30)
+data_file.write(response.content)
+data_file.flush()
 
 # %% [markdown]
 # ### Image reconstruction

examples/iterative_sense_reconstruction.ipynb

@@ -37,10 +37,10 @@
     "\n",
     "import requests\n",
     "\n",
-    "with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:\n",
-    "    response = requests.get(zenodo_url + fname, timeout=30)\n",
-    "    data_file.write(response.content)\n",
-    "    data_file.flush()"
+    "data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')\n",
+    "response = requests.get(zenodo_url + fname, timeout=30)\n",
+    "data_file.write(response.content)\n",
+    "data_file.flush()"
    ]
   },
   {

examples/iterative_sense_reconstruction.py

@@ -11,10 +11,10 @@
 
 import requests
 
-with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:
-    response = requests.get(zenodo_url + fname, timeout=30)
-    data_file.write(response.content)
-    data_file.flush()
+data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')
+response = requests.get(zenodo_url + fname, timeout=30)
+data_file.write(response.content)
+data_file.flush()
 
 # %% [markdown]
 # ### Image reconstruction

examples/pulseq_2d_radial_golden_angle.ipynb

@@ -33,14 +33,13 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "d16f41f1",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# define zenodo records URL and create a temporary directory and h5-file\n",
     "zenodo_url = 'https://zenodo.org/records/10854057/files/'\n",
-    "fname = 'pulseq_radial_2D_402spokes_golden_angle_with_traj.h5'"
+    "fname = 'pulseq_radial_2D_402spokes_golden_angle_with_traj.h5'\n",
+    "data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')"
    ]
   },
   {
@@ -51,10 +50,9 @@
    "outputs": [],
    "source": [
     "# Download raw data using requests\n",
-    "with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:\n",
-    "    response = requests.get(zenodo_url + fname, timeout=30)\n",
-    "    data_file.write(response.content)\n",
-    "    data_file.flush()"
+    "response = requests.get(zenodo_url + fname, timeout=30)\n",
+    "data_file.write(response.content)\n",
+    "data_file.flush()"
    ]
   },
   {
@@ -127,10 +125,10 @@
     "# download the sequence file from zenodo\n",
     "zenodo_url = 'https://zenodo.org/records/10868061/files/'\n",
     "seq_fname = 'pulseq_radial_2D_402spokes_golden_angle.seq'\n",
-    "with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.seq') as seq_file:\n",
-    "    response = requests.get(zenodo_url + seq_fname, timeout=30)\n",
-    "    seq_file.write(response.content)\n",
-    "    seq_file.flush()"
+    "seq_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.seq')\n",
+    "response = requests.get(zenodo_url + seq_fname, timeout=30)\n",
+    "seq_file.write(response.content)\n",
+    "seq_file.flush()"
    ]
   },
   {

examples/pulseq_2d_radial_golden_angle.py

@@ -19,14 +19,13 @@
 # define zenodo records URL and create a temporary directory and h5-file
 zenodo_url = 'https://zenodo.org/records/10854057/files/'
 fname = 'pulseq_radial_2D_402spokes_golden_angle_with_traj.h5'
-
+data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')
 
 # %%
 # Download raw data using requests
-with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:
-    response = requests.get(zenodo_url + fname, timeout=30)
-    data_file.write(response.content)
-    data_file.flush()
+response = requests.get(zenodo_url + fname, timeout=30)
+data_file.write(response.content)
+data_file.flush()
 
 # %% [markdown]
 # ### Image reconstruction using KTrajectoryIsmrmrd
@@ -63,10 +62,10 @@
 # download the sequence file from zenodo
 zenodo_url = 'https://zenodo.org/records/10868061/files/'
 seq_fname = 'pulseq_radial_2D_402spokes_golden_angle.seq'
-with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.seq') as seq_file:
-    response = requests.get(zenodo_url + seq_fname, timeout=30)
-    seq_file.write(response.content)
-    seq_file.flush()
+seq_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.seq')
+response = requests.get(zenodo_url + seq_fname, timeout=30)
+seq_file.write(response.content)
+seq_file.flush()
 
 # %%
 # Read raw data and calculate trajectory using KTrajectoryPulseq

examples/regularized_iterative_sense_reconstruction.ipynb

@@ -37,10 +37,10 @@
     "\n",
     "import requests\n",
     "\n",
-    "with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:\n",
-    "    response = requests.get(zenodo_url + fname, timeout=30)\n",
-    "    data_file.write(response.content)\n",
-    "    data_file.flush()"
+    "data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')\n",
+    "response = requests.get(zenodo_url + fname, timeout=30)\n",
+    "data_file.write(response.content)\n",
+    "data_file.flush()"
    ]
   },
   {

examples/regularized_iterative_sense_reconstruction.py

@@ -11,10 +11,10 @@
 
 import requests
 
-with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5') as data_file:
-    response = requests.get(zenodo_url + fname, timeout=30)
-    data_file.write(response.content)
-    data_file.flush()
+data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')
+response = requests.get(zenodo_url + fname, timeout=30)
+data_file.write(response.content)
+data_file.flush()
 
 # %% [markdown]
 # ### Image reconstruction

examples/ruff.toml

@@ -5,4 +5,5 @@ lint.extend-ignore = [
     "T20",    #print
     "E402",   #module-import-not-at-top-of-file
     "S101",   #assert
+    "SIM115", #context manager for opening files
 ]

src/mrpro/VERSION

@@ -1 +1 @@
-0.241029
+0.241112

src/mrpro/data/MoveDataMixin.py

@@ -252,11 +252,9 @@ def apply_(
         ----------
         function
             The function to apply to all fields. None is interpreted as a no-op.
-
         memo
-            A dictionary to keep track of  objects that the function has already been applied to,
+            A dictionary to keep track of objects that the function has already been applied to,
             to avoid multiple applications. This is useful if the object has a circular reference.
-
         recurse
             If True, the function will be applied to all children that are MoveDataMixin instances.
         """

src/mrpro/data/_kdata/KDataSelectMixin.py

@@ -7,6 +7,7 @@
 from typing_extensions import Self
 
 from mrpro.data._kdata.KDataProtocol import _KDataProtocol
+from mrpro.data.Rotation import Rotation
 
 
 class KDataSelectMixin(_KDataProtocol):
@@ -50,7 +51,9 @@ def select_other_subset(
         other_idx = torch.cat([torch.where(idx == label_idx[:, 0, 0])[0] for idx in subset_idx], dim=0)
 
         # Adapt header
-        kheader.acq_info.apply_(lambda field: field[other_idx, ...] if isinstance(field, torch.Tensor) else field)
+        kheader.acq_info.apply_(
+            lambda field: field[other_idx, ...] if isinstance(field, torch.Tensor | Rotation) else field
+        )
 
         # Select data
         kdat = self.data[other_idx, ...]

src/mrpro/data/_kdata/KDataSplitMixin.py

@@ -1,6 +1,6 @@
 """Mixin class to split KData into other subsets."""
 
-from typing import Literal, TypeVar
+from typing import Literal, TypeVar, cast
 
 import torch
 from einops import rearrange, repeat
@@ -10,10 +10,8 @@
 from mrpro.data.AcqInfo import rearrange_acq_info_fields
 from mrpro.data.EncodingLimits import Limits
 from mrpro.data.Rotation import Rotation
-from mrpro.data.SpatialDimension import SpatialDimension
-
-T = TypeVar('T', torch.Tensor, Rotation, SpatialDimension)
 
+RotationOrTensor = TypeVar('RotationOrTensor', bound=torch.Tensor | Rotation)
 
 class KDataSplitMixin(_KDataProtocol):
     """Split KData into other subsets."""
@@ -59,8 +57,9 @@ def _split_k2_or_k1_into_other(
             def split_data_traj(dat_traj: torch.Tensor) -> torch.Tensor:
                 return dat_traj[:, :, :, split_idx, :]
 
-            def split_acq_info(acq_info: T) -> T:
-                return acq_info[:, :, split_idx, ...]
+            def split_acq_info(acq_info: RotationOrTensor) -> RotationOrTensor:
+                # cast due to https://github.com/python/mypy/issues/10817
+                return cast(RotationOrTensor, acq_info[:, :, split_idx, ...])
 
             # Rearrange other_split and k1 dimension
             rearrange_pattern_data = 'other coils k2 other_split k1 k0->(other other_split) coils k2 k1 k0'
@@ -72,8 +71,8 @@ def split_acq_info(acq_info: T) -> T:
             def split_data_traj(dat_traj: torch.Tensor) -> torch.Tensor:
                 return dat_traj[:, :, split_idx, :, :]
 
-            def split_acq_info(acq_info: T) -> T:
-                return acq_info[:, split_idx, ...]
+            def split_acq_info(acq_info: RotationOrTensor) -> RotationOrTensor:
+                return cast(RotationOrTensor, acq_info[:, split_idx, ...])
 
             # Rearrange other_split and k1 dimension
             rearrange_pattern_data = 'other coils other_split k2 k1 k0->(other other_split) coils k2 k1 k0'
@@ -101,7 +100,7 @@ def split_acq_info(acq_info: T) -> T:
         # Update shape of acquisition info index
         kheader.acq_info.apply_(
             lambda field: rearrange_acq_info_fields(split_acq_info(field), rearrange_pattern_acq_info)
-            if isinstance(field, T.__constraints__)
+            if isinstance(field, Rotation | torch.Tensor)
             else field
         )
 

src/mrpro/utils/__init__.py

@@ -1,13 +1,16 @@
 import mrpro.utils.slice_profiles
 import mrpro.utils.typing
+import mrpro.utils.unit_conversion
 from mrpro.utils.smap import smap
 from mrpro.utils.remove_repeat import remove_repeat
 from mrpro.utils.zero_pad_or_crop import zero_pad_or_crop
 from mrpro.utils.split_idx import split_idx
-from mrpro.utils.reshape import broadcast_right, unsqueeze_left, unsqueeze_right
+from mrpro.utils.reshape import broadcast_right, unsqueeze_left, unsqueeze_right, reduce_view
 import mrpro.utils.unit_conversion
+
 __all__ = [
     "broadcast_right",
+    "reduce_view",
     "remove_repeat",
     "slice_profiles",
     "smap",

src/mrpro/utils/reshape.py

@@ -1,5 +1,7 @@
 """Tensor reshaping utilities."""
 
+from collections.abc import Sequence
+
 import torch
 
 
@@ -67,3 +69,33 @@ def broadcast_right(*x: torch.Tensor) -> tuple[torch.Tensor, ...]:
     max_dim = max(el.ndim for el in x)
     unsqueezed = torch.broadcast_tensors(*(unsqueeze_right(el, max_dim - el.ndim) for el in x))
     return unsqueezed
+
+
+def reduce_view(x: torch.Tensor, dim: int | Sequence[int] | None = None) -> torch.Tensor:
+    """Reduce expanded dimensions in a view to singletons.
+
+    Reduce either all or specific dimensions to a singleton if it
+    points to the same memory address.
+    This undoes expand.
+
+    Parameters
+    ----------
+    x
+        input tensor
+    dim
+        only reduce expanded dimensions in the specified dimensions.
+        If None, reduce all expanded dimensions.
+    """
+    if dim is None:
+        dim_: Sequence[int] = range(x.ndim)
+    elif isinstance(dim, Sequence):
+        dim_ = [d % x.ndim for d in dim]
+    else:
+        dim_ = [dim % x.ndim]
+
+    stride = x.stride()
+    newsize = [
+        1 if stride == 0 and d in dim_ else oldsize
+        for d, (oldsize, stride) in enumerate(zip(x.size(), stride, strict=True))
+    ]
+    return torch.as_strided(x, newsize, stride)