def main():
"""Run sliding estimator."""
if not config.contrasts:
msg = 'No contrasts specified; not performing decoding.'
logger.info(**gen_log_kwargs(message=msg))
return
if not config.decode:
msg = 'No decoding requested by user.'
logger.info(**gen_log_kwargs(message=msg))
return
# Here we go parallel inside the :class:`mne.decoding.SlidingEstimator`
# so we don't dispatch manually to multiple jobs.
parallel, run_func, _ = parallel_func(run_time_decoding, n_jobs=1)
logs = parallel(
run_func(cfg=get_config(),
subject=subject,
condition1=cond_1,
condition2=cond_2,
session=session)
for subject, session, (cond_1, cond_2) in itertools.product(
config.get_subjects(), config.get_sessions(), config.contrasts))
config.save_logs(logs)
def main():
"""Make reports."""
parallel, run_func, _ = parallel_func(run_report,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(
cfg=get_config(subject=subject), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
sessions = config.get_sessions()
if not sessions:
sessions = [None]
if (config.get_task() is not None and config.get_task().lower() == 'rest'):
msg = ' … skipping "average" report for "rest" task.'
logger.info(**gen_log_kwargs(message=msg))
return
for session in sessions:
run_report_average(cfg=get_config(subject='average'),
subject='average',
session=session)
def main():
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
log = run_group_average_source(cfg=get_config())
config.save_logs([log])
def main():
"""Run epochs."""
parallel, run_func, _ = parallel_func(drop_ptp, n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run inv."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(run_inverse,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run ICA."""
if not config.spatial_filter == 'ica':
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(run_ica, n_jobs=config.get_n_jobs())
logs = parallel(
run_func(
cfg=get_config(subject=subject), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run Time-frequency decomposition."""
if not config.time_frequency_conditions:
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(run_time_frequency,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run epochs."""
# Here we use fewer n_jobs to prevent potential memory problems
parallel, run_func, _ = parallel_func(
run_epochs,
n_jobs=max(config.get_n_jobs() // 4, 1)
)
logs = parallel(
run_func(cfg=get_config(subject, session), subject=subject,
session=session)
for subject, session in
itertools.product(config.get_subjects(), config.get_sessions())
)
config.save_logs(logs)
def main():
"""Run filter."""
parallel, run_func, _ = parallel_func(filter_data,
n_jobs=config.get_n_jobs())
# Enabling different runs for different subjects
sub_run_ses = []
for subject in config.get_subjects():
sub_run_ses += list(
itertools.product([subject], config.get_runs(subject=subject),
config.get_sessions()))
logs = parallel(
run_func(
cfg=get_config(subject), subject=subject, run=run, session=session)
for subject, run, session in sub_run_ses)
config.save_logs(logs)
def main():
"""Run maxwell_filter."""
if not config.use_maxwell_filter:
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(run_maxwell_filter,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(subject, session),
subject=subject,
session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Run BEM surface extraction."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
if config.use_template_mri:
msg = ' … skipping BEM computating when using MRI template.'
logger.info(**gen_log_kwargs(message=msg))
return
parallel, run_func, _ = parallel_func(make_bem_and_scalp_surface,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(subject=subject), subject=subject)
for subject in config.get_subjects())
config.save_logs(logs)
def main():
"""Run cov."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
if config.noise_cov == "ad-hoc":
msg = ' … skipping: using ad-hoc diagonal covariance.'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(run_covariance,
n_jobs=config.get_n_jobs())
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
config.save_logs(logs)
def main():
"""Apply ssp."""
if not config.spatial_filter == 'ssp':
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(
apply_ssp,
n_jobs=config.get_n_jobs()
)
logs = parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in
itertools.product(
config.get_subjects(),
config.get_sessions()
)
)
config.save_logs(logs)
def main():
log = run_group_average_source(cfg=get_config())
config.save_logs([log])
def main():
log = run_group_average_sensor(cfg=get_config(), subject='average')
config.save_logs([log])
