def _init_subjects(filter_sub, root, ch_type):
"""Initialize collection of files by loading selected subjects
Parameters
----------
filter_sub : list
a list of subjects to select from all BIDS files
root : str
root of BIDS dataset
ch_type: str
type of BIDS dataset
Returns
----------
files: list
a list of partially filtered BIDS paths according to subjects
"""
if filter_sub == ["*"]:
filter_sub = mne_bids.get_entity_vals(root, 'subject')
filered_subjects = []
bids_root = pathlib.Path(root)
type_exten = ALLOWED_DATATYPE_EXTENSIONS[ch_type]
for subject in filter_sub:
bids_path = mne_bids.BIDSPath(subject=subject,
datatype=ch_type,
root=bids_root)
files = bids_path.match()
files_eeg = [f for f in files if f.extension.lower() in type_exten]
filered_subjects += files_eeg
return filered_subjects
def fif_to_bids(path: Path):
raw = mne.io.read_raw_fif(path)
bids_path = mne_bids.BIDSPath(
subject="01",
session="01",
task="testing",
acquisition="01",
run="01",
root=data_dir / "BIDS",
)
mne_bids.write_raw_bids(raw, bids_path, overwrite=True)
def subj_files(avail_subj, root):
# create a dictionary of subjects and corresponding files
subjects = {}
for subject in avail_subj:
bids_path = mne_bids.BIDSPath(subject=subject, root=root)
files = bids_path.match()
scans_file = [f for f in files if "scans" in f.suffix][0]
scans_info = pd.read_csv(scans_file, sep='\t')
filenames = scans_info["filename"].to_list()
cleaned_filenames = []
for file in filenames:
_, tail = os.path.split(file)
cleaned_filenames.append(tail)
subjects[subject] = cleaned_filenames
return subjects
def _filter_exceptions(subjects, tasks, runs, files, root, ch_type):
"""Remove exceptions as defined by user_params
Parameters
----------
subjects, tasks, runs : list
a list of subjects, tasks, and runs to be
cartesian multiplied to get omitted files
files: list
list of partially filtered files according to subject and tasks
root: str
root of BIDS dataset
ch_type: str
type of BIDS dataset
Returns
----------
files: list
a list of fully filtered BIDS paths according to exceptions
"""
# get cartesian product of subjects, tasks, and runs
exceptions = list(product(subjects, tasks, runs))
bids_root = pathlib.Path(root)
type_exten = ALLOWED_DATATYPE_EXTENSIONS[ch_type]
# turn each exception into a BIDS path
for i in range(len(exceptions)):
file = exceptions[i]
sub = file[0]
task = file[1]
run = file[2]
bids_path = mne_bids.BIDSPath(subject=sub,
task=task,
run=run,
datatype=ch_type,
root=bids_root)
e_files = bids_path.match()
e_files_eeg = [f for f in e_files if f.extension.lower() in type_exten]
exceptions[i] = e_files_eeg[0]
# remove any file in files that shows up in exceptions and return
return [f for f in files if f not in exceptions]
ch_type = 'eeg'
for subject in subjects:
frontalEOGChannel = 'E22'
# Load preprocessed mne data from Guillaume
fname = os.path.join(data_path, subject, 'RestingState_Blinks_epo.fif')
epochs = mne.read_epochs(fname, proj=True, preload=True, verbose=None)
subject = subject.lower() + "01"
#eo.plot_image(picks=[frontalEOGChannel])
## Do some preprocessing on the epochs - jitter the center so the model doesn't learn the position
epochs_preprocessed = epochs.crop(tmin=-0.4 + np.random.random() * 0.1,
tmax=0.40 + np.random.random() * 0.1,
include_tmax=True)
# Check if resampling is needed
epochs_preprocessed = epochs_preprocessed.resample(100)
# Create bids folder
bids_path = mne_bids.BIDSPath(subject=subject, root=export_folder)
# Use the raw object that the trials came from in order to build the BIDS tree
mne_bids.write_raw_bids(fake_raw, bids_path, overwrite=True, verbose=True)
# Export trials into .nii files
export_epoch_to_nifti_small.run_export(epochs_preprocessed, ch_type,
annotated_event_for_gt, bids_path)
if subj_fullname in sub_id_name.keys():
subj_id = str(sub_id_name[subj_fullname])
task_name = raw_file_path.parts[-1].split('.')[0][-4:] # vid%
if subj_id in bad_subjects.keys(
) and task_name in bad_subjects[subj_id]:
continue
raw_meg = make_annotations_from_events(
raw_meg) # make readable annotations
else:
subj_id = 'emptyroom'
task_name = 'noise'
date_record = str(raw_meg.info['meas_date']).split(' ')[0].replace('-', '')
meg_bids_path = mne_bids.BIDSPath(subject=subj_id,
session=date_record,
task=task_name,
root=data_bids_dir)
# mri_bids_path = mne_bids.BIDSPath(subject=subj_id, session=date_record,
# root=data_bids_dir)
#
mne_bids.write_raw_bids(raw=raw_meg,
bids_path=meg_bids_path,
anonymize={'daysback': 40000},
overwrite=True,
verbose=True)
if subj_id != 'emptyroom':
mne_bids.write_meg_crosstalk(ct_file, meg_bids_path)
if int(date_record[:4]) < 2020:
#epochs_heartbeat['999'].plot_image(picks='meg')
export_folder = '/home/nas/Desktop/test_BIDS'
# Select channel type to create the topographies on
ch_type = 'grad'
for iSubject in range(1, 15):
annotated_event_for_gt = '998' # This is the event that will be used to create the derivatives
# 999 Heartbeats
# 998 Blinks
#epochs_ = epochs_heartbeat[(iSubject-1)*3:iSubject*3]
epochs_ = epochs_blink[(iSubject - 1) * 3:iSubject * 3]
# Create bids folder
bids_path = mne_bids.BIDSPath(subject='IVADOMEDSubjTest' +
str(iSubject),
session='IVADOMEDSession1',
task='testing',
acquisition='01',
run='01',
root=export_folder)
# Use the raw object that the trials came from in order to build the BIDS tree
mne_bids.write_raw_bids(raw, bids_path, overwrite=True, verbose=True)
# Export trials into .nii files
export_epoch_to_nifti_small.run_export(epochs_, ch_type,
annotated_event_for_gt,
bids_path)