replace pick_types with .pick

pyprep/find_noisy_channels.py

@@ -60,8 +60,7 @@ def __init__(self, raw, do_detrend=True, random_state=None, matlab_strict=False)
         raw.load_data()
         self.raw_mne = raw.copy()
         self.bad_by_manual = raw.info["bads"]
-        # Do not work on channels that were manually marked as bad
-        self.raw_mne.pick_types(eeg=True)
+        self.raw_mne.pick("eeg")  # excludes bads
         self.sample_rate = raw.info["sfreq"]
         if do_detrend:
             self.raw_mne._data = removeTrend(

pyprep/ransac.py

@@ -3,7 +3,6 @@
 # Authors: The PyPREP developers
 # SPDX-License-Identifier: MIT
 
-import mne
 import numpy as np
 from mne.channels.interpolation import _make_interpolation_matrix
 from mne.utils import ProgressBar, check_random_state, logger
@@ -138,7 +137,9 @@ def find_bad_by_ransac(
     # Get all channel positions and the position subset of "clean channels"
     # Exclude should be the bad channels from other methods
     # That is, identify all bad channels by other means
-    good_idx = np.array([idx for idx, ch in enumerate(complete_chn_labs) if ch not in exclude])
+    good_idx = np.array(
+        [idx for idx, ch in enumerate(complete_chn_labs) if ch not in exclude]
+    )
     n_chans_good = good_idx.shape[0]
     chn_pos_good = chn_pos[good_idx, :]
 

pyprep/reference.py

@@ -83,7 +83,8 @@ def __init__(
         raw.load_data()
         self.raw = raw.copy()
         self.ch_names = self.raw.ch_names
-        self.raw.pick_types(eeg=True, eog=False, meg=False, exclude=[])
+        self.raw.pick("eeg", exclude=[])  # include previously marked bads
+        self.bads_manual = raw.info["bads"]
         self.ch_names_eeg = self.raw.ch_names
         self.EEG = self.raw.get_data()
         self.reference_channels = params["ref_chs"]
@@ -97,7 +98,6 @@ def __init__(
         self.random_state = check_random_state(random_state)
         self._extra_info = {}
         self.matlab_strict = matlab_strict
-        self.bads_manual = raw.info["bads"]
 
     def perform_reference(self, max_iterations=4):
         """Estimate the true signal mean and interpolate bad channels.

pyprep/utils.py

@@ -6,7 +6,6 @@
 import math
 from cmath import sqrt
 
-import mne
 import numpy as np
 import scipy.interpolate
 from mne.surface import _normalize_vectors
@@ -361,8 +360,16 @@ def _eeglab_interpolate_bads(raw):
 
     """
     # Get the indices of good and bad EEG channels
-    eeg_chans = mne.pick_types(raw.info, eeg=True, exclude=[])
-    good_idx = mne.pick_types(raw.info, eeg=True, exclude="bads")
+    eeg_chans = np.array(
+        [idx for idx, typ in enumerate(raw.get_channel_types()) if typ == "eeg"]
+    )
+    good_idx = np.array(
+        [
+            idx
+            for idx, (ch, typ) in enumerate(zip(raw.ch_names, raw.get_channel_types()))
+            if (typ == "eeg") and (ch not in raw.info["bads"])
+        ]
+    )
     bad_idx = sorted(_set_diff(eeg_chans, good_idx))
 
     # Get the spatial coordinates of the good and bad electrodes

tests/test_prep_pipeline.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: MIT
 
 import matplotlib.pyplot as plt
-import mne
 import numpy as np
 import pytest
 import scipy.io as sio
@@ -17,7 +16,9 @@
 @pytest.mark.usefixtures("raw", "montage")
 def test_prep_pipeline(raw, montage):
     """Test prep pipeline."""
-    eeg_index = mne.pick_types(raw.info, eeg=True, eog=False, meg=False)
+    eeg_index = np.array(
+        [idx for idx, typ in enumerate(raw.get_channel_types()) if typ == "eeg"]
+    )
     raw_copy = raw.copy()
     ch_names = raw_copy.info["ch_names"]
     ch_names_eeg = list(np.asarray(ch_names)[eeg_index])
@@ -258,7 +259,9 @@ def test_prep_pipeline_non_eeg(raw, montage):
 @pytest.mark.usefixtures("raw", "montage")
 def test_prep_pipeline_filter_kwargs(raw, montage):
     """Test prep pipeline with filter kwargs."""
-    eeg_index = mne.pick_types(raw.info, eeg=True, eog=False, meg=False)
+    eeg_index = np.array(
+        [idx for idx, typ in enumerate(raw.get_channel_types()) if typ == "eeg"]
+    )
     raw_copy = raw.copy()
     ch_names = raw_copy.info["ch_names"]
     ch_names_eeg = list(np.asarray(ch_names)[eeg_index])