def test_report_no_participant_information(tmp_path):
"""Test report with participants.tsv with participant_id column only."""
bids_root = tmp_path
raw = mne.io.read_raw_fif(raw_fname, verbose=False)
raw.info['line_freq'] = 60
bids_path = _bids_path.copy().update(root=bids_root)
write_raw_bids(raw, bids_path, overwrite=True, verbose=False)
# remove all information and check if report still runs
(bids_root / 'participants.json').unlink()
# overwrite participant information to see if report still runs
(bids_root / 'participants.tsv').write_text('participant_id\nsub-001')
report = make_report(bids_root)
expected_report = \
f"""This dataset was created by [Unspecified] and conforms to BIDS version {BIDS_VERSION}.
This report was generated with MNE-BIDS (https://doi.org/10.21105/joss.01896).
The dataset consists of 1 participants (sex were all unknown; handedness were
all unknown; ages all unknown) and 1 recording sessions: 01. Data was recorded
using an MEG system (Elekta) sampled at 300.31 Hz with line noise at
60.0 Hz. The following software filters were applied during recording:
SpatialCompensation. There was 1 scan in total. Recording durations ranged from
20.0 to 20.0 seconds (mean = 20.0, std = 0.0), for a total of 20.0 seconds of
data recorded over all scans. For each dataset, there were on average 376.0 (std
= 0.0) recording channels per scan, out of which 374.0 (std = 0.0) were used in
analysis (2.0 +/- 0.0 were removed from analysis).""" # noqa
expected_report = '\n'.join(textwrap.wrap(expected_report, width=80))
assert report == expected_report
def run():
"""Run the raw_to_bids command."""
from mne.commands.utils import get_optparser
parser = get_optparser(__file__,
usage="usage: %prog options args",
prog_prefix='mne_bids',
version=mne_bids.__version__)
parser.add_option('--bids_root',
dest='bids_root',
help='The path of the BIDS compatible folder.')
opt, args = parser.parse_args()
if len(args) > 0:
parser.print_help()
parser.error(
'Do not specify arguments without flags. Found: "{}".\n'.format(
args))
if not all([opt.bids_root]):
parser.print_help()
parser.error('Arguments missing. You need to specify the '
'--bids_root parameter.')
report = make_report(opt.bids_root)
print('-' * 36 + ' REPORT ' + '-' * 36)
print(report)
assert ' ' not in report
def test_report():
"""Test that report generated works as intended."""
bids_root = _TempDir()
raw = mne.io.read_raw_fif(raw_fname, verbose=False)
raw.info['line_freq'] = 60
bids_path.update(root=bids_root)
write_raw_bids(raw, bids_path, overwrite=True, verbose=False)
report = make_report(bids_root)
print(report)
expected_report = \
"""This dataset was created with BIDS version 1.4.0 by Please cite MNE-BIDS in your
publication before removing this (citations in README). This report was
generated with MNE-BIDS (https://doi.org/10.21105/joss.01896). The dataset
consists of 1 participants (sex were all unknown; handedness were all unknown;
ages all unknown)and 1 recording sessions: 01. Data was recorded using a MEG
system (Elekta manufacturer) sampled at 300.31 Hz with line noise at 60 Hz.
There was 1 scan in total. Recording durations ranged from 20.0 to 20.0 seconds
(mean = 20.0, std = 0.0), for a total of 20.0 seconds of data recorded over all
scans. For each dataset, there were on average 376.0 (std = 0.0) recording
channels per scan, out of which 374.0 (std = 0.0) were used in analysis (2.0 +/-
0.0 were removed from analysis).""" # noqa
assert report == expected_report
def test_report(tmpdir):
"""Test that report generated works as intended."""
bids_root = str(tmpdir)
raw = mne.io.read_raw_fif(raw_fname, verbose=False)
raw.info['line_freq'] = 60
bids_path.update(root=bids_root)
write_raw_bids(raw, bids_path, overwrite=True, verbose=False)
report = make_report(bids_root)
expected_report = \
f"""This dataset was created by [Unspecified] and conforms to BIDS version {BIDS_VERSION}.
This report was generated with MNE-BIDS (https://doi.org/10.21105/joss.01896).
The dataset consists of 1 participants (sex were all unknown; handedness were
all unknown; ages all unknown) and 1 recording sessions: 01. Data was recorded
using a MEG system (Elekta manufacturer) sampled at 300.31 Hz with line noise at
60.0 Hz. The following software filters were applied during recording:
SpatialCompensation. There was 1 scan in total. Recording durations ranged from
20.0 to 20.0 seconds (mean = 20.0, std = 0.0), for a total of 20.0 seconds of
data recorded over all scans. For each dataset, there were on average 376.0 (std
= 0.0) recording channels per scan, out of which 374.0 (std = 0.0) were used in
analysis (2.0 +/- 0.0 were removed from analysis).""" # noqa
assert report == expected_report
# BIDS dataset. bids_root = somato.data_path() ############################################################################### # Explore the dataset contents # ---------------------------- # # We can use MNE-BIDS to print a tree of all # included files and folders. We pass the ``max_depth`` parameter to # `mne_bids.print_dir_tree` to the output to three levels of folders, for # better readability in this example. print_dir_tree(bids_root, max_depth=3) # We can generate a report of the existing dataset print(make_report(bids_root)) ############################################################################### # Update the sidecar JSON dataset contents # ---------------------------------------- # # We can use MNE-BIDS to update all sidecar files for a matching # ``BIDSPath`` object. We then pass in a dictionary (or JSON file) to update # all matching metadata fields within the BIDS dataset. # create a BIDSPath object bids_root = somato.data_path() datatype = 'meg' subject = '01' task = 'somato' suffix = 'meg'
raw = mne.io.read_raw_ctf(raw_fname) #Change this - should be generic for meg vender
raw.info['line_freq'] = line_freq
sub = "{0:0=4d}".format(idx)
ses = '01'
task = 'rest'
run = '01'
bids_path = BIDSPath(subject=sub, session=ses, task=task,
run=run, root=output_path)
write_raw_bids(raw, bids_path)
report = make_report(bids_dir)
# def check_inputs(bids_root=None, line_freq=None, meg_data_dir=None,
# subjects_dir=None, subjids='all'):
# '''Glob all impotant directories and determine the missing inputs'''
# if subjids=='all':
# print('''Parsing all meg studies can take a long time depending on the
# number of datasets in the search''')
# all_meg_datasets = glob.glob(meg_glob)
# all_mri_datasets = glob.glob(mri_glob)
raw = mne.io.read_raw_edf(raw_fname)
raw.info['line_freq'] = 50 # specify power line frequency
raw_list.append(raw)
bids_path = BIDSPath(subject=f'{subject_id:03}',
session='01', task='MotorImagery',
run=f'{run_map[run]:02}',
root=bids_root)
bids_list.append(bids_path)
daysback_min, daysback_max = get_anonymization_daysback(raw_list)
for raw, bids_path in zip(raw_list, bids_list):
# By using the same anonymization `daysback` number we can
# preserve the longitudinal structure of multiple sessions for a
# single subject and the relation between subjects. Be sure to
# change or delete this number before putting code online, you
# wouldn't want to inadvertently de-anonymize your data.
write_raw_bids(raw, bids_path, event_id=event_id,
anonymize=dict(daysback=daysback_min + 2117),
overwrite=True)
###############################################################################
# Now let's see the structure of the BIDS folder we created.
print_dir_tree(bids_root)
###############################################################################
# Now let's generate a report on the dataset.
dataset_report = make_report(root=bids_root)
print(dataset_report)
# root of BIDs dataset
root = Path('../data/')
# BIDS entities
subject = '01'
session = 'interictalsleep'
run = '01'
datatype = 'ieeg'
###############################################################################
# show the contents of the BIDS dataset
print_dir_tree(root)
# Let's summarize the dataset.
print(make_report(root, verbose=False))
###############################################################################
# Load the dataset.
bids_path = BIDSPath(subject=subject,
session=session,
run=run,
datatype=datatype,
root=root,
suffix='ieeg',
extension='.vhdr')
raw = read_raw_bids(bids_path)
###############################################################################
# Let's plot the data and see what it looks like
# raw.plot()
