def generate_report(raw, ica, report_savepath):
logger.info("Generatingg report")
report = Report(verbose=False)
fig_topo = ica.plot_components(picks=range(ica.n_components_), show=False)
report.add_figs_to_section(fig_topo, section="ICA", captions="Timeseries")
report.save(report_savepath, overwrite=True, open_browser=False)
def detect_bad_components(*, cfg, which: Literal['eog', 'ecg'],
epochs: mne.BaseEpochs, ica: mne.preprocessing.ICA,
subject: str, session: str,
report: mne.Report) -> List[int]:
evoked = epochs.average()
artifact = which.upper()
msg = f'Performing automated {artifact} artifact detection …'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
if which == 'eog':
inds, scores = ica.find_bads_eog(epochs,
threshold=cfg.ica_eog_threshold)
else:
inds, scores = ica.find_bads_ecg(epochs,
method='ctps',
threshold=cfg.ica_ctps_ecg_threshold)
if not inds:
adjust_setting = ('ica_eog_threshold'
if which == 'eog' else 'ica_ctps_ecg_threshold')
warn = (f'No {artifact}-related ICs detected, this is highly '
f'suspicious. A manual check is suggested. You may wish to '
f'lower "{adjust_setting}".')
logger.warning(
**gen_log_kwargs(message=warn, subject=subject, session=session))
else:
msg = (f'Detected {len(inds)} {artifact}-related ICs in '
f'{len(epochs)} {artifact} epochs.')
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
# Mark the artifact-related components for removal
ica.exclude = inds
# Plot scores
fig = ica.plot_scores(scores, labels=which, show=cfg.interactive)
report.add_figs_to_section(figs=fig,
captions=f'Scores - {artifact}',
section=f'sub-{subject}')
# Plot source time course
fig = ica.plot_sources(evoked, show=cfg.interactive)
report.add_figs_to_section(figs=fig,
captions=f'Source time course - {artifact}',
section=f'sub-{subject}')
# Plot original & corrected data
fig = ica.plot_overlay(evoked, show=cfg.interactive)
report.add_figs_to_section(figs=fig,
captions=f'Corrections - {artifact}',
section=f'sub-{subject}')
return inds
raw = mne.io.read_raw_fif(raw_fif_file, preload=True)
# Load the Epochs
epoch_fif_file = deriv_path / \
f'{sub_string}_task-{task}_ref-mastoids_desc-cleaned_epo.fif.gz'
epochs = mne.read_epochs(epoch_fif_file, preload=True)
# Load Epochs json
json_file = deriv_path / \
f'{sub_string}_task-{task}_ref-mastoids_desc-cleaned_epo.json'
with open(json_file, 'r') as f:
json_info = json.load(f)
# Plot Sensors
fig = bv_montage.plot(show=False)
report.add_figs_to_section(fig, captions='EEG: Montage Layout',
section='EEG')
# Get events
events_file = deriv_path / f'{sub_string}_task-{task}_desc-resamp_eve.txt'
events = mne.read_events(events_file)
fig = mne.viz.plot_events(events, sfreq=raw.info['sfreq'],
event_id=event_id, show=False)
report.add_figs_to_section(fig, captions='EEG: Events', section='EEG',
scale=1.5)
# Add PSD plot from raw and epochsepochs
fig = plt.figure()
fig.set_size_inches(12, 7)
ax1 = plt.subplot(221)
raw.plot_psd(picks=['eeg'], xscale='linear', ax=ax1, show=False)
ax1.set_title('Raw: Linear Scale')
def make_report(subject_id):
subject = "sub%03d" % subject_id
print("processing %s" % subject)
meg_path = op.join(meg_dir, subject)
ave_fname = op.join(meg_path, "%s-ave.fif" % subject)
rep = Report(info_fname=ave_fname, subject=subject,
subjects_dir=subjects_dir)
rep.parse_folder(meg_path)
evokeds = mne.read_evokeds(op.join(meg_path, '%s-ave.fif' % subject))
fam = evokeds[0]
scramb = evokeds[1]
unfam = evokeds[2]
figs = list()
captions = list()
fig = fam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Famous faces')
fig = unfam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Unfamiliar faces')
fig = scramb.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Scrambled faces')
if 'EEG070' in fam.ch_names:
idx = fam.ch_names.index('EEG070')
fig = mne.viz.plot_compare_evokeds({'Famous': fam, 'Unfamiliar': unfam,
'Scrambled': scramb}, idx,
show=False)
figs.append(fig)
captions.append('Famous, unfamliliar and scrambled faces on EEG070')
fname_trans = op.join(study_path, 'ds117', subject, 'MEG',
'%s-trans.fif' % subject)
mne.viz.plot_trans(fam.info, fname_trans, subject=subject,
subjects_dir=subjects_dir, meg_sensors=True,
eeg_sensors=True)
fig = mlab.gcf()
figs.append(fig)
captions.append('Coregistration')
rep.add_figs_to_section(figs, captions)
for cond in ['faces', 'famous', 'unfamiliar', 'scrambled', 'contrast']:
fname = op.join(meg_path, 'mne_dSPM_inverse-%s' % cond)
stc = mne.read_source_estimate(fname, subject)
brain = stc.plot(views=['ven'], hemi='both')
brain.set_data_time_index(112)
fig = mlab.gcf()
rep._add_figs_to_section(fig, cond)
rep.save(fname=op.join(meg_dir, 'report%s.html' % subject),
open_browser=False, overwrite=True)
brain.set_data_time_index(112)
fig = mlab.gcf()
rep._add_figs_to_section(fig, cond)
rep.save(fname=op.join(meg_dir, 'report%s.html' % subject),
open_browser=False, overwrite=True)
# Group report
faces_fname = op.join(meg_dir, 'eeg_faces-ave.fif')
rep = Report(info_fname=faces_fname, subject='fsaverage',
subjects_dir=subjects_dir)
faces = mne.read_evokeds(faces_fname)[0]
rep.add_figs_to_section(faces.plot(spatial_colors=True, gfp=True, show=False),
'Average faces')
scrambled = mne.read_evokeds(op.join(meg_dir, 'eeg_scrambled-ave.fif'))[0]
rep.add_figs_to_section(scrambled.plot(spatial_colors=True, gfp=True,
show=False), 'Average scrambled')
fname = op.join(meg_dir, 'contrast-average')
stc = mne.read_source_estimate(fname, subject='fsaverage')
brain = stc.plot(views=['ven'], hemi='both', subject='fsaverage',
subjects_dir=subjects_dir)
brain.set_data_time_index(165)
fig = mlab.gcf()
rep.add_figs_to_section(fig, 'Average faces - scrambled')
rep.save(fname=op.join(meg_dir, 'report_average.html'),
def make_report(subject_id):
subject = "sub%03d" % subject_id
print("processing %s" % subject)
meg_path = op.join(meg_dir, subject)
ave_fname = op.join(meg_path,
"%s_highpass-%sHz-ave.fif" % (subject, l_freq))
rep = Report(info_fname=ave_fname,
subject=subject,
subjects_dir=subjects_dir)
rep.parse_folder(meg_path)
evokeds = mne.read_evokeds(ave_fname)
fam = evokeds[0]
scramb = evokeds[1]
unfam = evokeds[2]
figs = list()
captions = list()
fig = fam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Famous faces')
fig = unfam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Unfamiliar faces')
fig = scramb.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Scrambled faces')
if 'EEG070' in fam.ch_names:
idx = fam.ch_names.index('EEG070')
fig = mne.viz.plot_compare_evokeds(
{
'Famous': fam,
'Unfamiliar': unfam,
'Scrambled': scramb
},
idx,
show=False)
figs.append(fig)
captions.append('Famous, unfamliliar and scrambled faces on EEG070')
fname_trans = op.join(study_path, 'ds117', subject, 'MEG',
'%s-trans.fif' % subject)
mne.viz.plot_trans(fam.info,
fname_trans,
subject=subject,
subjects_dir=subjects_dir,
meg_sensors=True,
eeg_sensors=True)
fig = mlab.gcf()
figs.append(fig)
captions.append('Coregistration')
rep.add_figs_to_section(figs, captions)
for cond in ['faces', 'famous', 'unfamiliar', 'scrambled', 'contrast']:
fname = op.join(meg_path,
'mne_dSPM_inverse_highpass-%sHz-%s' % (l_freq, cond))
stc = mne.read_source_estimate(fname, subject)
brain = stc.plot(views=['ven'], hemi='both')
brain.set_data_time_index(112)
fig = mlab.gcf()
rep._add_figs_to_section(fig, cond)
rep.save(fname=op.join(meg_dir, 'report%s.html' % subject),
open_browser=False,
overwrite=True)
fig = mlab.gcf()
rep._add_figs_to_section(fig, cond)
rep.save(fname=op.join(meg_dir, 'report%s.html' % subject),
open_browser=False,
overwrite=True)
# Group report
faces_fname = op.join(meg_dir, 'eeg_faces_highpass-%sHz-ave.fif' % l_freq)
rep = Report(info_fname=faces_fname,
subject='fsaverage',
subjects_dir=subjects_dir)
faces = mne.read_evokeds(faces_fname)[0]
rep.add_figs_to_section(faces.plot(spatial_colors=True, gfp=True, show=False),
'Average faces')
scrambled = mne.read_evokeds(op.join(meg_dir, 'eeg_scrambled-ave.fif'))[0]
rep.add_figs_to_section(
scrambled.plot(spatial_colors=True, gfp=True, show=False),
'Average scrambled')
fname = op.join(meg_dir, 'contrast-average_highpass-%sHz' % l_freq)
stc = mne.read_source_estimate(fname, subject='fsaverage')
brain = stc.plot(views=['ven'],
hemi='both',
subject='fsaverage',
subjects_dir=subjects_dir)
brain.set_data_time_index(165)
fig = mlab.gcf()