Merge branch 'main' into gram

.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/AcqInfo.py

@@ -1,28 +1,29 @@
 """Acquisition information dataclass."""
 
-from collections.abc import Callable, Sequence
+from collections.abc import Sequence
 from dataclasses import dataclass
 
 import ismrmrd
 import numpy as np
 import torch
-from typing_extensions import Self, TypeVar
+from einops import rearrange
+from typing_extensions import Self
 
 from mrpro.data.MoveDataMixin import MoveDataMixin
+from mrpro.data.Rotation import Rotation
 from mrpro.data.SpatialDimension import SpatialDimension
+from mrpro.utils.unit_conversion import mm_to_m
 
-# Conversion functions for units
-T = TypeVar('T', float, torch.Tensor)
 
+def rearrange_acq_info_fields(field: object, pattern: str, **axes_lengths: dict[str, int]) -> object:
+    """Change the shape of the fields in AcqInfo."""
+    if isinstance(field, Rotation):
+        return Rotation.from_matrix(rearrange(field.as_matrix(), pattern, **axes_lengths))
 
-def ms_to_s(ms: T) -> T:
-    """Convert ms to s."""
-    return ms / 1000
+    if isinstance(field, torch.Tensor):
+        return rearrange(field, pattern, **axes_lengths)
 
-
-def mm_to_m(m: T) -> T:
-    """Convert mm to m."""
-    return m / 1000
+    return field
 
 
 @dataclass(slots=True)
@@ -121,30 +122,24 @@ class AcqInfo(MoveDataMixin):
     number_of_samples: torch.Tensor
     """Number of sample points per readout (readouts may have different number of sample points)."""
 
+    orientation: Rotation
+    """Rotation describing the orientation of the readout, phase and slice encoding direction."""
+
     patient_table_position: SpatialDimension[torch.Tensor]
     """Offset position of the patient table, in LPS coordinates [m]."""
 
-    phase_dir: SpatialDimension[torch.Tensor]
-    """Directional cosine of phase encoding (2D)."""
-
     physiology_time_stamp: torch.Tensor
     """Time stamps relative to physiological triggering, e.g. ECG. Not in s but in vendor-specific time units"""
 
     position: SpatialDimension[torch.Tensor]
     """Center of the excited volume, in LPS coordinates relative to isocenter [m]."""
 
-    read_dir: SpatialDimension[torch.Tensor]
-    """Directional cosine of readout/frequency encoding."""
-
     sample_time_us: torch.Tensor
     """Readout bandwidth, as time between samples [us]."""
 
     scan_counter: torch.Tensor
     """Zero-indexed incrementing counter for readouts."""
 
-    slice_dir: SpatialDimension[torch.Tensor]
-    """Directional cosine of slice normal, i.e. cross-product of read_dir and phase_dir."""
-
     trajectory_dimensions: torch.Tensor  # =3. We only support 3D Trajectories: kz always exists.
     """Dimensionality of the k-space trajectory vector."""
 
@@ -206,17 +201,13 @@ def tensor_2d(data: np.ndarray) -> torch.Tensor:
                 data_tensor = data_tensor[None, None]
             return data_tensor
 
-        def spatialdimension_2d(
-            data: np.ndarray, conversion: Callable[[torch.Tensor], torch.Tensor] | None = None
-        ) -> SpatialDimension[torch.Tensor]:
+        def spatialdimension_2d(data: np.ndarray) -> SpatialDimension[torch.Tensor]:
             # Ensure spatial dimension is (k1*k2*other, 1, 3)
             if data.ndim != 2:
                 raise ValueError('Spatial dimension is expected to be of shape (N,3)')
             data = data[:, None, :]
             # all spatial dimensions are float32
-            return (
-                SpatialDimension[torch.Tensor].from_array_xyz(torch.tensor(data.astype(np.float32))).apply_(conversion)
-            )
+            return SpatialDimension[torch.Tensor].from_array_xyz(torch.tensor(data.astype(np.float32)))
 
         acq_idx = AcqIdx(
             k1=tensor(idx['kspace_encode_step_1']),
@@ -251,14 +242,16 @@ def spatialdimension_2d(
             flags=tensor_2d(headers['flags']),
             measurement_uid=tensor_2d(headers['measurement_uid']),
             number_of_samples=tensor_2d(headers['number_of_samples']),
-            patient_table_position=spatialdimension_2d(headers['patient_table_position'], mm_to_m),
-            phase_dir=spatialdimension_2d(headers['phase_dir']),
+            orientation=Rotation.from_directions(
+                spatialdimension_2d(headers['slice_dir']),
+                spatialdimension_2d(headers['phase_dir']),
+                spatialdimension_2d(headers['read_dir']),
+            ),
+            patient_table_position=spatialdimension_2d(headers['patient_table_position']).apply_(mm_to_m),
             physiology_time_stamp=tensor_2d(headers['physiology_time_stamp']),
-            position=spatialdimension_2d(headers['position'], mm_to_m),
-            read_dir=spatialdimension_2d(headers['read_dir']),
+            position=spatialdimension_2d(headers['position']).apply_(mm_to_m),
             sample_time_us=tensor_2d(headers['sample_time_us']),
             scan_counter=tensor_2d(headers['scan_counter']),
-            slice_dir=spatialdimension_2d(headers['slice_dir']),
             trajectory_dimensions=tensor_2d(headers['trajectory_dimensions']).fill_(3),  # see above
             user_float=tensor_2d(headers['user_float']),
             user_int=tensor_2d(headers['user_int']),