def run_group_average_source(*, cfg, subject='average'):
"""Run group average in source space"""
if not config.run_source_estimation:
msg = ' … skipping: run_source_estimation is set to False.'
logger.info(**gen_log_kwargs(message=msg))
return
mne.datasets.fetch_fsaverage(subjects_dir=config.get_fs_subjects_dir())
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 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."""
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():
"""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():
# Ensure we're also processing fsaverage if present
subjects = config.get_subjects()
if (Path(config.get_fs_subjects_dir()) / 'fsaverage').exists():
subjects.append('fsaverage')
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 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 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 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 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():
"""Initialize the output directories."""
msg = 'Running: Initializing output directories.'
logger.info(**gen_log_kwargs(message=msg))
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 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 get_config(subject: Optional[str] = None,
session: Optional[str] = None) -> BunchConst:
cfg = BunchConst(task=config.get_task(),
datatype=config.get_datatype(),
acq=config.acq,
rec=config.rec,
space=config.space,
deriv_root=config.get_deriv_root(),
conditions=config.conditions,
contrasts=config.contrasts,
decode=config.decode,
decoding_metric=config.decoding_metric,
decoding_n_splits=config.decoding_n_splits,
random_state=config.random_state,
analyze_channels=config.analyze_channels,
ch_types=config.ch_types,
eeg_reference=config.get_eeg_reference(),
n_jobs=config.get_n_jobs())
return cfg
def get_config(subject: Optional[str] = None,
session: Optional[str] = None) -> SimpleNamespace:
cfg = SimpleNamespace(
task=config.get_task(),
runs=config.get_runs(subject=subject),
datatype=config.get_datatype(),
acq=config.acq,
rec=config.rec,
space=config.space,
mindist=config.mindist,
spacing=config.spacing,
use_template_mri=config.use_template_mri,
source_info_path_update=config.source_info_path_update,
ch_types=config.ch_types,
fs_subject=config.get_fs_subject(subject=subject),
fs_subjects_dir=config.get_fs_subjects_dir(),
deriv_root=config.get_deriv_root(),
bids_root=config.get_bids_root(),
n_jobs=config.get_n_jobs())
return cfg
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 get_config(
subject: Optional[str] = None,
session: Optional[str] = None
) -> BunchConst:
cfg = BunchConst(
task=config.get_task(),
runs=config.get_runs(subject=subject),
datatype=config.get_datatype(),
acq=config.acq,
rec=config.rec,
space=config.space,
mindist=config.mindist,
spacing=config.spacing,
use_template_mri=config.use_template_mri,
ch_types=config.ch_types,
fs_subject=config.get_fs_subject(subject=subject),
fs_subjects_dir=config.get_fs_subjects_dir(),
deriv_root=config.get_deriv_root(),
bids_root=config.get_bids_root(),
n_jobs=config.get_n_jobs()
)
return cfg
