def test_count_events_bids_path(tmpdir):
"""Test the event counts passing a BIDSPath."""
root, events, event_id = \
_make_dataset(tmpdir, subjects=['01', '02'], tasks=['task1'])
with pytest.raises(ValueError, match='datatype .*anat.* is not supported'):
bids_path = BIDSPath(root=root, subject='01', datatype='anat')
count_events(bids_path)
bids_path = BIDSPath(root=root, subject='01', datatype='meg')
counts = count_events(bids_path)
_check_counts(counts, events, event_id, subjects=['01'], tasks=['task1'])
def test_count_no_events_file(tmpdir):
"""Test count_events with no event present."""
data_path = testing.data_path()
raw_fname = \
Path(data_path) / 'MEG' / 'sample' / 'sample_audvis_trunc_raw.fif'
raw = mne.io.read_raw(raw_fname)
raw.info['line_freq'] = 60.
root = str(tmpdir)
bids_path = BIDSPath(
subject='01', task='task1', root=root,
)
write_raw_bids(raw, bids_path, overwrite=True, verbose=False)
with pytest.raises(ValueError, match='No events files found.'):
count_events(root)
def add_event_counts(*, cfg, session: str, report: mne.Report) -> None:
try:
df_events = count_events(BIDSPath(root=cfg.bids_root, session=session))
except ValueError:
logger.warning('Could not read events.')
df_events = None
if df_events is not None:
css_classes = ('table', 'table-striped', 'table-borderless',
'table-hover')
report.add_htmls_to_section(
f'<div class="event-counts">\n'
f'{df_events.to_html(classes=css_classes, border=0)}\n'
f'</div>',
captions='Event counts',
section='events')
css = ('.event-counts {\n'
' display: -webkit-box;\n'
' display: -ms-flexbox;\n'
' display: -webkit-flex;\n'
' display: flex;\n'
' justify-content: center;\n'
' text-align: center;\n'
'}\n\n'
'th, td {\n'
' text-align: center;\n'
'}\n')
report.add_custom_css(css)
def run():
"""Run the raw_to_bids command."""
import pandas as pd
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.')
parser.add_option('--datatype', dest='datatype', default='auto',
help='The datatype to consider.')
parser.add_option('--describe', dest='describe', action="store_true",
help=('If set print the descriptive statistics '
'(min, max, etc.).'))
parser.add_option('--output', dest='output', default=None,
help='Path to the CSV file where to store the results.')
parser.add_option('--overwrite', dest='overwrite', action='store_true',
help='If set, overwrite an existing output file.')
parser.add_option('--silent', dest='silent', action='store_true',
help='Whether to print the event counts on the screen.')
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.')
if opt.output and Path(opt.output).exists() and not opt.overwrite:
parser.error('Output file exists. To overwrite, pass --overwrite')
counts = count_events(opt.bids_root, datatype=opt.datatype)
if opt.describe:
counts = counts.describe()
if not opt.silent:
with pd.option_context(
'display.max_rows', 1000,
'display.max_columns', 80,
'display.width', 1000
):
print(counts)
if opt.output:
counts.to_csv(opt.output)
print(f'\nOutput stored in {opt.output}')
def test_count_events(tmpdir, subjects, tasks, runs, sessions):
"""Test the event counts."""
root, events, event_id = _make_dataset(tmpdir, subjects, tasks, runs,
sessions)
counts = count_events(root)
_check_counts(counts, events, event_id, subjects, tasks, runs, sessions)
def test_count_no_events_column(tmpdir):
"""Test case where events.tsv doesn't contain [stim,trial]_type column."""
subject, task, run, session, datatype = '01', 'task1', '01', '01', 'meg'
root, events, event_id = _make_dataset(tmpdir, [subject], [task], [run],
[session])
# Delete the `stim_type` column.
events_tsv_fpath = BIDSPath(root=root, subject=subject, task=task, run=run,
session=session, datatype=datatype,
suffix='events', extension='.tsv').fpath
events_tsv = _from_tsv(events_tsv_fpath)
events_tsv['stim_type'] = events_tsv['trial_type']
del events_tsv['trial_type']
_write_tsv(fname=events_tsv_fpath, dictionary=events_tsv, overwrite=True)
counts = count_events(root)
_check_counts(counts, events, event_id, [subject], [task], [run],
[session])
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.')
parser.add_option('--datatype',
dest='datatype',
default='auto',
help='The datatype to consider.')
parser.add_option('--describe',
dest='describe',
action="store_true",
help=('If set print the descriptive statistics '
'(min, max, etc.).'))
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.')
counts = count_events(opt.bids_root, datatype=opt.datatype)
if opt.describe:
counts = counts.describe()
print(counts)
print(raw.annotations) # %% # Finally, let's write the BIDS data! bids_path = BIDSPath(subject=subject_id, task=task, root=bids_root) write_raw_bids(raw, bids_path, overwrite=True) # %% # What does our fresh BIDS directory look like? print_dir_tree(bids_root) # %% # Finally let's get an overview of the events on the whole dataset counts = count_events(bids_root) counts # %% # We can see that MNE-BIDS wrote several important files related to subject 1 # for us: # # * ``optodes.tsv`` containing the optode coordinates and # ``coordsystem.json``, which contains the metadata about the optode # coordinates. # * The actual SNIRF data file (with a proper BIDS name) and an accompanying # ``*_nirs.json`` file that contains metadata about the NIRS recording. # * The ``*scans.json`` file lists all data recordings with their acquisition # date. This file becomes more handy once there are multiple sessions and # recordings to keep track of. # * And finally, ``channels.tsv`` and ``events.tsv`` which contain even further
cal_fname = op.join(data_path, 'SSS', 'sss_cal_mgh.dat') ct_fname = op.join(data_path, 'SSS', 'ct_sparse_mgh.fif') write_meg_calibration(cal_fname, bids_path) write_meg_crosstalk(ct_fname, bids_path) ############################################################################### # Now let's see the structure of the BIDS folder we created. print_dir_tree(output_path) ############################################################################### # Now let's get an overview of the events on the whole dataset counts = count_events(output_path) counts ############################################################################### # A big advantage of having data organized according to BIDS is that software # packages can automate your workflow. For example, reading the data back # into MNE-Python can easily be done using :func:`read_raw_bids`. raw = read_raw_bids(bids_path=bids_path) ############################################################################### # The resulting data is already in a convenient form to create epochs and # evoked data. events, event_id = mne.events_from_annotations(raw) epochs = mne.Epochs(raw, events, event_id)
