MAINT: Use ruff for linting and formatting

.pre-commit-config.yaml

@@ -3,20 +3,12 @@ repos:
     rev: v4.4.0
     hooks:
       - id: check-yaml
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: '23.11.0'
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.7
     hooks:
-      - id: black
-  - repo: https://github.com/PyCQA/isort
-    rev: '5.12.0'
-    hooks:
-      - id: isort
-  - repo: https://github.com/PyCQA/flake8
-    rev: '6.1.0'
-    hooks:
-      - id: flake8
-        additional_dependencies:
-          - flake8-pyproject
+      - id: ruff
+        args: [ --fix ]
+      - id: ruff-format
   - repo: https://github.com/codespell-project/codespell
     rev: v2.2.6
     hooks:

clinica/iotools/bids_utils.py

@@ -402,7 +402,7 @@ def create_scans_dict(
                 # Some flutemeta lines contain a non-coded string value at the second-to-last position. This value
                 # contains a comma which adds an extra column and shifts the remaining values to the right. In this
                 # case, we just remove the erroneous content and replace it with -4 which AIBL uses as n/a value.
-                on_bad_lines = (
+                on_bad_lines = (  # noqa: E731
                     lambda bad_line: bad_line[:-3] + [-4, bad_line[-1]]
                     if "flutemeta" in file_path and study_name == "AIBL"
                     else "error"

clinica/pipelines/dwi_connectome/dwi_connectome_pipeline.py

@@ -245,11 +245,11 @@ def build_core_nodes(self):
         import nipype.interfaces.mrtrix3 as mrtrix3
         import nipype.interfaces.utility as niu
         import nipype.pipeline.engine as npe
+        from nipype.interfaces.mrtrix.preprocess import MRTransform
         from nipype.interfaces.mrtrix3 import (
             ConstrainedSphericalDeconvolution,
             Tractography,
         )
-        from nipype.interfaces.mrtrix.preprocess import MRTransform
 
         import clinica.pipelines.dwi_connectome.dwi_connectome_utils as utils
         from clinica.utils.exceptions import ClinicaCAPSError

clinica/pipelines/dwi_dti/dwi_dti_pipeline.py

@@ -213,8 +213,8 @@ def build_core_nodes(self):
         import nipype.interfaces.utility as nutil
         import nipype.pipeline.engine as npe
         from nipype.interfaces.ants import ApplyTransforms, RegistrationSynQuick
-        from nipype.interfaces.mrtrix3 import TensorMetrics
         from nipype.interfaces.mrtrix.preprocess import DWI2Tensor
+        from nipype.interfaces.mrtrix3 import TensorMetrics
 
         from clinica.utils.check_dependency import check_environment_variable
         from clinica.utils.dwi import extract_bids_identifier_from_filename

clinica/pipelines/dwi_preprocessing_using_fmap/dwi_preprocessing_using_phasediff_fmap_pipeline.py

@@ -278,8 +278,8 @@ def build_core_nodes(self):
         """Build and connect the core nodes of the pipeline."""
         import nipype.interfaces.fsl as fsl
         import nipype.interfaces.mrtrix3 as mrtrix3
-        import nipype.interfaces.utility as nutil
         import nipype.interfaces.utility as niu
+        import nipype.interfaces.utility as nutil
         import nipype.pipeline.engine as npe
 
         from clinica.pipelines.dwi_preprocessing_using_t1.dwi_preprocessing_using_t1_workflows import (

clinica/pipelines/machine_learning/algorithm.py

@@ -457,9 +457,9 @@ def _select_best_parameter(self, async_result):
         best_min_samples_split = int(round(np.mean([x[2] for x in params_list])))
 
         def max_feature_to_float(m):
-            if type(m) is float:
+            if isinstance(m, float):
                 return m
-            if type(m) is int:
+            if isinstance(m, int):
                 return float(m) / float(self._x.shape[1])
             if m == "auto" or m == "sqrt":
                 return np.sqrt(self._x.shape[1]) / float(self._x.shape[1])

clinica/pipelines/machine_learning/vertex_based_io.py

@@ -13,9 +13,7 @@ def load_data(mgh_list):
 
     # Construct 0-matrix with the good size, based on the size of the surfaces
     # provided by the first subject
-    N_vertex = (
-        []
-    )  # array containing the surface size of the different surfaces of a subject
+    N_vertex = []  # array containing the surface size of the different surfaces of a subject
     sample = mgh_list[0]
     for i in range(len(sample)):
         N_vertex.append(np.max(nib.load(sample[i]).header.get_data_shape()))

clinica/pipelines/machine_learning_spatial_svm/spatial_svm_utils.py

@@ -262,8 +262,8 @@ def roots_poly(C):
             ]
         )
         # two roots
-        rts1 = (-C[1, :] + delta) * (1 / ((2 * C[0, :])))
-        rts2 = (-C[1, :] - delta) * (1 / ((2 * C[0, :])))
+        rts1 = (-C[1, :] + delta) * (1 / (2 * C[0, :]))
+        rts2 = (-C[1, :] - delta) * (1 / (2 * C[0, :]))
         rts = np.array([rts1, rts2])
 
     elif C.shape[0] < 5:

pyproject.toml

@@ -93,35 +93,22 @@ clinica = "clinica.cmdline:main"
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
 
-[tool.black]
+[tool.ruff]
+target-version = "py38"
 line-length = 88
-target-version = ['py36', 'py37', 'py38']
-include = '\.pyi?$'
-force-exclude = '''
-/(
-    \.eggs
-  | \.git
-  | \.hg
-  | \.mypy_cache
-  | \.tox
-  | \.venv
-  | \.pytest_cache
-  | _build
-  | buck-out
-  | build
-  | dist
-  | docs
-  | clinica/lib
-)/
-'''
 
-[tool.isort]
-profile = "black"
+[tool.ruff.lint]
+select = ["E", "W", "I001"]
+ignore = ["E203", "E501"]
 
-[tool.flake8]
-max-line-length = 88
-select = ["E", "W"]
-extend-ignore = ["E203", "E501", "W503"]
+[tool.ruff.lint.isort]
+known-first-party = ["clinica"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
 
 [tool.codespell]
 summary = ''

test/unittests/pipelines/dwi_preprocessing_using_t1/test_dwi_preprocessing_using_t1_utils.py

@@ -360,22 +360,25 @@ def test_prepare_reference_b0(tmp_path, mocker):
     assert reference_dataset.dwi == tmp_path / "sub-foo_ses-bar_dwi_merged.nii.gz"
     assert reference_dataset.b_values == tmp_path / "sub-foo_ses-bar_dwi_merged.bval"
     assert reference_dataset.b_vectors == tmp_path / "sub-foo_ses-bar_dwi_merged.bvec"
-    assert sorted([p.name for p in tmp_path.iterdir()]) == [
-        "reference_b0_volume.nii.gz",  # This is the 3D volume corresponding to the co-registered volumes for b<=low_b
-        "sub-foo_ses-bar_dwi.bval",  # Initial bvalue file
-        "sub-foo_ses-bar_dwi.bvec",  # Initial bvectors file
-        "sub-foo_ses-bar_dwi.nii.gz",  # Initial dwi image file
-        "sub-foo_ses-bar_dwi_large_b.bval",  # bvalue file corresponding to DWI volumes with b>low_b
-        "sub-foo_ses-bar_dwi_large_b.bvec",  # bvectors file corresponding to DWI volumes with b>low_b
-        "sub-foo_ses-bar_dwi_large_b.nii.gz",  # DWI image file holding volumes for which b>low_b
-        "sub-foo_ses-bar_dwi_merged.bval",  # bvalue file corresponding to the merged DWI dataset
-        "sub-foo_ses-bar_dwi_merged.bvec",  # bvectors file corresponding to the merged DWI dataset
-        "sub-foo_ses-bar_dwi_merged.nii.gz",  # image file holding the merged DWI volumes
-        "sub-foo_ses-bar_dwi_small_b.bval",  # bvalue file corresponding to the volumes for which b<=low_b
-        "sub-foo_ses-bar_dwi_small_b.bvec",  # bvectors file corresponding to the volumes for which b<=low_b
-        "sub-foo_ses-bar_dwi_small_b.nii.gz",  # DWI image file holding volumes for which b<=low_b
-        "tmp",  # Working directory containing all the stuff generated by b0_flirt_pipeline
-    ]
+    assert (
+        sorted([p.name for p in tmp_path.iterdir()])
+        == [
+            "reference_b0_volume.nii.gz",  # This is the 3D volume corresponding to the co-registered volumes for b<=low_b
+            "sub-foo_ses-bar_dwi.bval",  # Initial bvalue file
+            "sub-foo_ses-bar_dwi.bvec",  # Initial bvectors file
+            "sub-foo_ses-bar_dwi.nii.gz",  # Initial dwi image file
+            "sub-foo_ses-bar_dwi_large_b.bval",  # bvalue file corresponding to DWI volumes with b>low_b
+            "sub-foo_ses-bar_dwi_large_b.bvec",  # bvectors file corresponding to DWI volumes with b>low_b
+            "sub-foo_ses-bar_dwi_large_b.nii.gz",  # DWI image file holding volumes for which b>low_b
+            "sub-foo_ses-bar_dwi_merged.bval",  # bvalue file corresponding to the merged DWI dataset
+            "sub-foo_ses-bar_dwi_merged.bvec",  # bvectors file corresponding to the merged DWI dataset
+            "sub-foo_ses-bar_dwi_merged.nii.gz",  # image file holding the merged DWI volumes
+            "sub-foo_ses-bar_dwi_small_b.bval",  # bvalue file corresponding to the volumes for which b<=low_b
+            "sub-foo_ses-bar_dwi_small_b.bvec",  # bvectors file corresponding to the volumes for which b<=low_b
+            "sub-foo_ses-bar_dwi_small_b.nii.gz",  # DWI image file holding volumes for which b<=low_b
+            "tmp",  # Working directory containing all the stuff generated by b0_flirt_pipeline
+        ]
+    )
     ref_b0_volume = nib.load(tmp_path / "reference_b0_volume.nii.gz")
     assert_array_equal(ref_b0_volume.get_fdata(), 5.0 * np.ones((5, 5, 5, 1)))
     large_b_image = nib.load(tmp_path / "sub-foo_ses-bar_dwi_large_b.nii.gz")

test/unittests/utils/test_freesurfer.py

@@ -4,10 +4,10 @@
 import pandas as pd
 import pytest
 
-from clinica.utils.freesurfer import _get_prefix  # noqa
 from clinica.utils.freesurfer import (
     ColumnType,
     InfoType,
+    _get_prefix,  # noqa
     extract_image_id_from_longitudinal_segmentation,
 )