def plot_auto_scores(cfg, subject, session):
"""Plot automated bad channel detection scores.
"""
import json_tricks
fname_scores = BIDSPath(subject=subject,
session=session,
task=cfg.task,
acquisition=cfg.acq,
run=None,
processing=cfg.proc,
recording=cfg.rec,
space=cfg.space,
suffix='scores',
extension='.json',
datatype=cfg.datatype,
root=cfg.deriv_root,
check=False)
all_figs = []
all_captions = []
for run in cfg.runs:
with open(fname_scores.update(run=run), 'r') as f:
auto_scores = json_tricks.load(f)
figs = config.plot_auto_scores(auto_scores)
all_figs.extend(figs)
# Could be more than 1 fig, e.g. "grad" and "mag"
captions = [f'Run {run}'] * len(figs)
all_captions.extend(captions)
return all_figs, all_captions
def plot_auto_scores(subject, session):
"""Plot automated bad channel detection scores.
"""
import json_tricks
deriv_path = config.get_subject_deriv_path(subject=subject,
session=session,
kind=config.get_kind())
fname_scores = BIDSPath(subject=subject,
session=session,
task=config.get_task(),
acquisition=config.acq,
run=None,
processing=config.proc,
recording=config.rec,
space=config.space,
kind='scores',
extension='.json',
prefix=deriv_path,
check=False)
all_figs = []
all_captions = []
for run in config.get_runs():
with open(fname_scores.update(run=run), 'r') as f:
auto_scores = json_tricks.load(f)
figs = config.plot_auto_scores(auto_scores)
all_figs.extend(figs)
# Could be more than 1 fig, e.g. "grad" and "mag"
captions = [f'Run {run}'] * len(figs)
all_captions.extend(captions)
return all_figs, all_captions
def find_bad_channels(raw, subject, session, task, run):
if (config.find_flat_channels_meg and not config.find_noisy_channels_meg):
msg = 'Finding flat channels.'
elif (config.find_noisy_channels_meg
and not config.find_flat_channels_meg):
msg = 'Finding noisy channels using Maxwell filtering.'
else:
msg = ('Finding flat channels, and noisy channels using '
'Maxwell filtering.')
logger.info(
gen_log_message(message=msg, step=1, subject=subject, session=session))
bids_path = BIDSPath(subject=subject,
session=session,
task=task,
run=run,
acquisition=config.acq,
processing=config.proc,
recording=config.rec,
space=config.space,
suffix=config.get_datatype(),
datatype=config.get_datatype(),
root=config.deriv_root)
auto_noisy_chs, auto_flat_chs, auto_scores = find_bad_channels_maxwell(
raw=raw,
calibration=get_mf_cal_fname(subject, session),
cross_talk=get_mf_ctc_fname(subject, session),
return_scores=True)
preexisting_bads = raw.info['bads'].copy()
bads = preexisting_bads.copy()
if config.find_flat_channels_meg:
msg = f'Found {len(auto_flat_chs)} flat channels.'
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
bads.extend(auto_flat_chs)
if config.find_noisy_channels_meg:
msg = f'Found {len(auto_noisy_chs)} noisy channels.'
logger.info(
gen_log_message(message=msg,
step=1,
subject=subject,
session=session))
bads.extend(auto_noisy_chs)
bads = sorted(set(bads))
raw.info['bads'] = bads
msg = f'Marked {len(raw.info["bads"])} channels as bad.'
logger.info(
gen_log_message(message=msg, step=1, subject=subject, session=session))
if config.find_noisy_channels_meg:
auto_scores_fname = bids_path.copy().update(suffix='scores',
extension='.json',
check=False)
with open(auto_scores_fname, 'w') as f:
json_tricks.dump(auto_scores,
fp=f,
allow_nan=True,
sort_keys=False)
if config.interactive:
import matplotlib.pyplot as plt
config.plot_auto_scores(auto_scores)
plt.show()
# Write the bad channels to disk.
bads_tsv_fname = bids_path.copy().update(suffix='bads',
extension='.tsv',
check=False)
bads_for_tsv = []
reasons = []
if config.find_flat_channels_meg:
bads_for_tsv.extend(auto_flat_chs)
reasons.extend(['auto-flat'] * len(auto_flat_chs))
preexisting_bads = set(preexisting_bads) - set(auto_flat_chs)
if config.find_noisy_channels_meg:
bads_for_tsv.extend(auto_noisy_chs)
reasons.extend(['auto-noisy'] * len(auto_noisy_chs))
preexisting_bads = set(preexisting_bads) - set(auto_noisy_chs)
preexisting_bads = list(preexisting_bads)
if preexisting_bads:
bads_for_tsv.extend(preexisting_bads)
reasons.extend(['pre-existing (before mne-study-template was run)'] *
len(preexisting_bads))
tsv_data = pd.DataFrame(dict(name=bads_for_tsv, reason=reasons))
tsv_data = tsv_data.sort_values(by='name')
tsv_data.to_csv(bads_tsv_fname, sep='\t', index=False)