def _save_bads(*, bads, bids_path, verbose):
"""Update the set of channels marked as bad."""
channels_tsv_fname = bids_path.copy().update(suffix='channels',
extension='.tsv')
channels_tsv_data = _from_tsv(channels_tsv_fname)
descriptions = []
for ch_name in bads:
idx = channels_tsv_data['name'].index(ch_name)
if channels_tsv_data['status'][idx] == 'bad':
# Channel was already marked as bad, retain existing description.
description = channels_tsv_data['status_description'][idx]
else:
# Channel has been manually marked as bad during inspection, assign
# default description.
description = 'Manual inspection via MNE-BIDS'
descriptions.append(description)
# We pass overwrite=True, causing all channels not passed as bad here to
# be marked as good.
mark_bad_channels(ch_names=bads,
descriptions=descriptions,
bids_path=bids_path,
overwrite=True,
verbose=verbose)
def _handle_events_reading_core(events_fname, raw):
"""Read associated events.tsv and populate raw.
Handle onset, duration, and description of each event.
"""
events_dict = _from_tsv(events_fname)
if ('value' in events_dict) and ('trial_type' in events_dict):
events_dict = _drop(events_dict, 'n/a', 'trial_type')
events_dict = _drop(events_dict, 'n/a', 'value')
descriptions = np.asarray([
a + ':' + b
for a, b in zip(events_dict["trial_type"], events_dict["value"])
],
dtype=str)
# Get the descriptions of the events
elif 'trial_type' in events_dict:
# Drop events unrelated to a trial type
events_dict = _drop(events_dict, 'n/a', 'trial_type')
descriptions = np.asarray(events_dict['trial_type'], dtype=str)
# If we don't have a proper description of the events, perhaps we have
# at least an event value?
elif 'value' in events_dict:
# Drop events unrelated to value
events_dict = _drop(events_dict, 'n/a', 'value')
descriptions = np.asarray(events_dict['value'], dtype=str)
# Worst case, we go with 'n/a' for all events
else:
descriptions = 'n/a'
# Deal with "n/a" strings before converting to float
ons = [np.nan if on == 'n/a' else on for on in events_dict['onset']]
dus = [0 if du == 'n/a' else du for du in events_dict['duration']]
onsets = np.asarray(ons, dtype=float)
durations = np.asarray(dus, dtype=float)
# Keep only events where onset is known
good_events_idx = ~np.isnan(onsets)
onsets = onsets[good_events_idx]
durations = durations[good_events_idx]
descriptions = descriptions[good_events_idx]
del good_events_idx
# Add Events to raw as annotations
annot_from_events = mne.Annotations(onset=onsets,
duration=durations,
description=descriptions,
orig_time=None)
raw.set_annotations(annot_from_events)
return raw
def test_inspect_auto_flats(tmp_path, save_changes):
"""Test flat channel & segment detection."""
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
plt.close('all')
bids_root = setup_bids_test_dir(tmp_path)
bids_path = _bids_path.copy().update(root=bids_root)
channels_tsv_fname = bids_path.copy().update(suffix='channels',
extension='.tsv')
raw = read_raw_bids(bids_path=bids_path, verbose='error')
# Inject an entirely flat channel.
raw.load_data()
raw._data[10] = np.zeros_like(raw._data[10], dtype=raw._data.dtype)
# Add a a flat time segment (approx. 100 ms) to another channel
raw._data[20, 500:500 + int(np.ceil(0.1 * raw.info['sfreq']))] = 0
raw.save(raw.filenames[0], overwrite=True)
old_bads = raw.info['bads'].copy()
inspect_dataset(bids_path)
raw_fig = mne_bids.inspect._global_vars['raw_fig']
# Closing the window should open a dialog box.
raw_fig.canvas.key_press_event(raw_fig.mne.close_key)
fig_dialog = mne_bids.inspect._global_vars['dialog_fig']
if save_changes:
key = 'return'
else:
key = 'escape'
fig_dialog.canvas.key_press_event(key)
raw = read_raw_bids(bids_path=bids_path, verbose='error')
if save_changes:
assert old_bads != raw.info['bads']
assert raw.ch_names[10] in raw.info['bads']
channels_tsv_data = _from_tsv(channels_tsv_fname)
assert (channels_tsv_data['status_description'][10] ==
'Flat channel, auto-detected via MNE-BIDS')
# This channel should not have been added to `bads`, but produced a
# flat annotation.
assert raw.ch_names[20] not in raw.info['bads']
assert 'BAD_flat' in raw.annotations.description
else:
assert old_bads == raw.info['bads']
assert 'BAD_flat' not in raw.annotations.description
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 _save_raw_if_changed(*, old_bads, new_bads, flat_chans, old_annotations,
new_annotations, bids_path, verbose):
"""Save bad channel selection if it has been changed.
Parameters
----------
old_bads : list
The original bad channels.
new_bads : list
The updated set of bad channels (i.e. **all** of them, not only the
changed ones).
flat_chans : list
The auto-detected flat channels. This is either an empty list or a
subset of ``new_bads``.
old_annotations : mne.Annotations
The original Annotations.
new_annotations : mne.Annotations
The new Annotations.
"""
assert set(flat_chans).issubset(set(new_bads))
if set(old_bads) == set(new_bads):
bads = None
bad_descriptions = []
else:
bads = new_bads
bad_descriptions = []
# Generate entries for the `status_description` column.
channels_tsv_fname = bids_path.copy().update(suffix='channels',
extension='.tsv')
channels_tsv_data = _from_tsv(channels_tsv_fname)
for ch_name in bads:
idx = channels_tsv_data['name'].index(ch_name)
if channels_tsv_data['status'][idx] == 'bad':
# Channel was already marked as bad in the data, so retain
# existing description.
description = channels_tsv_data['status_description'][idx]
elif ch_name in flat_chans:
description = 'Flat channel, auto-detected via MNE-BIDS'
else:
# Channel has been manually marked as bad during inspection
description = 'Interactive inspection via MNE-BIDS'
bad_descriptions.append(description)
del ch_name, description
del channels_tsv_data, channels_tsv_fname,
if _annotations_almost_equal(old_annotations, new_annotations):
annotations = None
else:
annotations = new_annotations
if bads is None and annotations is None:
# Nothing has changed, so we can just exit.
return None
return _save_raw_dialog_box(bads=bads,
bad_descriptions=bad_descriptions,
annotations=annotations,
bids_path=bids_path,
verbose=verbose)
