Merge branch 'main' into ids

.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

@@ -3,10 +3,9 @@
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: check-added-large-files
-      - id: check-docstring-first
       - id: check-merge-conflict
       - id: check-yaml
       - id: check-toml
@@ -15,14 +14,14 @@ repos:
       - id: mixed-line-ending
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.9
+    rev: v0.7.2
     hooks:
       - id: ruff # linter
         args: [--fix]
       - id: ruff-format # formatter
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1.25.0
+    rev: v1.27.0
     hooks:
       - id: typos
 
@@ -34,7 +33,7 @@ repos:
         exclude: ^tests/
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.2
+    rev: v1.13.0
     hooks:
       - id: mypy
         pass_filenames: false

examples/direct_reconstruction.ipynb

@@ -37,10 +37,10 @@
     "\n",
     "import requests\n",
     "\n",
-    "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()"
+    "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()"
    ]
   },
   {

examples/direct_reconstruction.py

@@ -11,10 +11,10 @@
 
 import requests
 
-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()
+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()
 
 # %% [markdown]
 # ### Image reconstruction

examples/iterative_sense_reconstruction.ipynb

@@ -37,10 +37,10 @@
     "\n",
     "import requests\n",
     "\n",
-    "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()"
+    "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()"
    ]
   },
   {

examples/iterative_sense_reconstruction.py

@@ -11,10 +11,10 @@
 
 import requests
 
-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()
+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()
 
 # %% [markdown]
 # ### Image reconstruction

examples/pulseq_2d_radial_golden_angle.ipynb

@@ -33,13 +33,14 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "d16f41f1",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "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'\n",
-    "data_file = tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.h5')"
+    "fname = 'pulseq_radial_2D_402spokes_golden_angle_with_traj.h5'"
    ]
   },
   {
@@ -50,9 +51,10 @@
    "outputs": [],
    "source": [
     "# Download raw data using requests\n",
-    "response = requests.get(zenodo_url + fname, timeout=30)\n",
-    "data_file.write(response.content)\n",
-    "data_file.flush()"
+    "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()"
    ]
   },
   {
@@ -125,10 +127,10 @@
     "# 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",
-    "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()"
+    "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()"
    ]
   },
   {

examples/pulseq_2d_radial_golden_angle.py

@@ -19,13 +19,14 @@
 # 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
-response = requests.get(zenodo_url + fname, timeout=30)
-data_file.write(response.content)
-data_file.flush()
+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()
 
 # %% [markdown]
 # ### Image reconstruction using KTrajectoryIsmrmrd
@@ -62,10 +63,10 @@
 # download the sequence file from zenodo
 zenodo_url = 'https://zenodo.org/records/10868061/files/'
 seq_fname = 'pulseq_radial_2D_402spokes_golden_angle.seq'
-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()
+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()
 
 # %%
 # Read raw data and calculate trajectory using KTrajectoryPulseq

examples/regularized_iterative_sense_reconstruction.ipynb

@@ -37,10 +37,10 @@
     "\n",
     "import requests\n",
     "\n",
-    "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()"
+    "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()"
    ]
   },
   {

examples/regularized_iterative_sense_reconstruction.py

@@ -11,10 +11,10 @@
 
 import requests
 
-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()
+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()
 
 # %% [markdown]
 # ### Image reconstruction

pyproject.toml

@@ -84,6 +84,7 @@ testpaths = ["tests"]
 filterwarnings = [
     "error",
     "ignore:'write_like_original':DeprecationWarning:pydicom:",
+    "ignore:Anomaly Detection has been enabled:UserWarning", #torch.autograd
 ]
 addopts = "-n auto"
 markers = ["cuda : Tests only to be run when cuda device is available"]

src/mrpro/VERSION

@@ -1 +1 @@
-0.241015
+0.241029

src/mrpro/algorithms/optimizers/cg.py

@@ -81,11 +81,6 @@ def cg(
     if torch.vdot(residual.flatten(), residual.flatten()) == 0:
         return solution
 
-    # squared tolerance;
-    # (we will check ||residual||^2 < tolerance^2 instead of ||residual|| < tol
-    # to avoid the computation of the root for the norm)
-    tolerance_squared = tolerance**2
-
     # dummy value. new value will be set in loop before first usage
     residual_norm_squared_previous = None
 
@@ -95,7 +90,7 @@ def cg(
         residual_norm_squared = torch.vdot(residual_flat, residual_flat).real
 
         # check if the solution is already accurate enough
-        if tolerance != 0 and (residual_norm_squared < tolerance_squared):
+        if tolerance != 0 and (residual_norm_squared < tolerance**2):
             return solution
 
         if iteration > 0:
@@ -105,8 +100,8 @@ def cg(
         # update estimates of the solution and the residual
         (operator_conjugate_vector,) = operator(conjugate_vector)
         alpha = residual_norm_squared / (torch.vdot(conjugate_vector.flatten(), operator_conjugate_vector.flatten()))
-        solution += alpha * conjugate_vector
-        residual -= alpha * operator_conjugate_vector
+        solution = solution + alpha * conjugate_vector
+        residual = residual - alpha * operator_conjugate_vector
 
         residual_norm_squared_previous = residual_norm_squared
 

src/mrpro/data/AcqInfo.py

@@ -1,6 +1,6 @@
 """Acquisition information dataclass."""
 
-from collections.abc import Callable, Sequence
+from collections.abc import Sequence
 from dataclasses import dataclass
 
 import ismrmrd
@@ -206,17 +206,13 @@ 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']),
@@ -251,10 +247,10 @@ 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),
+            patient_table_position=spatialdimension_2d(headers['patient_table_position']).apply_(mm_to_m),
             phase_dir=spatialdimension_2d(headers['phase_dir']),
             physiology_time_stamp=tensor_2d(headers['physiology_time_stamp']),
-            position=spatialdimension_2d(headers['position'], mm_to_m),
+            position=spatialdimension_2d(headers['position']).apply_(mm_to_m),
             read_dir=spatialdimension_2d(headers['read_dir']),
             sample_time_us=tensor_2d(headers['sample_time_us']),
             scan_counter=tensor_2d(headers['scan_counter']),