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 get_report(subject):
"""Get a Report object for a subject.
If the Report had been saved (pickle'd) before, load it. Otherwise,
construct a new one.
"""
report_fname = fname.report(subject=subject)
if os.path.exists(report_fname):
with open(report_fname, 'rb') as f:
return pickle.load(f)
else:
return Report(subjects_dir=fname.subjects_dir,
subject=subject,
title='Analysis for %s' % subject)
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)
matplotlib.rcParams.update({'figure.max_open_warning': 0})
# Ask for subject IDs to analyze
sub_list = get_sub_list(deriv_dir, allow_all=True)
for sub_string in sub_list:
# SUBJECT INFORMATION DEFINITION
# Define the Subject ID and paths
deriv_path = deriv_dir / sub_string
fig_path = deriv_path / 'figures'
print(f'Generating report for task-{task} data from {sub_string}')
# Initialize the report
# Make the report object
report = Report(subject=sub_string,
title=f'{sub_string}: task-{task} report',
image_format='png', verbose=True, projs=False,
subjects_dir=None)
# Behavioral Section
# Plot behavioral data
behav_fig_file = fig_path / f'{sub_string}_task-{task}_beh_performance.png'
report.add_images_to_section(behav_fig_file,
captions='Behavior: Performance Summary',
section='Behavior')
# EEG Section
# Load the Raw data
raw_fif_file = deriv_path / \
f'{sub_string}_task-{task}_ref-FCz_desc-resamp_raw.fif.gz'
raw = mne.io.read_raw_fif(raw_fif_file, preload=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)
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',
