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 make_report(subject, subjects_dir, meg_filename, output_dir):
#Create report from output
report = Report(image_format='png', subjects_dir=subjects_dir,
subject=subject,
raw_psd=False) # use False for speed here
#info_fname=meg_filename,
report.parse_folder(output_dir, on_error='ignore', mri_decim=10)
report_filename = op.join(output_dir, 'QA_report.html')
report.save(report_filename)
def generate_report(subj):
report = Report(subject=subj.name, title=subj.name)
if subj.name == "sub-emptyroom":
report.parse_folder(str(subj), render_bem=False)
else:
report.parse_folder(str(subj / "meg"), render_bem=False)
# process head positions
if subj.name != "sub-emptyroom":
report = add_head_postions(subj, report)
# add bad channels
report = add_bad_channels(subj, report)
# add maxwell filtering part
report = add_maxwell_filtering_figures(subj, report)
# -------- save results -------- #
dest_dir = dirs.reports / subj.name
dest_dir.mkdir(exist_ok=True)
savename = dest_dir / (subj.name + "-report.html")
report.save(str(savename), open_browser=False, overwrite=True)
# Objects Hits vs. Misses (6)
conds = ['object-hit6', 'object-miss6']
these_evokeds = [
evokeds[evokeds_key[x]] for x in evokeds_key.keys() if x in conds]
fig1 = plot_compare_evokeds(
these_evokeds, title='Objects: Hits vs. Misses (6 as old)',
axes='topo', show=False, show_sensors=True)
# Plot 100ms time window topomaps of difference wave
# (min-max scaled) (centered on 0,.5, etc.)
times = np.arange(.05, evokeds[0].tmax, .1)
evoked = evokeds[evokeds_key['object-hit-miss6']]
fig2 = evoked.plot_topomap(times=times, show=False,
average=.1, nrows=4, ncols=5)
# Make the slider
figs = [fig1[0], fig2]
captions = [
'Objects: Hits vs. Misses (6 as old)',
'Objects: Hits - Misses (6 as old) Topos (100ms bins)'
]
report.add_slider_to_section(
figs, section='ERP', captions=captions,
title='ERP (Mastoid ref.): Object Hits - Misses (6 as old)', scale=1.5)
plt.close()
# Save report
report_file = report_dir / f'{sub_string}_task-{task}_report.html'
report.save(report_file, overwrite=True, open_browser=False)
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)
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'),
open_browser=False, overwrite=True)
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)
# 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()
rep.add_figs_to_section(fig, 'Average faces - scrambled')
rep.save(fname=op.join(meg_dir, 'report_average.html'),
open_browser=False,
overwrite=True)
