lint: resolve pre-commit errors, format

examples/preprocessing/esg_rm_heart_artefact_pcaobs.py

@@ -30,8 +30,8 @@
 
 ###############################################################################
 # Download sample subject data from OpenNeuro if you haven't already
-# This will download simultaneous EEG and ESG data from a single run of a single participant after
-# median nerve stimulation of the left wrist
+# This will download simultaneous EEG and ESG data from a single run of a
+# single participant after median nerve stimulation of the left wrist
 import openneuro
 from matplotlib import pyplot as plt
 
@@ -40,7 +40,7 @@
 from mne.io import read_raw_eeglab
 from mne.preprocessing import find_ecg_events, fix_stim_artifact
 
-# add the path where you want the OpenNeuro data downloaded. Each run is ~2GB of data to download.
+# add the path where you want the OpenNeuro data downloaded. Each run is ~2GB of data
 ds = "ds004388"
 target_dir = mne.datasets.default_path() / ds
 run_name = "sub-001/eeg/*median_run-03_eeg*.set"
@@ -101,9 +101,9 @@
 iv_baseline = [-400e-3, -300e-3]
 iv_epoch = [-400e-3, 600e-3]
 
-###############################################################################
-# Perform minimal preprocessing including removing the stimulation artefact, downsampling
-# and filtering
+######################################################
+# Perform minimal preprocessing including removing the
+# stimulation artefact, downsampling and filtering
 
 raw = read_raw_eeglab(block_files[0], verbose="error")
 raw.set_channel_types(dict(ECG="ecg"))
@@ -126,7 +126,7 @@
     stim_channel=None,
 )
 
-###############################################################################
+###################################################################
 # Find ECG events and add to the raw structure as event annotations
 
 ecg_events, ch_ecg, average_pulse = find_ecg_events(raw, ch_name="ECG")
@@ -144,9 +144,9 @@
     qrs_event_time, duration, description, ch_names=[esg_chans] * len(qrs_event_time)
 )
 
-###############################################################################
-# Create evoked response about the detected R-peaks before and after cardiac artefact
-# correction
+###################################################
+# Create evoked response about the detected R-peaks
+# before and after cardiac artefact correction
 
 events, event_ids = events_from_annotations(raw)
 event_id_dict = {key: value for key, value in event_ids.items() if key == "qrs"}
@@ -176,7 +176,7 @@
 )
 evoked_after = epochs.average()
 
-###############################################################################
+##################
 # Comparison image
 
 fig, axes = plt.subplots(1, 1, layout="constrained")

mne/preprocessing/pca_obs.py

@@ -27,7 +27,7 @@ def apply_pca_obs(
     n_components: int = 4,
     n_jobs: int | None = None,
     copy: bool = True,
-    verbose : bool | str | int | None = None
+    verbose: bool | str | int | None = None,
 ) -> Raw | None:
     """
     Apply the PCA-OBS algorithm to picks of a Raw object.
@@ -45,7 +45,7 @@ def apply_pca_obs(
     n_components : int
         Number of PCA components to use to form the OBS (default 4).
     copy : bool
-        If False, modify the Raw instance in-place. 
+        If False, modify the Raw instance in-place.
         If True, return a copied, modified Raw instance. Defaults to True.
     %(n_jobs)s
     %(verbose)s
@@ -80,6 +80,7 @@ def apply_pca_obs(
     if copy:
         return raw
 
+
 def _pca_obs(
     data: np.ndarray,
     qrs: np.ndarray,