def test_flat_acq_skip():
"""Test that acquisition skips are handled properly."""
raw = read_raw_fif(skip_fname).load_data()
annot, bads = annotate_flat(raw)
assert len(annot) == 0
assert bads == [ # MaxFilter finds the same 21 channels
'MEG%04d' % (int(num),) for num in
'141 331 421 431 611 641 1011 1021 1031 1241 1421 '
'1741 1841 2011 2131 2141 2241 2531 2541 2611 2621'.split()]
def test_annotate_flat(first_samp):
"""Test marking flat segments."""
# Test if ECG analysis will work on data that is not preloaded
n_ch, n_times = 11, 1000
data = np.random.RandomState(0).randn(n_ch, n_times)
assert not (np.diff(data, axis=-1) == 0).any() # nothing flat at first
info = create_info(n_ch, 1000., 'eeg')
info['meas_date'] = (1, 2)
# test first_samp != for gh-6295
raw = RawArray(data, info, first_samp=first_samp)
raw.info['bads'] = [raw.ch_names[-1]]
#
# First make a channel flat the whole time
#
raw_0 = raw.copy()
raw_0._data[0] = 0.
for kwargs, bads, want_times in [
# Anything < 1 will mark spatially
(dict(bad_percent=100.), [], 0),
(dict(bad_percent=99.9), [raw.ch_names[0]], n_times),
(dict(), [raw.ch_names[0]], n_times)
]: # default (1)
raw_time = raw_0.copy()
annot, got_bads = annotate_flat(raw_0, verbose='debug', **kwargs)
assert got_bads == bads
raw_time.set_annotations(raw_time.annotations + annot)
raw_time.info['bads'] += got_bads
n_good_times = raw_time.get_data(reject_by_annotation='omit').shape[1]
assert n_good_times == want_times
with pytest.deprecated_call(match='annotate_flat'):
raw_time_2 = mark_flat(raw_0.copy(), verbose='debug', **kwargs)
_assert_annotations_equal(raw_time_2.annotations, raw_time.annotations)
assert raw_time_2.info['bads'] == raw_time.info['bads']
#
# Now make a channel flat for 20% of the time points
#
raw_0 = raw.copy()
n_good_times = int(round(0.8 * n_times))
raw_0._data[0, n_good_times:] = 0.
threshold = 100 * (n_times - n_good_times) / n_times
for kwargs, bads, want_times in [
# Should change behavior at bad_percent=20
(dict(bad_percent=100), [], n_good_times),
(dict(bad_percent=threshold), [], n_good_times),
(dict(bad_percent=threshold - 1e-5), [raw.ch_names[0]], n_times),
(dict(), [raw.ch_names[0]], n_times)
]:
annot, got_bads = annotate_flat(raw_0, verbose='debug', **kwargs)
assert got_bads == bads
raw_time = raw_0.copy()
raw_time.set_annotations(raw_time.annotations + annot)
raw_time.info['bads'] += got_bads
n_good_times = raw_time.get_data(reject_by_annotation='omit').shape[1]
assert n_good_times == want_times
with pytest.raises(TypeError, match='must be an instance of BaseRaw'):
annotate_flat(0.)
with pytest.raises(ValueError, match='not convert string to float'):
annotate_flat(raw, 'x')
def _inspect_raw(*,
bids_path,
l_freq,
h_freq,
find_flat,
show_annotations,
verbose=None):
"""Raw data inspection."""
# Delay the import
import matplotlib
import matplotlib.pyplot as plt
extra_params = dict()
if bids_path.extension == '.fif':
extra_params['allow_maxshield'] = True
raw = read_raw_bids(bids_path, extra_params=extra_params, verbose='error')
old_bads = raw.info['bads'].copy()
old_annotations = raw.annotations.copy()
if find_flat:
raw.load_data() # Speeds up processing dramatically
flat_annot, flat_chans = annotate_flat(raw=raw,
min_duration=0.05,
verbose=verbose)
new_annot = raw.annotations + flat_annot
raw.set_annotations(new_annot)
raw.info['bads'] = list(set(raw.info['bads'] + flat_chans))
del new_annot, flat_annot
else:
flat_chans = []
fig = raw.plot(title=f'{bids_path.root.name}: {bids_path.basename}',
highpass=l_freq,
lowpass=h_freq,
show_options=True,
block=False,
show=False,
verbose='warning')
# Add our own event handlers so that when the MNE Raw Browser is being
# closed, our dialog box will pop up, asking whether to save changes.
def _handle_close(event):
mne_raw_fig = event.canvas.figure
# Bads alterations are only transferred to `inst` once the figure is
# closed; Annotation changes are immediately reflected in `inst`
new_bads = mne_raw_fig.mne.info['bads'].copy()
new_annotations = mne_raw_fig.mne.inst.annotations.copy()
if not new_annotations:
# Ensure it's not an empty list, but an empty set of Annotations.
new_annotations = mne.Annotations(
onset=[],
duration=[],
description=[],
orig_time=mne_raw_fig.mne.info['meas_date'])
_save_raw_if_changed(old_bads=old_bads,
new_bads=new_bads,
flat_chans=flat_chans,
old_annotations=old_annotations,
new_annotations=new_annotations,
bids_path=bids_path,
verbose=verbose)
_global_vars['raw_fig'] = None
def _keypress_callback(event):
if event.key == _global_vars['mne_close_key']:
_handle_close(event)
fig.canvas.mpl_connect('close_event', _handle_close)
fig.canvas.mpl_connect('key_press_event', _keypress_callback)
if not show_annotations:
# Remove annotations and kill `_toggle_annotation_fig` method, since
# we cannot directly and easily remove the associated `a` keyboard
# event callback.
fig._clear_annotations()
fig._toggle_annotation_fig = lambda: None
# Ensure it's not an empty list, but an empty set of Annotations.
old_annotations = mne.Annotations(onset=[],
duration=[],
description=[],
orig_time=raw.info['meas_date'])
if matplotlib.get_backend() != 'agg':
plt.show(block=True)
_global_vars['raw_fig'] = fig
_global_vars['mne_close_key'] = fig.mne.close_key
