[WIP][MAINT] implement walrus operator?

.pre-commit-config.yaml

@@ -51,6 +51,11 @@ repos:
     hooks:
     -   id: flynt
 
+-   repo: https://github.com/MarcoGorelli/auto-walrus
+    rev: v0.2.2
+    hooks:
+    -   id: auto-walrus
+
 -   repo: https://github.com/asottile/pyupgrade
     rev: v3.15.0
     hooks:

nilearn/datasets/atlas.py

@@ -100,9 +100,8 @@ def fetch_atlas_difumo(
         512: "9b76y",
         1024: "34792",
     }
-    valid_dimensions = [64, 128, 256, 512, 1024]
     valid_resolution_mm = [2, 3]
-    if dimension not in valid_dimensions:
+    if dimension not in (valid_dimensions := [64, 128, 256, 512, 1024]):
         raise ValueError(
             f"Requested dimension={dimension} is not available. "
             f"Valid options: {valid_dimensions}"
@@ -1268,8 +1267,7 @@ def fetch_atlas_aal(
     Licence: unknown.
 
     """
-    versions = ["SPM5", "SPM8", "SPM12"]
-    if version not in versions:
+    if version not in (versions := ["SPM5", "SPM8", "SPM12"]):
         raise ValueError(
             f"The version of AAL requested '{version}' does not exist."
             f"Please choose one among {versions}."
@@ -1405,8 +1403,7 @@ def fetch_atlas_basc_multiscale_2015(
     https://figshare.com/articles/basc/1285615
 
     """
-    versions = ["sym", "asym"]
-    if version not in versions:
+    if version not in (versions := ["sym", "asym"]):
         raise ValueError(
             f"The version of Brain parcellations requested '{version}' "
             "does not exist. "
@@ -2113,20 +2110,17 @@ def fetch_atlas_schaefer_2018(
     Licence: MIT.
 
     """
-    valid_n_rois = list(range(100, 1100, 100))
-    valid_yeo_networks = [7, 17]
-    valid_resolution_mm = [1, 2]
-    if n_rois not in valid_n_rois:
+    if n_rois not in (valid_n_rois := list(range(100, 1100, 100))):
         raise ValueError(
             f"Requested n_rois={n_rois} not available. "
             f"Valid options: {valid_n_rois}"
         )
-    if yeo_networks not in valid_yeo_networks:
+    if yeo_networks not in (valid_yeo_networks := [7, 17]):
         raise ValueError(
             f"Requested yeo_networks={yeo_networks} not available. "
             f"Valid options: {valid_yeo_networks}"
         )
-    if resolution_mm not in valid_resolution_mm:
+    if resolution_mm not in (valid_resolution_mm := [1, 2]):
         raise ValueError(
             f"Requested resolution_mm={resolution_mm} not available. "
             f"Valid options: {valid_resolution_mm}"

nilearn/datasets/func.py

@@ -1492,8 +1492,7 @@ def fetch_megatrawls_netmats(
         "Invalid {0} input is provided: {1}, choose one of them {2}"
     )
     # standard dataset terms
-    dimensionalities = [25, 50, 100, 200, 300]
-    if dimensionality not in dimensionalities:
+    if dimensionality not in (dimensionalities := [25, 50, 100, 200, 300]):
         raise ValueError(
             error_message.format(
                 "dimensionality", dimensionality, dimensionalities
@@ -2092,8 +2091,7 @@ def fetch_development_fmri(
 
 def _filter_func_regressors_by_participants(participants, age_group):
     """Filter functional and regressors based on participants."""
-    valid_age_groups = ("both", "child", "adult")
-    if age_group not in valid_age_groups:
+    if age_group not in (valid_age_groups := ("both", "child", "adult")):
         raise ValueError(
             f"Wrong value for age_group={age_group}. "
             f"Valid arguments are: {valid_age_groups}"
@@ -2983,8 +2981,7 @@ def fetch_spm_multimodal_fmri(
     subject_dir = os.path.join(data_dir, subject_id)
 
     # maybe data_dir already contains the data ?
-    data = _glob_spm_multimodal_fmri_data(subject_dir)
-    if data is not None:
+    if (data := _glob_spm_multimodal_fmri_data(subject_dir)) is not None:
         return data
 
     # No. Download the data
@@ -3040,8 +3037,7 @@ def _glob_fiac_data():
         return Bunch(**_subject_data)
 
     # maybe data_dir already contains the data ?
-    data = _glob_fiac_data()
-    if data is not None:
+    if (data := _glob_fiac_data()) is not None:
         return data
 
     # No. Download the data

nilearn/glm/_utils.py

@@ -172,8 +172,7 @@ def full_rank(X, cmax=1e15):
     """
     U, s, V = spl.svd(X, full_matrices=False)
     smax, smin = s.max(), s.min()
-    cond = smax / smin
-    if cond < cmax:
+    if (cond := smax / smin) < cmax:
         return X, cond
 
     warn("Matrix is singular at working precision, regularizing...")

nilearn/glm/contrasts.py

@@ -93,8 +93,7 @@ def compute_contrast(labels, regression_result, con_val, stat_type=None):
     if stat_type is None:
         stat_type = "t" if dim == 1 else "F"
 
-    acceptable_stat_types = ["t", "F"]
-    if stat_type not in acceptable_stat_types:
+    if stat_type not in (acceptable_stat_types := ["t", "F"]):
         raise ValueError(
             f"'{stat_type}' is not a known contrast type. "
             f"Allowed types are {acceptable_stat_types}."

nilearn/glm/first_level/experimental_paradigm.py

@@ -161,8 +161,9 @@ def _handle_modulation(events):
 
 def _check_unexpected_columns(events):
     """Warn for each unexpected column that will not be used afterwards."""
-    unexpected_columns = list(set(events.columns).difference(VALID_FIELDS))
-    if unexpected_columns:
+    if unexpected_columns := list(
+        set(events.columns).difference(VALID_FIELDS)
+    ):
         warnings.warn(
             "The following unexpected columns "
             "in events data will be ignored: "

nilearn/interfaces/fmriprep/load_confounds_components.py

@@ -77,8 +77,7 @@ def _load_high_pass(confounds_raw):
         DataFrame of high pass filter regressors.
         If not present in file, return an empty DataFrame.
     """
-    high_pass_params = find_confounds(confounds_raw, ["cosine"])
-    if high_pass_params:
+    if high_pass_params := find_confounds(confounds_raw, ["cosine"]):
         return confounds_raw[high_pass_params]
     else:
         return pd.DataFrame()
@@ -287,8 +286,7 @@ def _load_non_steady_state(confounds_raw):
         DataFrame of non steady state regressors generated by fMRIPrep.
         If none were found, return an empty DataFrame.
     """
-    nss_outliers = find_confounds(confounds_raw, ["non_steady_state"])
-    if nss_outliers:
+    if nss_outliers := find_confounds(confounds_raw, ["non_steady_state"]):
         return confounds_raw[nss_outliers]
     else:
         return pd.DataFrame()

nilearn/plotting/img_plotting.py

@@ -906,8 +906,7 @@ def plot_roi(
         (4D images)
 
     """
-    valid_view_types = ["continuous", "contours"]
-    if view_type not in valid_view_types:
+    if view_type not in (valid_view_types := ["continuous", "contours"]):
         raise ValueError(
             f"Unknown view type: {view_type}. "
             f"Valid view types are {valid_view_types}."

nilearn/plotting/matrix_plotting.py

@@ -99,8 +99,7 @@ def _sanitize_labels(mat_shape, labels):
 
 def _sanitize_tri(tri):
     """Help for plot_matrix."""
-    VALID_TRI_VALUES = ("full", "lower", "diag")
-    if tri not in VALID_TRI_VALUES:
+    if tri not in (VALID_TRI_VALUES := ("full", "lower", "diag")):
         raise ValueError(
             "Parameter tri needs to be one of: "
             f"{', '.join(VALID_TRI_VALUES)}."

nilearn/signal.py

@@ -1014,9 +1014,8 @@ def _sanitize_sample_mask(n_time, n_runs, runs, sample_mask):
 def _check_sample_mask_index(i, n_runs, runs, current_mask):
     """Ensure the index in sample mask is valid."""
     len_run = sum(i == runs)
-    len_current_mask = len(current_mask)
     # sample_mask longer than signal
-    if len_current_mask > len_run:
+    if (len_current_mask := len(current_mask)) > len_run:
         raise IndexError(
             f"sample_mask {i + 1} of {n_runs} is has more timepoints "
             f"than the current run ;sample_mask contains {len_current_mask} "

pyproject.toml

@@ -62,7 +62,7 @@ doc = [
     "sphinx-design",
     "sphinx-gallery",
     "sphinxcontrib-bibtex",
-    "sphinxext-opengraph",
+    "sphinxext-opengraph"
 ]
 # the following is kept for "backward compatibility"
 plotly = ["nilearn[plotting]"]