complete merge

.git_archival.txt

@@ -1,4 +1,3 @@
 node: $Format:%H$
 node-date: $Format:%cI$
 describe-name: $Format:%(describe:tags=true,match=*[0-9]*)$
-ref-names: $Format:%D$

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ files: ^(.*\.(py|yaml))$
 exclude: ^(\.[^/]*cache/.*|.*/freesurfer/contrib/.*)$
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.3
+    rev: v0.4.4
     hooks:
       - id: ruff
         args: ["--fix"]

mne_bids_pipeline/steps/sensor/_05_decoding_csp.py

@@ -7,11 +7,10 @@
 import mne
 import numpy as np
 import pandas as pd
-from mne.decoding import CSP, LinearModel
+from mne.decoding import CSP
 from mne_bids import BIDSPath
 from sklearn.model_selection import StratifiedKFold, cross_val_score
 from sklearn.pipeline import make_pipeline
-from sklearn.linear_model import LogisticRegression
 
 from ..._config_utils import (
     _bids_kwargs,
@@ -257,7 +256,7 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
 
         # COEFS
         clf.fit(X, y)
-        weights_csp = mne.decoding.get_coef(clf, 'patterns_', inverse_transform=True)
+        weights_csp = mne.decoding.get_coef(clf, "patterns_", inverse_transform=True)
 
         # PATTERNS
         csp.fit_transform(X, y)
@@ -267,9 +266,8 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
         # XXX right now this saves in working directory
         csp_fname = cond1 + cond2 + str(fmin) + str(fmax)
 
-        np.save(csp_fname + '_patterns', sensor_pattern_csp)
-        np.save(csp_fname + '_weights', weights_csp)
-
+        np.save(csp_fname + "_patterns", sensor_pattern_csp)
+        np.save(csp_fname + "_weights", weights_csp)
 
     # Loop over times x frequencies
     #
@@ -344,7 +342,7 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
 
         # COEFS
         clf.fit(X, y)
-        weights_csp = mne.decoding.get_coef(clf, 'patterns_', inverse_transform=True)
+        weights_csp = mne.decoding.get_coef(clf, "patterns_", inverse_transform=True)
 
         # PATTERNS
         csp.fit_transform(X, y)
@@ -354,8 +352,8 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
         # XXX right now this saves in working directory
         csp_fname = cond1 + cond2 + str(fmin) + str(fmax) + str(tmin) + str(tmax)
 
-        np.save(csp_fname + '_patterns', sensor_pattern_csp)
-        np.save(csp_fname + '_weights', weights_csp)
+        np.save(csp_fname + "_patterns", sensor_pattern_csp)
+        np.save(csp_fname + "_weights", weights_csp)
 
     # Write each DataFrame to a different Excel worksheet.
     a_vs_b = f"{condition1}+{condition2}".replace(op.sep, "")