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

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examples/preprocessing/interpolate_to.py

@@ -18,10 +18,11 @@
 # Authors: Antoine Collas <[email protected]>
 # License: BSD-3-Clause
 
+import matplotlib.pyplot as plt
+
 import mne
-from mne.datasets import sample
 from mne.channels import make_standard_montage
-import matplotlib.pyplot as plt
+from mne.datasets import sample
 
 print(__doc__)
 
@@ -39,7 +40,7 @@
 
 # %%
 # Define the target montage
-standard_montage = make_standard_montage('standard_1020')
+standard_montage = make_standard_montage("standard_1020")
 
 # %%
 # Use interpolate_to to project EEG data to the standard montage

mne/channels/channels.py

@@ -950,7 +950,7 @@ def interpolate_bads(
 
         return self
 
-    def interpolate_to(self, montage, method='MNE', reg=0.0):
+    def interpolate_to(self, montage, method="MNE", reg=0.0):
         """Interpolate data onto a new montage.
 
         Parameters
@@ -968,6 +968,7 @@ def interpolate_to(self, montage, method='MNE', reg=0.0):
             The instance with updated channel locations and data.
         """
         import numpy as np
+
         import mne
         from mne import pick_types
         from mne.forward._field_interpolation import _map_meg_or_eeg_channels
@@ -980,58 +981,66 @@ def interpolate_to(self, montage, method='MNE', reg=0.0):
         if len(picks_from) == 0:
             raise ValueError("No EEG channels available for interpolation.")
 
-        if hasattr(self, '_data'):
+        if hasattr(self, "_data"):
             data_orig = self._data[picks_from]
         else:
             # If epochs-like data, for simplicity take the mean across epochs
             data_orig = self.get_data()[:, picks_from, :].mean(axis=0)
 
         # Get target positions from the montage
-        ch_pos = montage.get_positions()['ch_pos']
+        ch_pos = montage.get_positions()["ch_pos"]
         target_ch_names = list(ch_pos.keys())
         if len(target_ch_names) == 0:
-            raise ValueError("The provided montage does not contain any channel positions.")
+            raise ValueError(
+                "The provided montage does not contain any channel positions."
+            )
 
         # Create a new info structure using MNE public API
-        sfreq = self.info['sfreq']
-        ch_types = ['eeg'] * len(target_ch_names)
-        new_info = mne.create_info(ch_names=target_ch_names, sfreq=sfreq, ch_types=ch_types)
+        sfreq = self.info["sfreq"]
+        ch_types = ["eeg"] * len(target_ch_names)
+        new_info = mne.create_info(
+            ch_names=target_ch_names, sfreq=sfreq, ch_types=ch_types
+        )
         new_info.set_montage(montage)
 
         # Create a simple old_info
-        sfreq = self.info['sfreq']
-        ch_names = self.info['ch_names']
-        ch_types = ['eeg'] * len(ch_names)
+        sfreq = self.info["sfreq"]
+        ch_names = self.info["ch_names"]
+        ch_types = ["eeg"] * len(ch_names)
         old_info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
         old_info.set_montage(self.info.get_montage())
 
         # Compute mapping from current montage to target montage
-        mapping = _map_meg_or_eeg_channels(old_info, new_info, mode='accurate', origin='auto')
+        mapping = _map_meg_or_eeg_channels(
+            old_info, new_info, mode="accurate", origin="auto"
+        )
 
         # Apply the interpolation mapping
         D_new = mapping.dot(data_orig)
 
         # Update bad channels
-        new_bads = [ch for ch in self.info['bads'] if ch in target_ch_names]
-        new_info['bads'] = new_bads
+        new_bads = [ch for ch in self.info["bads"] if ch in target_ch_names]
+        new_info["bads"] = new_bads
 
         # Update the instance's info and data
         self.info = new_info
-        if hasattr(self, '_data'):
+        if hasattr(self, "_data"):
             if self._data.ndim == 2:
                 # Raw-like: directly assign the new data
                 self._data = D_new
             else:
                 # Epochs-like
                 n_epochs, _, n_times = self._data.shape
-                new_data = np.zeros((n_epochs, len(target_ch_names), n_times), dtype=self._data.dtype)
+                new_data = np.zeros(
+                    (n_epochs, len(target_ch_names), n_times), dtype=self._data.dtype
+                )
                 for e in range(n_epochs):
                     epoch_data_orig = self._data[e, picks_from, :]
                     new_data[e, :, :] = mapping.dot(epoch_data_orig)
                 self._data = new_data
         else:
             # Evoked-like data
-            if hasattr(self, 'data'):
+            if hasattr(self, "data"):
                 self.data = D_new
             else:
                 raise NotImplementedError("This method requires preloaded data.")