fixes

cosmit

mne/preprocessing/ecg.py

@@ -304,11 +304,10 @@ def create_ecg_epochs(raw, ch_name=None, event_id=999, picks=None,
             ecg[None],
             create_info(ch_names=['ECG-SYN'],
                         sfreq=raw.info['sfreq'], ch_types=['ecg']))
-        ignore = ['ch_names', 'chs', 'nchan']
+        ignore = ['ch_names', 'chs', 'nchan', 'bads']
         for k, v in raw.info.items():
             if k not in ignore:
                 ecg_raw.info[k] = v
-        raw.load_data()
         raw.add_channels([ecg_raw])
 
     if picks is None and not keep_ecg:

mne/viz/ica.py

@@ -337,8 +337,7 @@ def plot_ica_scores(ica, scores,
         If list, should match the outer shape of `scores`.
         If 'ecg' or 'eog', the labels_ attributes will be looked up.
         Note that '/' is used internally for sublabels specifying ECG and
-        EOG channels. Unless you want to lookup these labels don not use
-        '/'.
+        EOG channels.
     axhline : float
         Draw horizontal line to e.g. visualize rejection threshold.
     title : str
@@ -370,9 +369,9 @@ def plot_ica_scores(ica, scores,
     plt.suptitle(title)
 
     if labels == 'ecg':
-        labels = [l for l in ica.labels_ if l.startswith('ecg|')]
+        labels = [l for l in ica.labels_ if l.startswith('ecg/')]
     elif labels == 'eog':
-        labels = [l for l in ica.labels_ if l.startswith('eog|')]
+        labels = [l for l in ica.labels_ if l.startswith('eog/')]
         labels.sort(key=lambda l: l.split('/')[1])  # sort by index
     elif isinstance(labels, string_types):
         if len(axes) > 1:

tutorials/plot_ica_from_raw.py

@@ -57,7 +57,7 @@
 ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5, picks=picks)
 
 ecg_inds, scores = ica.find_bads_ecg(ecg_epochs, method='ctps')
-ica.plot_scores(scores, exclude=ecg_inds, title=title % 'ecg')
+ica.plot_scores(scores, exclude=ecg_inds, title=title % 'ecg', label='ecg')
 
 show_picks = np.abs(scores).argsort()[::-1][:5]
 
@@ -70,7 +70,7 @@
 # detect EOG by correlation
 
 eog_inds, scores = ica.find_bads_eog(raw)
-ica.plot_scores(scores, exclude=eog_inds, title=title % 'eog')
+ica.plot_scores(scores, exclude=eog_inds, title=title % 'eog', labels='eog')
 
 show_picks = np.abs(scores).argsort()[::-1][:5]