[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

mne_icalabel/megnet/features.py

@@ -1,17 +1,16 @@
 import io
 
 import matplotlib.pyplot as plt
-import mne # type: ignore
+import mne  # type: ignore
 import numpy as np
-from mne.io import BaseRaw # type: ignore
-from mne.preprocessing import ICA # type: ignore
-from mne.utils import warn # type: ignore
+from _utils import cart2sph, pol2cart
+from mne.io import BaseRaw  # type: ignore
+from mne.preprocessing import ICA  # type: ignore
+from mne.utils import warn  # type: ignore
 from numpy.typing import NDArray
 from PIL import Image
-from scipy import interpolate # type: ignore
-from scipy.spatial import ConvexHull # type: ignore
-
-from _utils import cart2sph, pol2cart
+from scipy import interpolate  # type: ignore
+from scipy.spatial import ConvexHull  # type: ignore
 
 
 def get_megnet_features(raw: BaseRaw, ica: ICA):

mne_icalabel/megnet/label_componets.py

@@ -67,7 +67,8 @@ def _chunk_predicting(
     overlap_len=3750,
 ) -> NDArray:
     """Predict the labels for each component using
-    MEGnet's chunk volte algorithm."""
+    MEGnet's chunk volte algorithm.
+    """
     predction_vote = []
 
     for comp_series, comp_map in zip(time_series, spatial_maps):
@@ -79,9 +80,7 @@ def _chunk_predicting(
 
         chunk_votes = {start: 0 for start in start_times}
         for t in range(time_len):
-            in_chunks = (
-                [start <= t < start + chunk_len for start in start_times]
-            )
+            in_chunks = [start <= t < start + chunk_len for start in start_times]
             # how many chunks the time point is in
             num_chunks = np.sum(in_chunks)
             for start_time, is_in_chunk in zip(start_times, in_chunks):
@@ -91,22 +90,18 @@ def _chunk_predicting(
         weighted_predictions = {}
         for start_time in chunk_votes.keys():
             onnx_inputs = {
-                session.get_inputs()[0].name:
-                    np.expand_dims(comp_map, 0).astype(np.float32),
-                session.get_inputs()[1].name:
-                    np.expand_dims(
-                    np.expand_dims(
-                        comp_series[start_time: start_time + chunk_len], 0
-                        ), -1).astype(np.float32),
+                session.get_inputs()[0].name: np.expand_dims(comp_map, 0).astype(
+                    np.float32
+                ),
+                session.get_inputs()[1].name: np.expand_dims(
+                    np.expand_dims(comp_series[start_time : start_time + chunk_len], 0),
+                    -1,
+                ).astype(np.float32),
             }
             prediction = session.run(None, onnx_inputs)[0]
-            weighted_predictions[start_time] = (
-                prediction * chunk_votes[start_time]
-            )
+            weighted_predictions[start_time] = prediction * chunk_votes[start_time]
 
-        comp_prediction = np.stack(
-            list(weighted_predictions.values())
-            ).mean(axis=0)
+        comp_prediction = np.stack(list(weighted_predictions.values())).mean(axis=0)
         comp_prediction /= comp_prediction.sum()
         predction_vote.append(comp_prediction)
 
@@ -129,21 +124,17 @@ def _get_chunk_start(
 
 if __name__ == "__main__":
     import mne
-
     from features import get_megnet_features
 
     sample_dir = mne.datasets.sample.data_path()
     sample_fname = sample_dir / "MEG" / "sample" / "sample_audvis_raw.fif"
     raw = mne.io.read_raw_fif(sample_fname).pick("mag")
     raw.resample(250)
     raw.filter(1, 100)
-    ica = mne.preprocessing.ICA(
-        n_components=20, max_iter="auto", method="infomax"
-        )
+    ica = mne.preprocessing.ICA(n_components=20, max_iter="auto", method="infomax")
     ica.fit(raw)
 
     res = megnet_label_components(raw, ica)
     print(res)
     ica.exclude = [i for i, label in enumerate(res["labels"]) if label != "brain/other"]
     ica.plot_components()
-