def add_event_counts(*, cfg, session: str, report: mne.Report) -> None:
try:
df_events = count_events(BIDSPath(root=cfg.bids_root, session=session))
except ValueError:
logger.warning('Could not read events.')
df_events = None
if df_events is not None:
css_classes = ('table', 'table-striped', 'table-borderless',
'table-hover')
report.add_htmls_to_section(
f'<div class="event-counts">\n'
f'{df_events.to_html(classes=css_classes, border=0)}\n'
f'</div>',
captions='Event counts',
section='events')
css = ('.event-counts {\n'
' display: -webkit-box;\n'
' display: -ms-flexbox;\n'
' display: -webkit-flex;\n'
' display: flex;\n'
' justify-content: center;\n'
' text-align: center;\n'
'}\n\n'
'th, td {\n'
' text-align: center;\n'
'}\n')
report.add_custom_css(css)
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 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 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 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
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-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)
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)
# 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()
# Ask for subject IDs to analyze
sub_list = get_sub_list(deriv_dir, allow_all=True)
for sub in sub_list:
# SUBJECT INFORMATION DEFINITION
# Define the Subject ID and paths
deriv_path = deriv_dir / sub
fig_path = deriv_path / 'figures'
print(f'Generating report for task-{task} data from {sub}')
# Initialize the report
# Make the report object
report = Report(subject=sub,
title=f'{sub}: 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}_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}_task-{task}_ref-FCz_desc-resamp_raw.fif.gz'
raw = mne.io.read_raw_fif(raw_fif_file, preload=True)
def run_report_source(*,
cfg: SimpleNamespace,
subject: str,
session: Optional[str] = None,
report: mne.Report) -> mne.Report:
import matplotlib.pyplot as plt
msg = 'Generating source-space analysis report …'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
if report is None:
report = _gen_empty_report(cfg=cfg, subject=subject, session=session)
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
# Use this as a source for the Info dictionary
fname_info = bids_path.copy().update(processing='clean', suffix='epo')
fname_trans = bids_path.copy().update(suffix='trans')
if not fname_trans.fpath.exists():
msg = 'No coregistration found, skipping source space report.'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
return report
###########################################################################
#
# Visualize the coregistration & inverse solutions.
#
if cfg.conditions is None:
conditions = []
elif isinstance(cfg.conditions, dict):
conditions = list(cfg.conditions.keys())
else:
conditions = cfg.conditions.copy()
conditions.extend(cfg.contrasts)
msg = 'Rendering MRI slices with BEM contours.'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
report.add_bem(subject=cfg.fs_subject,
subjects_dir=cfg.fs_subjects_dir,
title='BEM',
width=256,
decim=8)
msg = 'Rendering sensor alignment (coregistration).'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
report.add_trans(
trans=fname_trans,
info=fname_info,
title='Sensor alignment',
subject=cfg.fs_subject,
subjects_dir=cfg.fs_subjects_dir,
)
for condition in conditions:
msg = f'Rendering inverse solution for {condition}'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
if condition in cfg.conditions:
title = f'Source: {config.sanitize_cond_name(condition)}'
else: # It's a contrast of two conditions.
# XXX Will change once we process contrasts here too
continue
method = cfg.inverse_method
cond_str = config.sanitize_cond_name(condition)
inverse_str = method
hemi_str = 'hemi' # MNE will auto-append '-lh' and '-rh'.
fname_stc = bids_path.copy().update(
suffix=f'{cond_str}+{inverse_str}+{hemi_str}', extension=None)
tags = ('source-estimate', condition.lower().replace(' ', '-'))
if Path(f'{fname_stc.fpath}-lh.stc').exists():
report.add_stc(stc=fname_stc,
title=title,
subject=cfg.fs_subject,
subjects_dir=cfg.fs_subjects_dir,
tags=tags)
plt.close('all') # close all figures to save memory
return report
def run_report_sensor(*,
cfg: SimpleNamespace,
subject: str,
session: Optional[str] = None,
report: mne.Report) -> mne.Report:
import matplotlib.pyplot as plt
msg = 'Generating sensor-space analysis report …'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
if report is None:
report = _gen_empty_report(cfg=cfg, subject=subject, session=session)
bids_path = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
recording=cfg.rec,
space=cfg.space,
extension='.fif',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
fname_epo_clean = bids_path.copy().update(processing='clean', suffix='epo')
fname_ave = bids_path.copy().update(suffix='ave')
fname_decoding = bids_path.copy().update(processing=None,
suffix='decoding',
extension='.mat')
fname_tfr_pow = bids_path.copy().update(suffix='power+condition+tfr',
extension='.h5')
###########################################################################
#
# Visualize evoked responses.
#
if cfg.conditions is None:
conditions = []
elif isinstance(cfg.conditions, dict):
conditions = list(cfg.conditions.keys())
else:
conditions = cfg.conditions.copy()
conditions.extend(cfg.contrasts)
if conditions:
evokeds = mne.read_evokeds(fname_ave)
else:
evokeds = []
if evokeds:
msg = (f'Adding {len(conditions)} evoked signals and contrasts to the '
f'report.')
else:
msg = 'No evoked conditions or contrasts found.'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
for condition, evoked in zip(conditions, evokeds):
if cfg.analyze_channels:
evoked.pick(cfg.analyze_channels)
if condition in cfg.conditions:
title = f'Condition: {condition}'
tags = ('evoked', condition.lower().replace(' ', '-'))
else: # It's a contrast of two conditions.
title = f'Contrast: {condition[0]} – {condition[1]}'
tags = ('evoked', 'contrast',
f"{condition[0].lower().replace(' ', '-')}-"
f"{condition[1].lower().replace(' ', '-')}")
report.add_evokeds(evokeds=evoked, titles=title, tags=tags)
###########################################################################
#
# Visualize decoding results.
#
if cfg.decode:
msg = 'Adding time-by-time decoding results to the report.'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
epochs = mne.read_epochs(fname_epo_clean)
for contrast in cfg.contrasts:
cond_1, cond_2 = contrast
a_vs_b = f'{cond_1}+{cond_2}'.replace(op.sep, '')
processing = f'{a_vs_b}+{cfg.decoding_metric}'
processing = processing.replace('_', '-').replace('-', '')
fname_decoding_ = (fname_decoding.copy().update(
processing=processing))
decoding_data = loadmat(fname_decoding_)
del fname_decoding_, processing, a_vs_b
fig = plot_decoding_scores(
times=epochs.times,
cross_val_scores=decoding_data['scores'],
metric=cfg.decoding_metric)
title = f'Time-by-time Decoding: {cond_1} ./. {cond_2}'
caption = (f'{len(epochs[cond_1])} × {cond_1} ./. '
f'{len(epochs[cond_2])} × {cond_2}')
tags = ('epochs', 'contrast',
f"{contrast[0].lower().replace(' ', '-')}-"
f"{contrast[1].lower().replace(' ', '-')}")
report.add_figure(fig=fig, title=title, caption=caption, tags=tags)
plt.close(fig)
del decoding_data, cond_1, cond_2, title, caption
del epochs
###########################################################################
#
# Visualize TFR as topography.
#
if cfg.time_frequency_conditions is None:
conditions = []
elif isinstance(cfg.time_frequency_conditions, dict):
conditions = list(cfg.time_frequency_conditions.keys())
else:
conditions = cfg.time_frequency_conditions.copy()
if conditions:
msg = 'Adding TFR analysis results to the report.'
logger.info(
**gen_log_kwargs(message=msg, subject=subject, session=session))
for condition in conditions:
cond = config.sanitize_cond_name(condition)
fname_tfr_pow_cond = str(fname_tfr_pow.copy()).replace(
"+condition+", f"+{cond}+")
power = mne.time_frequency.read_tfrs(fname_tfr_pow_cond)
fig = power[0].plot_topo(show=False,
fig_facecolor='w',
font_color='k',
border='k')
report.add_figure(fig=fig,
title=f'TFR: {condition}',
caption=f'TFR Power: {condition}',
tags=('time-frequency',
condition.lower().replace(' ', '-')))
plt.close(fig)
return report
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',
# Ask for subject IDs to analyze
sub_list = get_sub_list(deriv_dir, allow_all=True)
for sub in sub_list:
# SUBJECT INFORMATION DEFINITION
# Define the Subject ID and paths
deriv_path = deriv_dir / sub
fig_path = deriv_path / 'figures'
print(f'Generating report for task-{task} data from {sub}')
# Initialize the report
# Make the report object
report = Report(subject=sub,
title=f'{sub}: 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}_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}_task-{task}_ref-FCz_desc-resamp_raw.fif.gz'
raw = mne.io.read_raw_fif(raw_fif_file, preload=True)
