def test_vhdr():
"""Test raw_to_bids conversion for BrainVision data."""
output_path = _TempDir()
data_path = op.join(base_path, 'brainvision', 'tests', 'data')
raw_fname = op.join(data_path, 'test.vhdr')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False,
kind='eeg')
cmd = ['bids-validator', '--bep006', output_path]
run_subprocess(cmd, shell=shell)
# create another bids folder with the overwrite command and check
# no files are in the folder
data_path = make_bids_folders(subject=subject_id,
session=session_id,
kind='eeg',
root=output_path,
overwrite=True)
assert len([f for f in os.listdir(data_path) if op.isfile(f)]) == 0
def test_bti():
"""
Testing the functionality of the raw_to_bids conversion for BTi data.
"""
output_path = _TempDir()
data_path = op.join(base_path, 'bti', 'tests', 'data')
raw_fname = op.join(data_path, 'test_pdf_linux')
config_fname = op.join(data_path, 'test_config_linux')
headshape_fname = op.join(data_path, 'test_hs_linux')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_file=raw_fname,
config=config_fname,
hsp=headshape_fname,
output_path=output_path,
acquisition=acq_a,
verbose=True,
overwrite=True)
# run again with a different acquisiton parameter
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_file=raw_fname,
config=config_fname,
hsp=headshape_fname,
output_path=output_path,
acquisition=acq_b,
verbose=True,
overwrite=True)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
def test_ctf():
"""Test functionality of the raw_to_bids conversion for CTF data."""
output_path = _TempDir()
data_path = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(data_path, 'testdata_ctf.ds')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
# test to check that running again with overwrite == False raises an error
with pytest.raises(OSError, match="already exists"):
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False)
assert op.exists(op.join(output_path, 'participants.tsv'))
def test_kit():
"""
Testing the functionality of the raw_to_bids conversion for KIT data.
"""
output_path = _TempDir()
data_path = op.join(base_path, 'kit', 'tests', 'data')
raw_fname = op.join(data_path, 'test.sqd')
events_fname = op.join(data_path, 'test-eve.txt')
hpi_fname = op.join(data_path, 'test_mrk.sqd')
electrode_fname = op.join(data_path, 'test_elp.txt')
headshape_fname = op.join(data_path, 'test_hsp.txt')
event_id = dict(cond=1)
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_file=raw_fname,
events_data=events_fname,
event_id=event_id,
hpi=hpi_fname,
electrode=electrode_fname,
hsp=headshape_fname,
output_path=output_path,
overwrite=True)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
def test_ctf():
"""
Testing the functionality of the raw_to_bids conversion for CTF data.
"""
output_path = _TempDir()
data_path = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(data_path, 'testdata_ctf.ds')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_file=raw_fname,
output_path=output_path,
acquisition=acq_a,
overwrite=True)
# run again with a different acquisiton parameter
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_file=raw_fname,
output_path=output_path,
acquisition=acq_b,
overwrite=True)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
def test_edf():
"""Test raw_to_bids conversion for European Data Format data."""
output_path = _TempDir()
data_path = op.join(testing.data_path(), 'EDF')
raw_fname = op.join(data_path, 'test_reduced.edf')
raw = mne.io.read_raw_edf(raw_fname, preload=True)
raw.rename_channels({raw.info['ch_names'][0]: 'EOG'})
raw.info['chs'][0]['coil_type'] = FIFF.FIFFV_COIL_EEG_BIPOLAR
raw.rename_channels({raw.info['ch_names'][1]: 'EMG'})
raw.set_channel_types({'EMG': 'emg'})
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw,
output_path=output_path,
overwrite=False,
kind='eeg')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run2,
task=task,
acquisition=acq,
raw_file=raw,
output_path=output_path,
overwrite=True,
kind='eeg')
cmd = ['bids-validator', '--bep006', output_path]
run_subprocess(cmd, shell=shell)
# ensure there is an EMG channel in the channels.tsv:
channels_tsv = make_bids_filename(subject=subject_id,
session=session_id,
task=task,
run=run,
suffix='channels.tsv',
acquisition=acq,
prefix=op.join(output_path,
'sub-01/ses-01/eeg'))
df = pd.read_csv(channels_tsv, sep='\t')
assert 'ElectroMyoGram' in df['description'].values
# check that the scans list contains two scans
scans_tsv = make_bids_filename(subject=subject_id,
session=session_id,
suffix='scans.tsv',
prefix=op.join(output_path,
'sub-01/ses-01'))
df = pd.read_csv(scans_tsv, sep='\t')
assert df.shape[0] == 2
def test_fif():
"""
Testing the functionality of the raw_to_bids conversion for
Neuromag data.
"""
output_path = _TempDir()
data_path = testing.data_path()
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
event_id = {
'Auditory/Left': 1,
'Auditory/Right': 2,
'Visual/Left': 3,
'Visual/Right': 4,
'Smiley': 5,
'Button': 32
}
events_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw-eve.fif')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_file=raw_fname,
events_data=events_fname,
output_path=output_path,
event_id=event_id,
acquisition=acq_a,
overwrite=True)
# let's do some modifications to meas_date
raw = mne.io.read_raw_fif(raw_fname)
raw.anonymize()
data_path2 = _TempDir()
raw_fname2 = op.join(data_path2, 'sample_audvis_raw.fif')
raw.save(raw_fname2)
raw_to_bids(subject_id=subject_id,
run=run,
task=task,
session_id=session_id,
raw_file=raw_fname2,
events_data=events_fname,
output_path=output_path,
event_id=event_id,
acquisition=acq_b,
overwrite=True)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
def test_ctf():
"""
Testing the functionality of the raw_to_bids conversion for CTF data.
"""
output_path = _TempDir()
data_path = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(data_path, 'testdata_ctf.ds')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_fname=raw_fname,
output_path=output_path,
overwrite=True)
def test_cnt():
"""Test raw_to_bids conversion for Neuroscan data."""
output_path = _TempDir()
data_path = op.join(testing.data_path(), 'CNT')
raw_fname = op.join(data_path, 'scan41_short.cnt')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False,
kind='eeg')
cmd = ['bids-validator', '--bep006', output_path]
run_subprocess(cmd, shell=shell)
def test_bdf():
"""Test raw_to_bids conversion for Biosemi data."""
output_path = _TempDir()
data_path = op.join(base_path, 'edf', 'tests', 'data')
raw_fname = op.join(data_path, 'test.bdf')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False,
kind='eeg')
cmd = ['bids-validator', '--bep006', output_path]
run_subprocess(cmd, shell=shell)
def test_bti():
"""
Testing the functionality of the raw_to_bids conversion for BTi data.
"""
output_path = _TempDir()
data_path = op.join(base_path, 'bti', 'tests', 'data')
raw_fname = op.join(data_path, 'test_pdf_linux')
config_fname = op.join(data_path, 'test_config_linux')
headshape_fname = op.join(data_path, 'test_hs_linux')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
raw_fname=raw_fname,
config=config_fname,
hsp=headshape_fname,
output_path=output_path,
overwrite=True)
def test_bti():
"""Test functionality of the raw_to_bids conversion for BTi data."""
output_path = _TempDir()
data_path = op.join(base_path, 'bti', 'tests', 'data')
raw_fname = op.join(data_path, 'test_pdf_linux')
config_fname = op.join(data_path, 'test_config_linux')
headshape_fname = op.join(data_path, 'test_hs_linux')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
config=config_fname,
hsp=headshape_fname,
output_path=output_path,
verbose=True,
overwrite=False)
assert op.exists(op.join(output_path, 'participants.tsv'))
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
def test_set():
"""Test raw_to_bids conversion for EEGLAB data."""
# standalone .set file
output_path = _TempDir()
data_path = op.join(testing.data_path(), 'EEGLAB')
raw_fname = op.join(data_path, 'test_raw_onefile.set')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False,
kind='eeg')
cmd = ['bids-validator', '--bep006', output_path]
run_subprocess(cmd, shell=shell)
with pytest.raises(OSError, match="already exists"):
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False,
kind='eeg')
# .set with associated .fdt
output_path = _TempDir()
data_path = op.join(testing.data_path(), 'EEGLAB')
raw_fname = op.join(data_path, 'test_raw.set')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
output_path=output_path,
overwrite=False,
kind='eeg')
cmd = ['bids-validator', '--bep006', output_path]
run_subprocess(cmd, shell=shell)
data_path = sample.data_path()
event_id = {
'Auditory/Left': 1,
'Auditory/Right': 2,
'Visual/Left': 3,
'Visual/Right': 4,
'Smiley': 5,
'Button': 32
}
raw_file = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
events_data = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw-eve.fif')
output_path = op.join(data_path, '..', 'MNE-sample-data-bids')
###############################################################################
# Finally, we specify the raw_file and events_data
raw_to_bids(subject_id='01',
run='01',
session_id='01',
task='audiovisual',
raw_file=raw_file,
events_data=events_data,
output_path=output_path,
event_id=event_id,
overwrite=False)
###############################################################################
# Now let's see the structure of the BIDS folder we created.
print_dir_tree(output_path)
# Alexandre Gramfort <*****@*****.**>
# Teon Brooks <*****@*****.**>
# License: BSD (3-clause)
###############################################################################
# Let us import mne_bids
import os.path as op
from mne.datasets import sample
from mne_bids import raw_to_bids
###############################################################################
# And define the paths and event_id dictionary.
data_path = sample.data_path()
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2, 'Visual/Left': 3,
'Visual/Right': 4, 'Smiley': 5, 'Button': 32}
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
events_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw-eve.fif')
output_path = op.join(data_path, '..', 'MNE-sample-data-bids')
###############################################################################
# Finally, we specify the raw_fname and events_fname
raw_to_bids(subject_id='01', run='01', session_id='01', task='audiovisual',
raw_fname=raw_fname, events_fname=events_fname,
output_path=output_path, event_id=event_id,
overwrite=True)
def run():
"""Run command."""
from mne.commands.utils import get_optparser
parser = get_optparser(__file__)
parser.set_defaults(kind='meg')
parser.add_option('--subject_id',
dest='subject_id',
help=('The subject name in BIDS compatible format',
'(01,02, etc.)'),
metavar='s')
parser.add_option('--task',
dest='task',
help='Name of the task the data is based on.',
metavar='t')
parser.add_option('--raw_file',
dest='raw_file',
help='The path to the raw MEG file.',
metavar='r')
parser.add_option('--output_path',
dest='output_path',
help='The path of the BIDS compatible folder.',
metavar='o')
parser.add_option('--session_id',
dest='session_id',
help='The session name in BIDS compatible format.',
metavar='ses')
parser.add_option('--run',
dest='run',
help='The run number for this dataset.',
metavar='run')
parser.add_option('--kind',
dest='kind',
help='The kind of data being converted.',
metavar='k')
parser.add_option('--events_data',
dest='events_data',
help='The events file.',
metavar='evt')
parser.add_option('--event_id',
dest='event_id',
help='The event id dict.',
metavar='eid')
parser.add_option('--hpi',
dest='hpi',
help='The path to the MEG Marker points',
metavar='filename')
parser.add_option('--electrode',
dest='electrode',
help='The path to head-native Digitizer points',
metavar='elec')
parser.add_option('--hsp',
dest='hsp',
help='The path to headshape points.',
metavar='hsp')
parser.add_option('--config',
dest='config',
help='The path to the configuration file',
metavar='cfg')
parser.add_option('--overwrite',
dest='overwrite',
help=("Boolean. Whether to overwrite existing data"),
metavar='ow')
opt, args = parser.parse_args()
raw_to_bids(subject_id=opt.subject_id,
task=opt.task,
raw_file=opt.raw_file,
output_path=opt.output_path,
session_id=opt.session_id,
run=opt.run,
kind=opt.kind,
events_data=opt.events_data,
event_id=opt.event_id,
hpi=opt.hpi,
electrode=opt.electrode,
hsp=opt.hsp,
config=opt.config,
overwrite=opt.overwrite,
verbose=True)
event_id = {
'face/famous/first': 5,
'face/famous/immediate': 6,
'face/famous/long': 7,
'face/unfamiliar/first': 13,
'face/unfamiliar/immediate': 14,
'face/unfamiliar/long': 15,
'scrambled/first': 17,
'scrambled/immediate': 18,
'scrambled/long': 19,
}
###############################################################################
# Let us loop over the subjects and create BIDS-compatible folder
for subject_id in subject_ids:
subject = 'sub%03d' % subject_id
for run in runs:
raw_file = op.join(data_path, repo, subject, 'MEG',
'run_%02d_raw.fif' % run)
# Make it BIDS compatible
raw_to_bids(subject_id='%02d' % subject_id,
session_id='01',
run=run,
task='VisualFaces',
raw_file=raw_file,
event_id=event_id,
output_path=output_path,
overwrite=True)
event_id = {
'face/famous/first': 5,
'face/famous/immediate': 6,
'face/famous/long': 7,
'face/unfamiliar/first': 13,
'face/unfamiliar/immediate': 14,
'face/unfamiliar/long': 15,
'scrambled/first': 17,
'scrambled/immediate': 18,
'scrambled/long': 19,
}
###############################################################################
# Let us loop over the subjects and create BIDS-compatible folder
for subject_id in subject_ids:
subject = 'sub%03d' % subject_id
for run in runs:
raw_fname = op.join(data_path, repo, subject, 'MEG',
'run_%02d_raw.fif' % run)
# Make it BIDS compatible
raw_to_bids(subject_id='%02d' % subject_id,
run=run,
task='visual_faces',
raw_fname=raw_fname,
event_id=event_id,
output_path=output_path,
overwrite=True)
def test_kit():
"""Test functionality of the raw_to_bids conversion for KIT data."""
output_path = _TempDir()
data_path = op.join(base_path, 'kit', 'tests', 'data')
raw_fname = op.join(data_path, 'test.sqd')
events_fname = op.join(data_path, 'test-eve.txt')
hpi_fname = op.join(data_path, 'test_mrk.sqd')
electrode_fname = op.join(data_path, 'test_elp.txt')
headshape_fname = op.join(data_path, 'test_hsp.txt')
event_id = dict(cond=1)
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
events_data=events_fname,
event_id=event_id,
hpi=hpi_fname,
electrode=electrode_fname,
hsp=headshape_fname,
output_path=output_path,
overwrite=False)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
assert op.exists(op.join(output_path, 'participants.tsv'))
# ensure the channels file has no STI 014 channel:
channels_tsv = make_bids_filename(subject=subject_id,
session=session_id,
task=task,
run=run,
suffix='channels.tsv',
acquisition=acq,
prefix=op.join(output_path,
'sub-01/ses-01/meg'))
df = pd.read_csv(channels_tsv, sep='\t')
assert not ('STI 014' in df['name'].values)
# ensure the marker file is produced in the right place
raw_folder = make_bids_filename(subject=subject_id,
session=session_id,
task=task,
run=run,
acquisition=acq,
suffix='%s' % 'meg')
marker_fname = make_bids_filename(subject=subject_id,
session=session_id,
task=task,
run=run,
acquisition=acq,
suffix='markers.sqd',
prefix=os.path.join(
output_path, 'sub-01/ses-01/meg',
raw_folder))
assert op.exists(marker_fname)
# check for error if there are multiple marker coils specified
with pytest.raises(ValueError):
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
task=task,
acquisition=acq,
raw_file=raw_fname,
events_data=events_fname,
event_id=event_id,
hpi=[hpi_fname, hpi_fname],
electrode=electrode_fname,
hsp=headshape_fname,
output_path=output_path,
overwrite=True)
###############################################################################
# Now we just need to specify a few more EEG details to get something sensible:
# First, tell `MNE-BIDS` that it is working with EEG data:
kind = 'eeg'
# Brief description of the event markers present in the data. This will become
# the `trial_type` column in our BIDS `events.tsv`. We know about the event
# meaning from the documentation on PhysioBank
trial_type = {'rest': 0, 'imagine left fist': 1, 'imagine right fist': 2}
# Now convert our data to be in a new BIDS dataset.
raw_to_bids(subject_id=subject_id,
task=task,
raw_file=raw_file,
output_path=output_path,
kind=kind,
event_id=trial_type,
overwrite=False)
###############################################################################
# What does our fresh BIDS directory look like?
print_dir_tree(output_path)
###############################################################################
# Looks good so far, let's convert the data for all tasks and subjects.
# Start with a clean directory
sh.rmtree(output_path)
# Some initial information that we found in the PhysioBank documentation
def test_fif():
"""Test functionality of the raw_to_bids conversion for Neuromag data."""
output_path = _TempDir()
data_path = testing.data_path()
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
event_id = {
'Auditory/Left': 1,
'Auditory/Right': 2,
'Visual/Left': 3,
'Visual/Right': 4,
'Smiley': 5,
'Button': 32
}
events_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw-eve.fif')
raw_to_bids(subject_id=subject_id,
session_id=session_id,
run=run,
acquisition=acq,
task=task,
raw_file=raw_fname,
events_data=events_fname,
output_path=output_path,
event_id=event_id,
overwrite=False)
# give the raw object some fake participant data
raw = mne.io.read_raw_fif(raw_fname)
raw.anonymize()
raw.info['subject_info'] = {
'his_id': subject_id2,
'birthday': (1994, 1, 26),
'sex': 1
}
data_path2 = _TempDir()
raw_fname2 = op.join(data_path2, 'sample_audvis_raw.fif')
raw.save(raw_fname2)
raw_to_bids(subject_id=subject_id2,
run=run,
task=task,
acquisition=acq,
session_id=session_id,
raw_file=raw_fname2,
events_data=events_fname,
output_path=output_path,
event_id=event_id,
overwrite=False)
# check that the overwrite parameters work correctly for the participant
# data
# change the gender but don't force overwrite.
raw.info['subject_info'] = {
'his_id': subject_id2,
'birthday': (1994, 1, 26),
'sex': 2
}
with pytest.raises(OSError, match="already exists"):
raw_to_bids(subject_id=subject_id2,
run=run,
task=task,
acquisition=acq,
session_id=session_id,
raw_file=raw,
events_data=events_fname,
output_path=output_path,
event_id=event_id,
overwrite=False)
# now force the overwrite
raw_to_bids(subject_id=subject_id2,
run=run,
task=task,
acquisition=acq,
session_id=session_id,
raw_file=raw,
events_data=events_fname,
output_path=output_path,
event_id=event_id,
overwrite=True)
cmd = ['bids-validator', output_path]
run_subprocess(cmd, shell=shell)
assert op.exists(op.join(output_path, 'participants.tsv'))
