def test_add_htmls_to_section():
"""Test adding html str to mne report.
"""
report = Report(info_fname=raw_fname, subject="sample", subjects_dir=subjects_dir)
html = "<b>MNE-Python is AWESOME</b>"
caption, section = "html", "html_section"
report.add_htmls_to_section(html, caption, section)
idx = report._sectionlabels.index("report_" + section)
html_compare = report.html[idx]
assert_true(html in html_compare)
def test_add_htmls_to_section():
"""Test adding html str to mne report."""
report = Report(info_fname=raw_fname,
subject='sample', subjects_dir=subjects_dir)
html = '<b>MNE-Python is AWESOME</b>'
caption, section = 'html', 'html_section'
report.add_htmls_to_section(html, caption, section)
idx = report._sectionlabels.index('report_' + section)
html_compare = report.html[idx]
assert_true(html in html_compare)
assert_true(repr(report))
epochs_ar, reject_log = ar.fit_transform(this_epoch, return_log=True)
reject_log.plot_epochs(this_epoch, scalings='auto')
epochs_ar.plot(scalings='auto')
epochs_ar.plot_drop_log() #dropp epochs
#---------------------Report-------------------------------#
###################################################################
name = 'Subject1_report'
report = Report(image_format='png', raw_psd=False, title=name, subject=name)
#generate ICA
fig = ica.plot_components(inst=raw)
report.add_htmls_to_section(
"Filter parameters: 0.1-50 Hz, fir_design='firwin'; ICA parameters: n_components=40, random_state=97, max_iter=800, method = 'fastica'",
captions="Description")
report.add_slider_to_section(fig,
title="ICA",
section="ICA_components",
image_format='png')
# generate images with properties
n = list(range(0, 40))
fig_2 = ica.plot_properties(raw, picks=n, psd_args={'fmax': 50})
report.add_slider_to_section(fig_2,
title="ICA",
section="ICA_components_properties",
image_format='png')
report.save('/Users/n/PycharmProjects/application/' + name + '.html',
def compute_ica(raw, subject, n_components=0.99, picks=None, decim=None,
reject=None, ecg_tmin=-0.5, ecg_tmax=0.5, eog_tmin=-0.5,
eog_tmax=0.5, n_max_ecg=3, n_max_eog=1,
n_max_ecg_epochs=200, show=True, img_scale=1.0,
random_state=None, report=None, artifact_stats=None):
"""Run ICA in raw data
Parameters
----------,
raw : instance of Raw
Raw measurements to be decomposed.
subject : str
The name of the subject.
picks : array-like of int, shape(n_channels, ) | None
Channels to be included. This selection remains throughout the
initialized ICA solution. If None only good data channels are used.
Defaults to None.
n_components : int | float | None | 'rank'
The number of components used for ICA decomposition. If int, it must be
smaller then max_pca_components. If None, all PCA components will be
used. If float between 0 and 1 components can will be selected by the
cumulative percentage of explained variance.
If 'rank', the number of components equals the rank estimate.
Defaults to 0.99.
decim : int | None
Increment for selecting each nth time slice. If None, all samples
within ``start`` and ``stop`` are used. Defalts to None.
reject : dict | None
Rejection parameters based on peak to peak amplitude.
Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.
If reject is None then no rejection is done. You should
use such parameters to reject big measurement artifacts
and not EOG for example. It only applies if `inst` is of type Raw.
Defaults to {'mag': 5e-12}
ecg_tmin : float
Start time before ECG event. Defaults to -0.5.
ecg_tmax : float
End time after ECG event. Defaults to 0.5.
eog_tmin : float
Start time before rog event. Defaults to -0.5.
eog_tmax : float
End time after rog event. Defaults to 0.5.
n_max_ecg : int | None
The maximum number of ECG components to exclude. Defaults to 3.
n_max_eog : int | None
The maximum number of EOG components to exclude. Defaults to 1.
n_max_ecg_epochs : int
The maximum number of ECG epochs to use for phase-consistency
estimation. Defaults to 200.
show : bool
Show figure if True
scale_img : float
The scaling factor for the report. Defaults to 1.0.
random_state : None | int | instance of np.random.RandomState
np.random.RandomState to initialize the FastICA estimation.
As the estimation is non-deterministic it can be useful to
fix the seed to have reproducible results. Defaults to None.
report : instance of Report | None
The report object. If None, a new report will be generated.
artifact_stats : None | dict
A dict that contains info on amplitude ranges of artifacts and
numbers of events, etc. by channel type.
Returns
-------
ica : instance of ICA
The ICA solution.
report : dict
A dict with an html report ('html') and artifact statistics ('stats').
"""
if report is None:
report = Report(subject=subject, title='ICA preprocessing')
if n_components == 'rank':
n_components = raw.estimate_rank(picks=picks)
ica = ICA(n_components=n_components, max_pca_components=None,
random_state=random_state, max_iter=256)
ica.fit(raw, picks=picks, decim=decim, reject=reject)
comment = []
for ch in ('mag', 'grad', 'eeg'):
if ch in ica:
comment += [ch.upper()]
if len(comment) > 0:
comment = '+'.join(comment) + ' '
else:
comment = ''
topo_ch_type = 'mag'
if 'GRAD' in comment and 'MAG' not in comment:
topo_ch_type = 'grad'
elif 'EEG' in comment:
topo_ch_type = 'eeg'
###########################################################################
# 2) identify bad components by analyzing latent sources.
title = '%s related to %s artifacts (red) ({})'.format(subject)
# generate ECG epochs use detection via phase statistics
reject_ = {'mag': 5e-12, 'grad': 5000e-13, 'eeg': 300e-6}
if reject is not None:
reject_.update(reject)
for ch_type in ['mag', 'grad', 'eeg']:
if ch_type not in ica:
reject_.pop(ch_type)
picks_ = np.array([raw.ch_names.index(k) for k in ica.ch_names])
if 'eeg' in ica:
if 'ecg' in raw:
picks_ = np.append(picks_,
pick_types(raw.info, meg=False, ecg=True)[0])
else:
logger.info('There is no ECG channel, trying to guess ECG from '
'magnetormeters')
if artifact_stats is None:
artifact_stats = dict()
ecg_epochs = create_ecg_epochs(raw, tmin=ecg_tmin, tmax=ecg_tmax,
keep_ecg=True, picks=picks_, reject=reject_)
n_ecg_epochs_found = len(ecg_epochs.events)
artifact_stats['ecg_n_events'] = n_ecg_epochs_found
n_max_ecg_epochs = min(n_max_ecg_epochs, n_ecg_epochs_found)
artifact_stats['ecg_n_used'] = n_max_ecg_epochs
sel_ecg_epochs = np.arange(n_ecg_epochs_found)
rng = np.random.RandomState(42)
rng.shuffle(sel_ecg_epochs)
ecg_ave = ecg_epochs.average()
report.add_figs_to_section(ecg_ave.plot(), 'ECG-full', 'artifacts')
ecg_epochs = ecg_epochs[sel_ecg_epochs[:n_max_ecg_epochs]]
ecg_ave = ecg_epochs.average()
report.add_figs_to_section(ecg_ave.plot(), 'ECG-used', 'artifacts')
_put_artifact_range(artifact_stats, ecg_ave, kind='ecg')
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs, method='ctps')
if len(ecg_inds) > 0:
ecg_evoked = ecg_epochs.average()
del ecg_epochs
fig = ica.plot_scores(scores, exclude=ecg_inds, labels='ecg',
title='', show=show)
report.add_figs_to_section(fig, 'scores ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
current_exclude = [e for e in ica.exclude] # issue #2608 MNE
fig = ica.plot_sources(raw, ecg_inds, exclude=ecg_inds,
title=title % ('components', 'ecg'), show=show)
report.add_figs_to_section(fig, 'sources ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
ica.exclude = current_exclude
fig = ica.plot_components(ecg_inds, ch_type=topo_ch_type,
title='', colorbar=True, show=show)
report.add_figs_to_section(fig, title % ('sources', 'ecg'),
section=comment + 'ECG', scale=img_scale)
ica.exclude = current_exclude
ecg_inds = ecg_inds[:n_max_ecg]
ica.exclude += ecg_inds
fig = ica.plot_sources(ecg_evoked, exclude=ecg_inds, show=show)
report.add_figs_to_section(fig, 'evoked sources ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
fig = ica.plot_overlay(ecg_evoked, exclude=ecg_inds, show=show)
report.add_figs_to_section(fig,
'rejection overlay ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
# detect EOG by correlation
picks_eog = np.concatenate(
[picks_, pick_types(raw.info, meg=False, eeg=False, ecg=False,
eog=True)])
eog_epochs = create_eog_epochs(raw, tmin=eog_tmin, tmax=eog_tmax,
picks=picks_eog, reject=reject_)
artifact_stats['eog_n_events'] = len(eog_epochs.events)
artifact_stats['eog_n_used'] = artifact_stats['eog_n_events']
eog_ave = eog_epochs.average()
report.add_figs_to_section(eog_ave.plot(), 'EOG-used', 'artifacts')
_put_artifact_range(artifact_stats, eog_ave, kind='eog')
eog_inds = None
if len(eog_epochs.events) > 0:
eog_inds, scores = ica.find_bads_eog(eog_epochs)
if eog_inds is not None and len(eog_epochs.events) > 0:
fig = ica.plot_scores(scores, exclude=eog_inds, labels='eog',
show=show, title='')
report.add_figs_to_section(fig, 'scores ({})'.format(subject),
section=comment + 'EOG',
scale=img_scale)
current_exclude = [e for e in ica.exclude] # issue #2608 MNE
fig = ica.plot_sources(raw, eog_inds, exclude=ecg_inds,
title=title % ('sources', 'eog'), show=show)
report.add_figs_to_section(fig, 'sources', section=comment + 'EOG',
scale=img_scale)
ica.exclude = current_exclude
fig = ica.plot_components(eog_inds, ch_type=topo_ch_type,
title='', colorbar=True, show=show)
report.add_figs_to_section(fig, title % ('components', 'eog'),
section=comment + 'EOG', scale=img_scale)
ica.exclude = current_exclude
eog_inds = eog_inds[:n_max_eog]
ica.exclude += eog_inds
eog_evoked = eog_epochs.average()
fig = ica.plot_sources(eog_evoked, exclude=eog_inds, show=show)
report.add_figs_to_section(
fig, 'evoked sources ({})'.format(subject),
section=comment + 'EOG', scale=img_scale)
fig = ica.plot_overlay(eog_evoked, exclude=eog_inds, show=show)
report.add_figs_to_section(
fig, 'rejection overlay({})'.format(subject),
section=comment + 'EOG', scale=img_scale)
else:
del eog_epochs
# check the amplitudes do not change
if len(ica.exclude) > 0:
fig = ica.plot_overlay(raw, show=show) # EOG artifacts remain
html = _render_components_table(ica)
report.add_htmls_to_section(
html, captions='excluded components',
section='ICA rejection summary (%s)' % ch_type)
report.add_figs_to_section(
fig, 'rejection overlay({})'.format(subject),
section=comment + 'RAW', scale=img_scale)
return ica, dict(html=report, stats=artifact_stats)
def generateReport(raw, ica, subj_name, subj_path, basename,
ecg_evoked, ecg_scores, ecg_inds, ECG_ch_name,
eog_evoked, eog_scores, eog_inds, EoG_ch_name):
from mne.report import Report
import numpy as np
import os
import HTML
report = Report()
ICA_title = 'Sources related to %s artifacts (red)'
is_show = False # visualization
File_length = str(round(raw.times[-1], 0))
# report.add_htmls_to_section(htmls=name_html, captions='File path', section='General')
name_html = '<h4 style="text-align:left;"> Path: ' + subj_path + '/' + basename + '.fif' + '</h4>'
ex_comps_table = [['', 'ICs to exclude'],['ECG', ecg_inds], ['EOG', eog_inds]]
ex_comps_html = '<h4>' + HTML.table(ex_comps_table) + '</h4>'
File_length_html = '<h4 style="text-align:left;">' + 'File length: ' + File_length + ' seconds' + '</h4>'
report.add_htmls_to_section(htmls=name_html + File_length_html + ex_comps_html, captions='General info', section='General info')
# --------------------- Generate report for ECG ---------------------------------------- #
fig1 = ica.plot_scores(
ecg_scores, exclude=ecg_inds, title=ICA_title % 'ecg', show=is_show)
# Pick the five largest ecg_scores and plot them
show_picks = np.abs(ecg_scores).argsort()[::-1][:5]
# Plot estimated latent sources given the unmixing matrix.
# topoplot of unmixing matrix columns
fig2 = ica.plot_components(
show_picks, title=ICA_title % 'ecg', colorbar=True, show=is_show)
# plot ECG sources + selection
fig3 = ica.plot_sources(ecg_evoked, exclude=ecg_inds, show=is_show)
fig = [fig1, fig2, fig3]
report.add_figs_to_section(fig, captions=['Scores of ICs related to ECG',
'TopoMap of ICs (ECG)',
'Time-locked ECG sources'], section='ICA - ECG')
# ----------------------------------- end generate report for ECG ------------------------------- #
# --------------------------------- Generate report for EoG --------------------------------------------- #
# check how many EoG ch we have
if set(EoG_ch_name.split(',')).issubset(set(raw.info['ch_names'])):
fig4 = ica.plot_scores(
eog_scores, exclude=eog_inds, title=ICA_title % 'eog', show=is_show)
report.add_figs_to_section(fig4, captions=['Scores of ICs related to EOG'],
section='ICA - EOG')
rs = np.shape(eog_scores)
if len(rs) > 1:
rr = rs[0]
show_picks = [np.abs(eog_scores[i][:]).argsort()[::-1][:5]
for i in range(rr)]
for i in range(rr):
fig5 = ica.plot_components(show_picks[i][:], title=ICA_title % 'eog',
colorbar=True, show=is_show) # ICA nel tempo
fig = [fig5]
report.add_figs_to_section(fig, captions=['Scores of ICs related to EOG'],
section='ICA - EOG')
else:
show_picks = np.abs(eog_scores).argsort()[::-1][:5]
fig5 = ica.plot_components(
show_picks, title=ICA_title % 'eog', colorbar=True, show=is_show)
fig = [fig5]
report.add_figs_to_section(fig, captions=['TopoMap of ICs (EOG)', ],
section='ICA - EOG')
fig9 = ica.plot_sources(eog_evoked, exclude=eog_inds, show=is_show) # plot EOG sources + selection
# fig10 = ica.plot_overlay(eog_evoked, exclude=eog_inds, show=is_show) # plot EOG cleaning
# fig = [fig9, fig10]
fig = [fig9]
report.add_figs_to_section(fig, captions=['Time-locked EOG sources'], section = 'ICA - EOG')
# -------------------- end generate report for EoG -------------------------------------------------------- #
# import ipdb; ipdb.set_trace()
IC_nums = range(ica.n_components_)
fig = ica.plot_components(picks=IC_nums, show=False)
report.add_figs_to_section(fig, captions=['All IC topographies'], section='ICA - muscles')
psds = []
captions_psd = []
ica_src = ica.get_sources(raw)
for iIC in IC_nums:
fig = ica_src.plot_psd(tmax=None, picks=[iIC], fmax=140, show=False)
fig.set_figheight(3)
fig.set_figwidth(5)
psds.append(fig)
captions_psd.append('IC #' + str(iIC))
# report.add_slider_to_section(figs=psds, captions=captions_psd, title='', section='ICA - muscles')
report.add_figs_to_section(figs=psds, captions=captions_psd, section='ICA - muscles')
report_filename = os.path.join(subj_path, basename + "-report.html")
print '******* ' + report_filename
report.save(report_filename, open_browser=False, overwrite=True)
percleft_cue = []
percleft_shock = []
for p in part:
# ______________________________________________________
# Make out dir
outdir = opj('/data/derivatives', p, 'eeg')
if not os.path.exists(outdir):
os.mkdir(outdir)
# _______________________________________________________
# Initialise MNE report
report = Report(verbose=False, subject=p, title='TFR report for part ' + p)
report.add_htmls_to_section(htmls=pprint.pformat(param),
captions='Parameters',
section='Parameters')
# ______________________________________________________
# Load cleaned raw file
raw = mne.io.read_raw_fif(opj(outdir,
p + '_task-fearcond_cleanedeeg_raw.fif'),
preload=True)
# ______________________________________________________
# Additional filter
raw = raw.filter(l_freq=None, h_freq=200)
# Load trial info in scr data
events = pd.read_csv(layout.get(subject=p[-2:],
extension='tsv',
def compute_ica(raw,
subject,
n_components=0.99,
picks=None,
decim=None,
reject=None,
ecg_tmin=-0.5,
ecg_tmax=0.5,
eog_tmin=-0.5,
eog_tmax=0.5,
n_max_ecg=3,
n_max_eog=1,
n_max_ecg_epochs=200,
show=True,
img_scale=1.0,
random_state=None,
report=None,
artifact_stats=None):
"""Run ICA in raw data
Parameters
----------,
raw : instance of Raw
Raw measurements to be decomposed.
subject : str
The name of the subject.
picks : array-like of int, shape(n_channels, ) | None
Channels to be included. This selection remains throughout the
initialized ICA solution. If None only good data channels are used.
Defaults to None.
n_components : int | float | None | 'rank'
The number of components used for ICA decomposition. If int, it must be
smaller then max_pca_components. If None, all PCA components will be
used. If float between 0 and 1 components can will be selected by the
cumulative percentage of explained variance.
If 'rank', the number of components equals the rank estimate.
Defaults to 0.99.
decim : int | None
Increment for selecting each nth time slice. If None, all samples
within ``start`` and ``stop`` are used. Defalts to None.
reject : dict | None
Rejection parameters based on peak to peak amplitude.
Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.
If reject is None then no rejection is done. You should
use such parameters to reject big measurement artifacts
and not EOG for example. It only applies if `inst` is of type Raw.
Defaults to {'mag': 5e-12}
ecg_tmin : float
Start time before ECG event. Defaults to -0.5.
ecg_tmax : float
End time after ECG event. Defaults to 0.5.
eog_tmin : float
Start time before rog event. Defaults to -0.5.
eog_tmax : float
End time after rog event. Defaults to 0.5.
n_max_ecg : int | None
The maximum number of ECG components to exclude. Defaults to 3.
n_max_eog : int | None
The maximum number of EOG components to exclude. Defaults to 1.
n_max_ecg_epochs : int
The maximum number of ECG epochs to use for phase-consistency
estimation. Defaults to 200.
show : bool
Show figure if True
scale_img : float
The scaling factor for the report. Defaults to 1.0.
random_state : None | int | instance of np.random.RandomState
np.random.RandomState to initialize the FastICA estimation.
As the estimation is non-deterministic it can be useful to
fix the seed to have reproducible results. Defaults to None.
report : instance of Report | None
The report object. If None, a new report will be generated.
artifact_stats : None | dict
A dict that contains info on amplitude ranges of artifacts and
numbers of events, etc. by channel type.
Returns
-------
ica : instance of ICA
The ICA solution.
report : dict
A dict with an html report ('html') and artifact statistics ('stats').
"""
if report is None:
report = Report(subject=subject, title='ICA preprocessing')
if n_components == 'rank':
n_components = raw.estimate_rank(picks=picks)
ica = ICA(n_components=n_components,
max_pca_components=None,
random_state=random_state,
max_iter=256)
ica.fit(raw, picks=picks, decim=decim, reject=reject)
comment = []
for ch in ('mag', 'grad', 'eeg'):
if ch in ica:
comment += [ch.upper()]
if len(comment) > 0:
comment = '+'.join(comment) + ' '
else:
comment = ''
topo_ch_type = 'mag'
if 'GRAD' in comment and 'MAG' not in comment:
topo_ch_type = 'grad'
elif 'EEG' in comment:
topo_ch_type = 'eeg'
###########################################################################
# 2) identify bad components by analyzing latent sources.
title = '%s related to %s artifacts (red) ({})'.format(subject)
# generate ECG epochs use detection via phase statistics
reject_ = {'mag': 5e-12, 'grad': 5000e-13, 'eeg': 300e-6}
if reject is not None:
reject_.update(reject)
for ch_type in ['mag', 'grad', 'eeg']:
if ch_type not in ica:
reject_.pop(ch_type)
picks_ = np.array([raw.ch_names.index(k) for k in ica.ch_names])
if 'eeg' in ica:
if 'ecg' in raw:
picks_ = np.append(picks_,
pick_types(raw.info, meg=False, ecg=True)[0])
else:
logger.info('There is no ECG channel, trying to guess ECG from '
'magnetormeters')
if artifact_stats is None:
artifact_stats = dict()
ecg_epochs = create_ecg_epochs(raw,
tmin=ecg_tmin,
tmax=ecg_tmax,
keep_ecg=True,
picks=picks_,
reject=reject_)
n_ecg_epochs_found = len(ecg_epochs.events)
artifact_stats['ecg_n_events'] = n_ecg_epochs_found
n_max_ecg_epochs = min(n_max_ecg_epochs, n_ecg_epochs_found)
artifact_stats['ecg_n_used'] = n_max_ecg_epochs
sel_ecg_epochs = np.arange(n_ecg_epochs_found)
rng = np.random.RandomState(42)
rng.shuffle(sel_ecg_epochs)
ecg_ave = ecg_epochs.average()
report.add_figs_to_section(ecg_ave.plot(), 'ECG-full', 'artifacts')
ecg_epochs = ecg_epochs[sel_ecg_epochs[:n_max_ecg_epochs]]
ecg_ave = ecg_epochs.average()
report.add_figs_to_section(ecg_ave.plot(), 'ECG-used', 'artifacts')
_put_artifact_range(artifact_stats, ecg_ave, kind='ecg')
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs, method='ctps')
if len(ecg_inds) > 0:
ecg_evoked = ecg_epochs.average()
del ecg_epochs
fig = ica.plot_scores(scores,
exclude=ecg_inds,
labels='ecg',
title='',
show=show)
report.add_figs_to_section(fig,
'scores ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
current_exclude = [e for e in ica.exclude] # issue #2608 MNE
fig = ica.plot_sources(raw,
ecg_inds,
exclude=ecg_inds,
title=title % ('components', 'ecg'),
show=show)
report.add_figs_to_section(fig,
'sources ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
ica.exclude = current_exclude
fig = ica.plot_components(ecg_inds,
ch_type=topo_ch_type,
title='',
colorbar=True,
show=show)
report.add_figs_to_section(fig,
title % ('sources', 'ecg'),
section=comment + 'ECG',
scale=img_scale)
ica.exclude = current_exclude
ecg_inds = ecg_inds[:n_max_ecg]
ica.exclude += ecg_inds
fig = ica.plot_sources(ecg_evoked, exclude=ecg_inds, show=show)
report.add_figs_to_section(fig,
'evoked sources ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
fig = ica.plot_overlay(ecg_evoked, exclude=ecg_inds, show=show)
report.add_figs_to_section(fig,
'rejection overlay ({})'.format(subject),
section=comment + 'ECG',
scale=img_scale)
# detect EOG by correlation
picks_eog = np.concatenate([
picks_,
pick_types(raw.info, meg=False, eeg=False, ecg=False, eog=True)
])
eog_epochs = create_eog_epochs(raw,
tmin=eog_tmin,
tmax=eog_tmax,
picks=picks_eog,
reject=reject_)
artifact_stats['eog_n_events'] = len(eog_epochs.events)
artifact_stats['eog_n_used'] = artifact_stats['eog_n_events']
eog_ave = eog_epochs.average()
report.add_figs_to_section(eog_ave.plot(), 'EOG-used', 'artifacts')
_put_artifact_range(artifact_stats, eog_ave, kind='eog')
eog_inds = None
if len(eog_epochs.events) > 0:
eog_inds, scores = ica.find_bads_eog(eog_epochs)
if eog_inds is not None and len(eog_epochs.events) > 0:
fig = ica.plot_scores(scores,
exclude=eog_inds,
labels='eog',
show=show,
title='')
report.add_figs_to_section(fig,
'scores ({})'.format(subject),
section=comment + 'EOG',
scale=img_scale)
current_exclude = [e for e in ica.exclude] # issue #2608 MNE
fig = ica.plot_sources(raw,
eog_inds,
exclude=ecg_inds,
title=title % ('sources', 'eog'),
show=show)
report.add_figs_to_section(fig,
'sources',
section=comment + 'EOG',
scale=img_scale)
ica.exclude = current_exclude
fig = ica.plot_components(eog_inds,
ch_type=topo_ch_type,
title='',
colorbar=True,
show=show)
report.add_figs_to_section(fig,
title % ('components', 'eog'),
section=comment + 'EOG',
scale=img_scale)
ica.exclude = current_exclude
eog_inds = eog_inds[:n_max_eog]
ica.exclude += eog_inds
eog_evoked = eog_epochs.average()
fig = ica.plot_sources(eog_evoked, exclude=eog_inds, show=show)
report.add_figs_to_section(fig,
'evoked sources ({})'.format(subject),
section=comment + 'EOG',
scale=img_scale)
fig = ica.plot_overlay(eog_evoked, exclude=eog_inds, show=show)
report.add_figs_to_section(fig,
'rejection overlay({})'.format(subject),
section=comment + 'EOG',
scale=img_scale)
else:
del eog_epochs
# check the amplitudes do not change
if len(ica.exclude) > 0:
fig = ica.plot_overlay(raw, show=show) # EOG artifacts remain
html = _render_components_table(ica)
report.add_htmls_to_section(html,
captions='excluded components',
section='ICA rejection summary (%s)' %
ch_type)
report.add_figs_to_section(fig,
'rejection overlay({})'.format(subject),
section=comment + 'RAW',
scale=img_scale)
return ica, dict(html=report, stats=artifact_stats)
# _______________________________________________________
# Make fslevel part dir
pdir = opj(outdir, p, 'eeg')
if not os.path.exists(pdir):
os.mkdir(pdir)
# _______________________________________________________
# Initialise MNE report
report = Report(verbose=False, subject=p,
title='EEG report for part ' + p)
# report.add_htmls_to_section(
# htmls=part.comments[p], captions='Comments', section='Comments')
report.add_htmls_to_section(
htmls=pprint.pformat(param),
captions='Parameters',
section='Parameters')
# ______________________________________________________
# Load EEG file
f = layout.get(subject=p[-2:], extension='bdf', return_type='filename')[0]
raw = mne.io.read_raw_bdf(f, verbose=False,
eog=param['eogchan'],
exclude=param['dropchan'],
preload=True)
# Rename external channels
raw.rename_channels(param['renamechan'])
events = pd.read_csv(layout.get(subject=p[-2:], extension='tsv',
