def run_group_average_source(*, cfg, subject='average'):
"""Run group average in source space"""
mne.datasets.fetch_fsaverage(subjects_dir=config.get_fs_subjects_dir())
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(morph_stc,
n_jobs=config.get_n_jobs())
all_morphed_stcs = parallel(
run_func(cfg=cfg,
subject=subject,
fs_subject=config.get_fs_subject(subject),
session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
mean_morphed_stcs = np.array(all_morphed_stcs).mean(axis=0)
# XXX to fix
sessions = config.get_sessions()
if sessions:
session = sessions[0]
else:
session = None
run_average(cfg=cfg,
session=session,
mean_morphed_stcs=mean_morphed_stcs)
def main():
"""Make reports."""
with config.get_parallel_backend():
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():
# Ensure we're also processing fsaverage if present
subjects = config.get_subjects()
if (Path(config.get_fs_subjects_dir()) / 'fsaverage').exists():
subjects.append('fsaverage')
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(make_coreg_surfaces,
n_jobs=config.get_n_jobs())
parallel(run_func(get_config(), subject) for subject in subjects)
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
with config.get_parallel_backend():
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 epochs."""
parallel, run_func, _ = parallel_func(drop_ptp, n_jobs=config.get_n_jobs())
with config.get_parallel_backend():
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 ICA."""
if not config.spatial_filter == 'ica':
msg = 'Skipping …'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(apply_ica,
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():
"""Initialize the output directories."""
msg = 'Running: Initializing output directories.'
logger.info(**gen_log_kwargs(message=msg))
with config.get_parallel_backend():
init_dataset(cfg=get_config())
parallel, run_func, _ = parallel_func(init_subject_dirs,
n_jobs=config.get_n_jobs())
parallel(
run_func(cfg=get_config(), subject=subject, session=session)
for subject, session in itertools.product(config.get_subjects(),
config.get_sessions()))
msg = 'Completed: Initializing output directories.'
logger.info(**gen_log_kwargs(message=msg))
def main() -> None:
"""Run freesurfer recon-all command on BIDS dataset.
The script allows to run the freesurfer recon-all
command on all subjects of your BIDS dataset. It can
run in parallel with the --n_jobs parameter.
It is built on top of the FreeSurfer BIDS app:
https://github.com/BIDS-Apps/freesurfer
and the MNE BIDS Pipeline
https://mne.tools/mne-bids-pipeline
You must have freesurfer available on your system.
Run via the MNE BIDS Pipeline's `run.py`:
python run.py --steps=freesurfer --config=your_pipeline_config.py
""" # noqa
logger.info('Running FreeSurfer')
subjects = config.get_subjects()
root_dir = config.get_bids_root()
subjects_dir = Path(config.get_fs_subjects_dir())
subjects_dir.mkdir(parents=True, exist_ok=True)
with config.get_parallel_backend():
n_jobs = config.get_n_jobs()
parallel, run_func, _ = parallel_func(run_recon, n_jobs=n_jobs)
parallel(run_func(root_dir, subject, fs_bids_app)
for subject in subjects)
# Handle fsaverage
fsaverage_dir = subjects_dir / 'fsaverage'
if fsaverage_dir.exists():
if fsaverage_dir.is_symlink():
fsaverage_dir.unlink()
else:
shutil.rmtree(fsaverage_dir)
env = os.environ
shutil.copytree(f"{env['FREESURFER_HOME']}/subjects/fsaverage",
subjects_dir / 'fsaverage')
def main():
"""Run forward."""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
with config.get_parallel_backend():
parallel, run_func, _ = parallel_func(run_forward,
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 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 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():
"""Run filter."""
with config.get_parallel_backend():
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)
