fix error D209 from ruff (nilearn#4583)

examples/04_glm_first_level/plot_first_level_details.py

@@ -194,7 +194,8 @@ def make_localizer_contrasts(design_matrix):
 
 def plot_contrast(first_level_model):
     """Specify, estimate and plot the main contrasts \
-        for given a first model."""
+        for given a first model.
+    """
     design_matrix = first_level_model.design_matrices_[0]
     # Call the contrast specification within the function
     contrasts = make_localizer_contrasts(design_matrix)

nilearn/_utils/niimg_conversions.py

@@ -21,7 +21,8 @@
 
 def _check_fov(img, affine, shape):
     """Return True if img's field of view correspond to given \
-    shape and affine, False elsewhere."""
+    shape and affine, False elsewhere.
+    """
     img = check_niimg(img)
     return img.shape[:3] == shape and np.allclose(img.affine, affine)
 

nilearn/_utils/segmentation.py

@@ -130,7 +130,8 @@ def _buildAB(lap_sparse, labels):
 
 def _mask_edges_weights(edges, weights, mask):
     """Remove edges of the graph connected to masked nodes, \
-    as well as corresponding weights of the edges."""
+    as well as corresponding weights of the edges.
+    """
     mask0 = np.hstack(
         (mask[:, :, :-1].ravel(), mask[:, :-1].ravel(), mask[:-1].ravel())
     )

nilearn/_utils/tests/test_data_gen.py

@@ -33,7 +33,8 @@
 
 def test_add_metadata_to_bids_derivatives_default_path(tmp_path):
     """Check the filename created is the default value \
-    of add_metadata_to_bids_dataset."""
+    of add_metadata_to_bids_dataset.
+    """
     target_dir = tmp_path / "derivatives" / "sub-01" / "ses-01" / "func"
     target_dir.mkdir(parents=True)
     json_file = add_metadata_to_bids_dataset(

nilearn/_utils/tests/test_helpers.py

@@ -7,7 +7,8 @@
 
 def _mock_args_for_testing_replace_parameter():
     """Create mock deprecated & replacement parameters for use \
-       with testing functions related to replace_parameters()."""
+       with testing functions related to replace_parameters().
+    """
     mock_kwargs_with_deprecated_params_used = {
         "unchanged_param_0": "unchanged_param_0_val",
         "deprecated_param_0": "deprecated_param_0_val",
@@ -82,7 +83,8 @@ def mock_function(
 
 def test_transfer_deprecated_param_vals():
     """Check that values assigned to deprecated parameters are \
-       correctly reassigned to the replacement parameters."""
+       correctly reassigned to the replacement parameters.
+    """
     mock_input, replacement_params = _mock_args_for_testing_replace_parameter()
     expected_output = {
         "unchanged_param_0": "unchanged_param_0_val",

nilearn/connectome/__init__.py

@@ -1,6 +1,7 @@
 """Tools for computing functional connectivity matrices \
 and also implementation of algorithm for sparse multi subjects learning \
-of Gaussian graphical models."""
+of Gaussian graphical models.
+"""
 
 from .connectivity_matrices import (
     ConnectivityMeasure,

nilearn/connectome/group_sparse_cov.py

@@ -1,5 +1,6 @@
 """Implementation of algorithm for sparse multi-subjects learning of Gaussian \
-graphical models."""
+graphical models.
+"""
 
 # Authors: Philippe Gervais
 

nilearn/connectome/tests/test_connectivity_matrices.py

@@ -101,7 +101,8 @@ def random_spd(p, eig_min, cond, random_state=0):
 
 def _signals(n_subjects=N_SUBJECTS):
     """Generate signals and compute covariances \
-    and apply confounds while computing covariances."""
+    and apply confounds while computing covariances.
+    """
     n_features = N_FEATURES
     signals = []
     for k in range(n_subjects):
@@ -646,7 +647,8 @@ def _assert_connectivity_precision(connectivities, covs):
     """Estimated precision matrix: \
     - is positive definite, \
     - its product with the true covariance matrix \
-      is close to the identity matrix."""
+      is close to the identity matrix.
+    """
     for true_covariance_matrix, estimated_covariance_matrix in zip(
         covs, connectivities
     ):

nilearn/datasets/func.py

@@ -1,5 +1,6 @@
 """Downloading NeuroImaging datasets: \
-functional datasets (task + resting-state)."""
+functional datasets (task + resting-state).
+"""
 
 import fnmatch
 import glob
@@ -1293,7 +1294,8 @@ def fetch_abide_pcp(
 def _load_mixed_gambles(zmap_imgs):
     """Ravel zmaps (one per subject) along time axis, resulting, \
     in a n_subjects * n_trials 3D niimgs and, and then make \
-    gain vector y of same length."""
+    gain vector y of same length.
+    """
     X = []
     y = []
     mask = []

nilearn/datasets/neurovault.py

@@ -1,5 +1,6 @@
 """Download statistical maps available \
-on Neurovault (https://neurovault.org)."""
+on Neurovault (https://neurovault.org).
+"""
 
 # Author: Jerome Dockes
 

nilearn/datasets/tests/test_utils.py

@@ -66,7 +66,8 @@
 
 def test_get_dataset_descr_warning():
     """Tests that function ``get_dataset_descr()`` gives a warning \
-       when no description is available."""
+       when no description is available.
+    """
     with pytest.warns(
         UserWarning, match="Could not find dataset description."
     ):

nilearn/decoding/decoder.py

@@ -1057,7 +1057,8 @@ def _binarize_y(self, y):
 
     def _predict_dummy(self, n_samples):
         """Non-sparse scikit-learn based prediction steps for classification \
-        and regression."""
+        and regression.
+        """
         if len(self.dummy_output_) == 1:
             dummy_output = self.dummy_output_[0]
         else:

nilearn/decoding/searchlight.py

@@ -1,7 +1,8 @@
 """The searchlight is a widely used approach for the study \
 of the fine-grained patterns of information in fMRI analysis, \
 in which multivariate statistical relationships are iteratively tested \
-in the neighborhood of each location of a domain."""
+in the neighborhood of each location of a domain.
+"""
 
 # Authors : Vincent Michel ([email protected])
 #           Alexandre Gramfort ([email protected])

nilearn/decoding/space_net.py

@@ -45,7 +45,8 @@
 
 def _crop_mask(mask):
     """Crops input mask to produce tighter (i.e smaller) bounding box \
-    with the same support (active voxels)."""
+    with the same support (active voxels).
+    """
     idx = np.where(mask)
     if idx[0].size == 0:
         raise ValueError(
@@ -809,7 +810,8 @@ def check_params(self):
 
     def _set_coef_and_intercept(self, w):
         """Set the loadings vector (coef) and the intercept of the fitted \
-        model."""
+        model.
+        """
         self.w_ = np.array(w)
         if self.w_.ndim == 1:
             self.w_ = self.w_[np.newaxis, :]

nilearn/decoding/space_net_solvers.py

@@ -177,7 +177,8 @@ def _squared_loss_derivative_lipschitz_constant(
 ):
     """Compute the lipschitz constant of the gradient of the smooth part \
     of the Graph-Net regression problem (squared_loss + grad_weight*grad) \
-    via power method."""
+    via power method.
+    """
     rng = np.random.RandomState(42)
     a = rng.randn(X.shape[1])
     a /= sqrt(np.dot(a, a))
@@ -215,7 +216,8 @@ def _logistic_derivative_lipschitz_constant(
     """Compute the lipschitz constant of the gradient of the smooth part \
     of the Graph-Net classification problem (logistic_loss + \
     grad_weight*grad) via analytical formula on the logistic loss + \
-    power method on the smooth part."""
+    power method on the smooth part.
+    """
     # L. constant for the data term (logistic)
     # data_constant = sp.linalg.norm(X, 2) ** 2
     data_constant = logistic_loss_lipschitz_constant(X)
@@ -238,7 +240,8 @@ def _logistic_derivative_lipschitz_constant(
 
 def _logistic_data_loss_and_spatial_grad(X, y, w, mask, grad_weight):
     """Compute the smooth part of the Graph-Net objective, \
-    with logistic loss."""
+    with logistic loss.
+    """
     grad_buffer = np.zeros(mask.shape)
     grad_buffer[mask] = w[:-1]
     grad_mask = np.array([mask for _ in range(mask.ndim)])

nilearn/decoding/tests/test_decoder.py

@@ -360,7 +360,8 @@ def test_check_unsupported_estimator(estimator):
 
 def test_parallel_fit(rand_X_Y):
     """Check that results of _parallel_fit is the same \
-    for different controlled param_grid."""
+    for different controlled param_grid.
+    """
     X, y = make_regression(
         n_samples=N_SAMPLES,
         n_features=20,
@@ -439,7 +440,8 @@ def test_parallel_fit_builtin_cv(
 ):
     """Check that the `fitted_param_name` output of _parallel_fit is \
        a single value even if param_grid is wrapped in a list \
-       for models with built-in CV."""
+       for models with built-in CV.
+    """
     # y will be replaced if this is a classification
     X, y = make_regression(
         n_samples=N_SAMPLES,
@@ -912,7 +914,8 @@ def test_decoder_multiclass_classification_cross_validation(
 
 def test_decoder_multiclass_classification_apply_mask_shape():
     """Test whether if _apply mask output has the same shape \
-    as original matrix."""
+    as original matrix.
+    """
     X_init, _ = make_classification(
         n_samples=200,
         n_features=125,

nilearn/decoding/tests/test_graph_net.py

@@ -87,7 +87,8 @@ def test_adjointness(rng, size=4):
 
 def test_identity_adjointness(rng, size=4):
     """Test adjointess between _graph_net_data_function and \
-    _graph_net_adjoint_data_function, with identity design matrix."""
+    _graph_net_adjoint_data_function, with identity design matrix.
+    """
     # A mask full of ones
     mask = np.ones((size, size, size), dtype=bool)
 
@@ -184,7 +185,8 @@ def func_grad(w):
 
 def test_squared_loss_derivative_lipschitz_constant(rng):
     """Test Lipschitz-continuity of the derivative of squared_loss loss \
-    function."""
+    function.
+    """
     X, y, w, mask, *_ = _make_data()
     grad_weight = 2.08e-1
 
@@ -235,7 +237,8 @@ def test_logistic_derivative_lipschitz_constant(rng):
 @pytest.mark.parametrize("l1_ratio", np.linspace(0.1, 1, 3))
 def test_max_alpha_squared_loss(l1_ratio):
     """Tests that models with L1 regularization over the theoretical bound \
-    are full of zeros, for logistic regression."""
+    are full of zeros, for logistic regression.
+    """
     X, y, _, _, mask_, X_ = _make_data()
 
     reg = BaseSpaceNet(

nilearn/decoding/tests/test_same_api.py

@@ -202,7 +202,8 @@ def test_graph_net_and_tvl1_same_for_pure_l1_BaseSpaceNet(
 
 def test_graph_net_and_tvl1_same_for_pure_l1_logistic(max_iter=20, decimal=2):
     """Check graph_net_solver and tvl1_solver should give same results \
-    when l1_ratio = 1."""
+    when l1_ratio = 1.
+    """
     iris = load_iris()
     X, y = iris.data, iris.target
     y = y > 0.0
@@ -232,7 +233,8 @@ def test_graph_net_and_tvl1_same_for_pure_l1_logistic_spacenet_classifier(
     standardize, max_iter=20, decimal=2
 ):
     """Check graph_net_solver and tvl1_solver should give same results \
-    when l1_ratio = 1."""
+    when l1_ratio = 1.
+    """
     iris = load_iris()
     X, y = iris.data, iris.target
     y = y > 0.0
@@ -266,7 +268,8 @@ def test_graph_net_and_tv_same_for_pure_l1_another_test(
     standardize, decimal=1
 ):
     """Check that graph_net_solver and tvl1_solver give same results \
-    when l1_ratio = 1."""
+    when l1_ratio = 1.
+    """
     dim = (3, 3, 3)
     X, y, _, mask = _make_data(masked=True, dim=dim)
     X, mask = to_niimgs(X, dim)

nilearn/decomposition/__init__.py

@@ -1,5 +1,6 @@
 """The :mod:`nilearn.decomposition` module includes a subject level \
-variant of the :term:`ICA` called Canonical :term:`ICA`."""
+variant of the :term:`ICA` called Canonical :term:`ICA`.
+"""
 
 from .canica import CanICA
 from .dict_learning import DictLearning

nilearn/decomposition/_base.py

@@ -534,7 +534,8 @@ def inverse_transform(self, loadings):
 
     def _sort_by_score(self, data):
         """Sort components on the explained variance over data of estimator \
-        components_."""
+        components_.
+        """
         components_score = self._raw_score(data, per_component=True)
         order = np.argsort(components_score)[::-1]
         self.components_ = self.components_[order]

nilearn/decomposition/canica.py

@@ -212,7 +212,8 @@ def __init__(
 
     def _unmix_components(self, components):
         """Core function of CanICA than rotate components_ to maximize \
-        independence."""
+        independence.
+        """
         random_state = check_random_state(self.random_state)
 
         seeds = random_state.randint(np.iinfo(np.int32).max, size=self.n_init)

nilearn/decomposition/tests/test_base.py

@@ -91,7 +91,8 @@ def test_mask_reducer_single_image_same_with_multiple_jobs(
     data_for_mask_and_reduce, masker, shape_3d_default
 ):
     """Mask and reduce a 3D image and check results is the same \
-    when split over several CPUs."""
+    when split over several CPUs.
+    """
     data_single = _mask_and_reduce(
         masker, data_for_mask_and_reduce[0], n_components=3
     )

nilearn/decomposition/tests/test_canica.py

@@ -111,7 +111,8 @@ def canica_data():
 
 def test_threshold_bound_error():
     """Test that an error is raised when the threshold is higher \
-    than the number of components."""
+    than the number of components.
+    """
     with pytest.raises(ValueError, match="Threshold must not be higher"):
         CanICA(n_components=4, threshold=5.0)
 

nilearn/decomposition/tests/test_multi_pca.py

@@ -58,7 +58,8 @@ def test_multi_pca_check_masker_attributes(multi_pca_data, mask_img):
 
 def test_multi_pca(multi_pca_data, mask_img):
     """Components are the same if we put twice the same data, \
-       and that fit output is deterministic."""
+       and that fit output is deterministic.
+    """
     multi_pca = _MultiPCA(mask=mask_img, n_components=3, random_state=0)
     multi_pca.fit(multi_pca_data)
 
@@ -79,7 +80,8 @@ def test_multi_pca_with_confounds_smoke(multi_pca_data, mask_img):
 
 def test_multi_pca_componenent_errors(mask_img):
     """Test that a ValueError is raised \
-    if the number of components is too low."""
+    if the number of components is too low.
+    """
     multi_pca = _MultiPCA(mask=mask_img)
     with pytest.raises(
         ValueError, match="Object has no components_ attribute."
@@ -129,7 +131,8 @@ def test_multi_pca_with_masker(multi_pca_data):
 
 def test_multi_pca_with_masker_without_cca_smoke(multi_pca_data):
     """Multi-pca can run with a masker \
-        and without canonical correlation analysis."""
+        and without canonical correlation analysis.
+    """
     masker = MultiNiftiMasker(mask_args=dict(opening=0))
 
     multi_pca = _MultiPCA(
@@ -186,7 +189,8 @@ def test_multi_pca_score_single_subject(mask_img):
 
 def test_multi_pca_score_single_subject_n_components(mask_img):
     """Score is one for n_components == n_sample \
-       in single subject configuration."""
+       in single subject configuration.
+    """
     data, _, _, _ = _make_multi_pca_test_data(with_activation=False)
     multi_pca = _MultiPCA(
         mask=mask_img, random_state=0, memory_level=0, n_components=5

nilearn/experimental/surface/_surface_image.py

@@ -62,7 +62,8 @@ def __init__(
 
 class Mesh(abc.ABC):
     """A surface :term:`mesh` having vertex, \
-    coordinates and faces (triangles)."""
+    coordinates and faces (triangles).
+    """
 
     n_vertices: int