Merge pull request #105 from Parietal-INRIA/maint/add-ruff

Use ruff for linting and formatting

.github/workflows/testing.yml

@@ -26,9 +26,8 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -e .[dev]
-      - name: Flake8
-        run: |
-          flake8 --verbose .
+      - name: Run Ruff
+        run: ruff check --output-format=github .
       - name: Test with pytest
         run: |
           pytest

.pre-commit-config.yaml

@@ -14,19 +14,26 @@ repos:
       - id: trailing-whitespace
       - id: check-toml
 
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+  # Checks for .rst files
+  - repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.10.0
     hooks:
-      - id: isort
-        args: ["--profile", "black"]
+      - id: rst-directive-colons
+      - id: rst-inline-touching-normal
 
-  - repo: https://github.com/psf/black
-    rev: 23.7.0
+  # Format TOML files
+  - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
+    rev: v2.14.0
     hooks:
-      - id: black
+      - id: pretty-format-toml
+        args: [--autofix, --indent, '4']
 
-  - repo: https://github.com/pyCQA/flake8
-    rev: 6.1.0
+  # Lint and format with Ruff
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.0
     hooks:
-      - id: flake8
-        additional_dependencies: [flake8-docstrings, flake8-use-fstring]
+    # Run the linter.
+      - id: ruff
+        args: ["check", "--select", "I", "--fix", "--show-fixes"]
+    # Run the formatter.
+      - id: ruff-format

doc/conf.py

@@ -12,7 +12,6 @@
 import sys
 from pathlib import Path
 
-import sphinx
 
 import fmralign
 

doc/sphinxext/github_link.py

@@ -23,12 +23,14 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
     This is called by sphinx.ext.linkcode
 
     An example with a long-untouched module that everyone has
-    >>> _linkcode_resolve('py', {'module': 'tty',
-    ...                          'fullname': 'setraw'},
-    ...                   package='tty',
-    ...                   url_fmt='https://hg.python.org/cpython/file/'
-    ...                           '{revision}/Lib/{package}/{path}#L{lineno}',
-    ...                   revision='xxxx')
+    >>> _linkcode_resolve(
+    ...     "py",
+    ...     {"module": "tty", "fullname": "setraw"},
+    ...     package="tty",
+    ...     url_fmt="https://hg.python.org/cpython/file/"
+    ...     "{revision}/Lib/{package}/{path}#L{lineno}",
+    ...     revision="xxxx",
+    ... )
     'https://hg.python.org/cpython/file/xxxx/Lib/tty/tty.py#L18'
     """
 
@@ -59,12 +61,16 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
     if not fn:
         return
 
-    fn = os.path.relpath(fn, start=os.path.dirname(__import__(package).__file__))
+    fn = os.path.relpath(
+        fn, start=os.path.dirname(__import__(package).__file__)
+    )
     try:
         lineno = inspect.getsourcelines(obj)[1]
     except Exception:
         lineno = ""
-    return url_fmt.format(revision=revision, package=package, path=fn, lineno=lineno)
+    return url_fmt.format(
+        revision=revision, package=package, path=fn, lineno=lineno
+    )
 
 
 def make_linkcode_resolve(package, url_fmt):

examples/plot_alignment_methods_benchmark.py

@@ -44,7 +44,9 @@
 # Select visual cortex, create a mask and resample it to the right resolution
 
 mask_visual = new_img_like(atlas, atlas.get_fdata() == 1)
-resampled_mask_visual = resample_to_img(mask_visual, mask, interpolation="nearest")
+resampled_mask_visual = resample_to_img(
+    mask_visual, mask, interpolation="nearest"
+)
 
 # Plot the mask we will use
 plotting.plot_roi(

examples/plot_alignment_simulated_2D_data.py

@@ -146,7 +146,9 @@ def _plot_distributions_and_alignment(
 # We make a target distribution Y as well
 Y = np.asarray(
     [
-        _rotate((x[origin_index], y[origin_index]), (x_, y_), math.radians(-10))
+        _rotate(
+            (x[origin_index], y[origin_index]), (x_, y_), math.radians(-10)
+        )
         for x_, y_ in zip(x, y)
     ]
 )

examples/plot_int_alignment.py

@@ -104,6 +104,7 @@
 #
 
 from nilearn.image import index_img
+
 from fmralign.alignment_methods import IndividualizedNeuralTuning
 from fmralign.hyperalignment.piecewise_alignment import PiecewiseAlignment
 from fmralign.hyperalignment.regions import compute_parcels
@@ -126,7 +127,9 @@
 target_test_masked = np.array(masked_imgs)[:, test_index, :]
 
 
-parcels = compute_parcels(niimg=template_train[0], mask=masker, n_parcels=100, n_jobs=5)
+parcels = compute_parcels(
+    niimg=template_train[0], mask=masker, n_parcels=100, n_jobs=5
+)
 denoiser = PiecewiseAlignment(n_jobs=5)
 denoised_signal = denoiser.fit_transform(X=denoising_data, regions=parcels)
 target_denoised_data = denoised_signal[-1]

examples/plot_pairwise_roi_alignment.py

@@ -49,7 +49,9 @@
 # Select visual cortex, create a mask and resample it to the right resolution
 
 mask_visual = new_img_like(atlas, atlas.get_fdata() == 1)
-resampled_mask_visual = resample_to_img(mask_visual, mask, interpolation="nearest")
+resampled_mask_visual = resample_to_img(
+    mask_visual, mask, interpolation="nearest"
+)
 
 # Plot the mask we will use
 plot_roi(

examples/plot_template_alignment.py

@@ -150,7 +150,9 @@
     score_voxelwise(target_test, prediction_from_average, masker, loss="corr")
 )
 template_score = masker.inverse_transform(
-    score_voxelwise(target_test, prediction_from_template[0], masker, loss="corr")
+    score_voxelwise(
+        target_test, prediction_from_template[0], masker, loss="corr"
+    )
 )
 
 

examples/plot_toy_int_experiment.py

@@ -14,15 +14,16 @@
 --matplotlib`` in a terminal, or use ``jupyter-notebook``.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+
 from fmralign.alignment_methods import IndividualizedNeuralTuning as INT
 from fmralign.fetch_example_data import generate_dummy_signal
 from fmralign.hyperalignment.correlation import (
-    tuning_correlation,
-    stimulus_correlation,
     compute_pearson_corr,
     matrix_MDS,
+    stimulus_correlation,
+    tuning_correlation,
 )
 
 ###############################################################################
@@ -143,7 +144,10 @@
 # MDS of tunning matrices
 corr_tunning = compute_pearson_corr(tuning_pred_run_1, tuning_pred_run_2)
 T_first_part_transformed, T_second_part_transformed = matrix_MDS(
-    tuning_pred_run_1, tuning_pred_run_2, n_components=2, dissimilarity=1 - corr_tunning
+    tuning_pred_run_1,
+    tuning_pred_run_2,
+    n_components=2,
+    dissimilarity=(1 - corr_tunning),
 )
 
 ax[0, 2].scatter(
@@ -171,16 +175,22 @@
 ax[1, 0].set_title("Correlation of estimated stimulus vs ground truth (Run 1)")
 ax[1, 0].set_xlabel("Latent components, Run 1")
 ax[1, 0].set_ylabel("Latent components, ground truth")
-fig.colorbar(ax[1, 0].imshow(correlation_stimulus_true_est_first_part), ax=ax[1, 0])
+fig.colorbar(
+    ax[1, 0].imshow(correlation_stimulus_true_est_first_part), ax=ax[1, 0]
+)
 
 correlation_stimulus_true_est_second_part = stimulus_correlation(
     stimulus_pred_run_2.T, stimulus_run_2.T
 )
 ax[1, 1].imshow(correlation_stimulus_true_est_second_part)
-ax[1, 1].set_title("Correlation of estimated stimulus vs ground truth (Run 2))")
+ax[1, 1].set_title(
+    "Correlation of estimated stimulus vs ground truth (Run 2))"
+)
 ax[1, 1].set_xlabel("Latent components, Run 2")
 ax[1, 1].set_ylabel("Latent components, ground truth")
-fig.colorbar(ax[1, 1].imshow(correlation_stimulus_true_est_second_part), ax=ax[1, 1])
+fig.colorbar(
+    ax[1, 1].imshow(correlation_stimulus_true_est_second_part), ax=ax[1, 1]
+)
 
 
 # Reconstruction

fmralign/_utils.py

@@ -16,7 +16,9 @@ def _intersect_clustering_mask(clustering, mask):
     new_ = np.zeros_like(dat)
     new_[dat > 0] = 1
     clustering_mask = new_img_like(clustering, new_)
-    return intersect_masks([clustering_mask, mask], threshold=1, connected=True)
+    return intersect_masks(
+        [clustering_mask, mask], threshold=1, connected=True
+    )
 
 
 def piecewise_transform(labels, estimators, X):
@@ -42,7 +44,9 @@ def piecewise_transform(labels, estimators, X):
 
     for i in range(len(unique_labels)):
         label = unique_labels[i]
-        X_transform[:, labels == label] = estimators[i].transform(X[:, labels == label])
+        X_transform[:, labels == label] = estimators[i].transform(
+            X[:, labels == label]
+        )
     return X_transform
 
 
@@ -71,7 +75,13 @@ def _check_labels(labels, threshold=1000):
 
 
 def _make_parcellation(
-    imgs, clustering_index, clustering, n_pieces, masker, smoothing_fwhm=5, verbose=0
+    imgs,
+    clustering_index,
+    clustering,
+    n_pieces,
+    masker,
+    smoothing_fwhm=5,
+    verbose=0,
 ):
     """
     Use nilearn Parcellation class in our pipeline.
@@ -106,13 +116,17 @@ def _make_parcellation(
         Parcellation of features in clusters
     """
     # check if clustering is provided
-    if isinstance(clustering, nib.nifti1.Nifti1Image) or os.path.isfile(clustering):
+    if isinstance(clustering, nib.nifti1.Nifti1Image) or os.path.isfile(
+        clustering
+    ):
         check_same_fov(masker.mask_img_, clustering)
         labels = apply_mask_fmri(clustering, masker.mask_img_).astype(int)
 
     # otherwise check it's needed, if not return 1 everywhere
     elif n_pieces == 1:
-        labels = np.ones(int(masker.mask_img_.get_fdata().sum()), dtype=np.int8)
+        labels = np.ones(
+            int(masker.mask_img_.get_fdata().sum()), dtype=np.int8
+        )
 
     # otherwise check requested clustering method
     elif isinstance(clustering, str) and n_pieces > 1:
@@ -140,7 +154,9 @@ def _make_parcellation(
             )
             err.args += (errmsg,)
             raise err
-        labels = apply_mask_fmri(parcellation.labels_img_, masker.mask_img_).astype(int)
+        labels = apply_mask_fmri(
+            parcellation.labels_img_, masker.mask_img_
+        ).astype(int)
 
     if verbose > 0:
         unique_labels, counts = np.unique(labels, return_counts=True)

fmralign/alignment_methods.py

@@ -1,13 +1,16 @@
 # -*- coding: utf-8 -*-
 """Module implementing alignment estimators on ndarrays."""
+
 import warnings
 
 import numpy as np
-import torch
+import ot
 import scipy
+import torch
+from fugw.mappings import FUGW, FUGWSparse
+from fugw.scripts import coarse_to_fine, lmds
 from joblib import Parallel, delayed
 from scipy import linalg
-import ot
 from scipy.optimize import linear_sum_assignment
 from scipy.sparse import diags
 from scipy.spatial.distance import cdist
@@ -19,9 +22,6 @@
 from fmralign.hyperalignment.linalg import safe_svd, svd_pca
 from fmralign.hyperalignment.piecewise_alignment import PiecewiseAlignment
 
-from fugw.mappings import FUGW, FUGWSparse
-from fugw.scripts import coarse_to_fine, lmds
-
 
 def scaled_procrustes(X, Y, scaling=False, primal=None):
     """

fmralign/fetch_example_data.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 import os
 
+import numpy as np
 import pandas as pd
-from nilearn.datasets._utils import fetch_files, get_dataset_dir
 from fastsrm.srm import projection
-import numpy as np
+from nilearn.datasets._utils import fetch_files, get_dataset_dir
 
 
 def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
@@ -45,9 +45,13 @@ def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
     """
     # The URLs can be retrieved from the nilearn account on OSF
     if subjects == "all":
-        subjects = ["sub-{i:02d}" for i in [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15]]
+        subjects = [
+            "sub-{i:02d}" for i in [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15]
+        ]
     dataset_name = "ibc"
-    data_dir = get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
+    data_dir = get_dataset_dir(
+        dataset_name, data_dir=data_dir, verbose=verbose
+    )
 
     # download or retrieve metadatas, put it in a dataframe,
     # list all condition and specify path to the right directory
@@ -64,7 +68,9 @@ def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
     )
     metadata_df = pd.read_csv(metadata_path[0])
     conditions = metadata_df.condition.unique()
-    metadata_df["path"] = metadata_df["path"].str.replace("path_to_dir", data_dir)
+    metadata_df["path"] = metadata_df["path"].str.replace(
+        "path_to_dir", data_dir
+    )
     # filter the dataframe to return only rows relevant for subjects argument
     metadata_df = metadata_df[metadata_df.subject.isin(subjects)]
 
@@ -202,7 +208,10 @@ def generate_dummy_signal(
     data_train, data_test = [], []
     Ts = []
     for _ in range(n_subjects):
-        if generative_method == "custom" or generative_method == "multiviewica":
+        if (
+            generative_method == "custom"
+            or generative_method == "multiviewica"
+        ):
             W = T_mean + T_std * np.random.randn(latent_dim, n_voxels)
         else:
             W = projection(rng.randn(latent_dim, n_voxels))