def test_read_curry_annotations_using_mocked_info(tmpdir, name_part):
"""Test reading for Curry events file."""
EXPECTED_ONSET = [
0.484, 0.486, 0.62, 0.622, 1.904, 1.906, 3.212, 3.214, 4.498, 4.5, 5.8,
5.802, 7.074, 7.076, 8.324, 8.326, 9.58, 9.582
]
EXPECTED_DURATION = np.zeros_like(EXPECTED_ONSET)
EXPECTED_DESCRIPTION = [
'4', '50000', '2', '50000', '1', '50000', '1', '50000', '1', '50000',
'1', '50000', '1', '50000', '1', '50000', '1', '50000'
]
original, fname = _get_read_annotations_mock_info("Curry " + name_part,
tmpdir)
copyfile(src=original['event'], dst=fname['event'])
_msg = 'required files cannot be found'
with pytest.raises(FileNotFoundError, match=_msg):
read_annotations(fname['event'], sfreq='auto')
_mock_info_file(src=original['info'],
dst=fname['info'],
sfreq=0,
time_step=2000)
annot = read_annotations(fname['event'], sfreq='auto')
assert annot.orig_time is None
assert_array_equal(annot.onset, EXPECTED_ONSET)
assert_array_equal(annot.duration, EXPECTED_DURATION)
assert_array_equal(annot.description, EXPECTED_DESCRIPTION)
def detect_movement(self, thr_mov=.01, plot=True, overwrite=False,
save=True):
from mne.transforms import read_trans
fname = self.subject + '_' + self.experiment + '_mov.txt'
out_csv_f = op.join(self.out_annot, fname)
fname_t = self.subject + '_' + self.experiment + '_dev2head-trans.fif'
out_csv_f_t = op.join(self.out_annot, fname_t)
if op.exists(out_csv_f) and not overwrite:
mov_annot = read_annotations(out_csv_f)
print('Reading from file, mov segments are:', mov_annot)
print('Reading from file, dev to head transformation')
dev_head_t = read_trans(out_csv_f_t)
else:
print('Calculating head pos')
pos = mne.chpi._calculate_head_pos_ctf(self.raw, gof_limit=-1)
mov_annot, hpi_disp, dev_head_t = annotate_motion(self.raw, pos,
thr=thr_mov)
if plot is True:
plt.figure()
plt.plot(hpi_disp)
plt.axhline(y=thr_mov, color='r')
plt.show(block=True)
if save is True:
mov_annot.save(out_csv_f)
dev_head_t.save(out_csv_f_t)
#fig.savefig(out_csv_f[:-4]+'.png')
old_annot = self.raw.annotations # if orig_time cant + with none time
self.raw.set_annotations(mov_annot)
self.raw.set_annotations(self.raw.annotations + old_annot)
self.raw.info['dev_head_t_old'] = self.raw.info['dev_head_t']
self.raw.info['dev_head_t'] = dev_head_t
self.annot_movement = mov_annot
def test_eeglab_annotations(fname):
"""Test reading annotations in EEGLAB files."""
_check_h5(fname)
annotations = read_annotations(fname)
assert len(annotations) == 154
assert set(annotations.description) == {'rt', 'square'}
assert np.all(annotations.duration == 0.)
def test_eeglab_annotations(fname):
"""Test reading annotations in EEGLAB files."""
_check_h5(fname)
annotations = read_annotations(fname)
assert len(annotations) == 154
assert set(annotations.description) == {'rt', 'square'}
assert np.all(annotations.duration == 0.)
def get_trial_info(subject, inputs, outputs, recompute, logpath):
"""Extract trial information from Presentation log files and BrainVision
marker files.
1. trial type {high threat, low threat}
2. start baseline (seconds)
3. cue (seconds)
4. stimulus (seconds)
"""
save_path = Path(individualize_path(outputs["save_path"], subject, expand_name=True))
if save_path.exists() and not recompute: # only recompute if requested
print(f"Not re-computing {save_path}")
return
log_path = next(Path(".").glob(individualize_path(inputs["log_path"], subject)))
marker_path = next(Path(".").glob(individualize_path(inputs["marker_path"], subject)))
df_log = pd.read_csv(log_path, sep="\t", usecols=["CSI", "shock"])
markers = read_annotations(marker_path, ECG_SFREQ_ORIGINAL)
cues = markers.onset[markers.description == "Stimulus/S 2"]
assert len(cues) == df_log.shape[0], ("Unequal number of trials between"
" BrainVision and Presentation files"
f" for participant {subject}.")
trial_types = df_log["shock"]
stimuli = cues + df_log["CSI"] / 1000
baselines = cues - 1 # baseline starts 1 second before cue
df = pd.DataFrame({"threat": trial_types, "baseline": baselines,
"cue": cues, "stimulus": stimuli})
df = df[(df["stimulus"] - df["cue"]) >= 6] # exclude trials with anticipation windows shorter than 6 seconds
df.to_csv(save_path, sep="\t", header=True, index=False, float_format="%.4f")
def test_eeglab_read_annotations():
"""Test annotations onsets are timestamps (+ validate some)."""
annotations = read_annotations(raw_fname_mat)
validation_samples = [0, 1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
expected_onset = np.array([1.00, 1.69, 2.08, 4.70, 7.71, 11.30, 17.18,
20.20, 26.12, 29.14, 35.25, 44.30, 47.15])
assert annotations.orig_time is None
assert_array_almost_equal(annotations.onset[validation_samples],
expected_onset, decimal=2)
def test_eeglab_read_annotations():
"""Test annotations onsets are timestamps (+ validate some)."""
annotations = read_annotations(raw_fname_mat)
validation_samples = [0, 1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
expected_onset = np.array([1.00, 1.69, 2.08, 4.70, 7.71, 11.30, 17.18,
20.20, 26.12, 29.14, 35.25, 44.30, 47.15])
assert annotations.orig_time is None
assert_array_almost_equal(annotations.onset[validation_samples],
expected_onset, decimal=2)
def detect_muscle(self, thr=1.5, t_min=.5, plot=True, overwrite=False):
"""Find and annotate mucsle artifacts - by Luke Bloy"""
fname = self.subject + '_' + self.experiment + '_mus.txt'
out_csv_f = op.join(self.out_annot, fname)
if op.exists(out_csv_f) and not overwrite:
mus_annot = read_annotations(out_csv_f)
print('Reading from file, muscle segments are:', mus_annot)
else:
print('Calculating muscle artifacts')
raw = self.raw.copy().load_data()
raw.pick_types(meg=True, ref_meg=False)
raw.notch_filter(np.arange(60, 241, 60), fir_design='firwin')
raw.filter(110, 140, fir_design='firwin')
raw.apply_hilbert(envelope=True)
sfreq = raw.info['sfreq']
art_scores = stats.zscore(raw._data, axis=1)
# band pass filter the data
art_scores_filt = mne.filter.filter_data(art_scores.mean(axis=0),
sfreq, None, 4)
art_mask = art_scores_filt > thr
# remove artifact free periods shorter than t_min
idx_min = t_min * sfreq
comps, num_comps = label(art_mask == 0)
for l in range(1, num_comps+1):
l_idx = np.nonzero(comps == l)[0]
if len(l_idx) < idx_min:
art_mask[l_idx] = True
mus_annot = _annotations_from_mask(raw.times, art_mask,
'Bad-muscle')
if plot:
del raw
print('Plotting data, mark or delete art, by pressing a \n'
'Marked or demarked channels will be saved')
old_bd_chns = self.raw.info['bads']
raw = self.raw.copy().load_data().pick_types(meg=True,
ref_meg=False)
raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')
raw.filter(1, 140)
old_annot = raw.annotations # if orig_time cant + none
raw.set_annotations(mus_annot)
raw.set_annotations(raw.annotations + old_annot)
raw.plot(n_channels=140, block=True, bad_color='r')
mus_annot = raw.annotations
if not (old_bd_chns == raw.info['bads']):
bad_chns = raw.info['bads']
print('Saving new bad channels list \n ')
print('Bad chans are:', bad_chns)
fname = self.subject + '_' + self.experiment + '_bads.csv'
csv_save(bad_chns, op.join(self.out_bd_ch, fname))
mus_annot.save(out_csv_f)
old_annot = self.raw.annotations # if orig_time cant + with none time
self.raw.set_annotations(mus_annot)
self.raw.set_annotations(self.raw.annotations + old_annot)
self.annot_muscle = mus_annot
def test_compare_events_and_annotations():
"""Test comparing annotations and events."""
with pytest.warns(RuntimeWarning, match='Could not parse meas date'):
raw = read_raw_cnt(fname)
events = np.array([[333, 0, 7], [1010, 0, 7], [1664, 0, 109], [2324, 0, 7],
[2984, 0, 109]])
annot = read_annotations(fname)
assert len(annot) == 6
assert_array_equal(annot.onset[:-1], events[:, 0] / raw.info['sfreq'])
assert 'STI 014' not in raw.info['ch_names']
def test_compare_events_and_annotations(recwarn):
"""Test comparing annotations and events."""
from mne import find_events
from numpy.testing import assert_array_equal
raw = read_raw_cnt(input_fname=fname, montage=None, preload=False,
stim_channel=True)
events = find_events(raw)
annot = read_annotations(fname)
assert len(annot) == 6
assert_array_equal(annot.onset[:-1], events[:, 0] / raw.info['sfreq'])
def test_eeglab_event_from_annot():
"""Test all forms of obtaining annotations."""
raw_fname_mat = op.join(base_dir, 'test_raw.set')
raw_fname = raw_fname_mat
event_id = {'rt': 1, 'square': 2}
raw1 = read_raw_eeglab(input_fname=raw_fname, preload=False)
annotations = read_annotations(raw_fname)
assert len(raw1.annotations) == 154
raw1.set_annotations(annotations)
events_b, _ = events_from_annotations(raw1, event_id=event_id)
assert len(events_b) == 154
def test_eeglab_read_annotations():
"""Test annotations onsets are timestamps (+ validate some)."""
annotations = read_annotations(raw_fname_mat)
validation_samples = [0, 1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
expected_onset = np.array([1.00, 1.69, 2.08, 4.70, 7.71, 11.30, 17.18,
20.20, 26.12, 29.14, 35.25, 44.30, 47.15])
assert annotations.orig_time is None
assert_array_almost_equal(annotations.onset[validation_samples],
expected_onset, decimal=2)
# test if event durations are imported correctly
raw = read_raw_eeglab(raw_fname_event_duration, preload=True)
# file contains 3 annotations with 0.5 s (64 samples) duration each
assert_allclose(raw.annotations.duration, np.ones(3) * 0.5)
def test_compare_events_and_annotations():
"""Test comparing annotations and events."""
with pytest.warns(RuntimeWarning, match='Could not parse meas date'):
raw = read_raw_cnt(fname)
events = np.array([[333, 0, 7],
[1010, 0, 7],
[1664, 0, 109],
[2324, 0, 7],
[2984, 0, 109]])
annot = read_annotations(fname)
assert len(annot) == 6
assert_array_equal(annot.onset[:-1], events[:, 0] / raw.info['sfreq'])
assert 'STI 014' not in raw.info['ch_names']
def test_eeglab_read_annotations():
"""Test annotations onsets are timestamps (+ validate some)."""
annotations = read_annotations(raw_fname_mat)
validation_samples = [0, 1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
expected_onset = np.array([1.00, 1.69, 2.08, 4.70, 7.71, 11.30, 17.18,
20.20, 26.12, 29.14, 35.25, 44.30, 47.15])
assert annotations.orig_time is None
assert_array_almost_equal(annotations.onset[validation_samples],
expected_onset, decimal=2)
# test if event durations are imported correctly
raw = read_raw_eeglab(raw_fname_event_duration, preload=True)
# file contains 3 annotations with 0.5 s (64 samples) duration each
assert_allclose(raw.annotations.duration, np.ones(3) * 0.5)
def test_eeglab_event_from_annot():
"""Test all forms of obtaining annotations."""
base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
raw_fname_mat = op.join(base_dir, 'test_raw.set')
raw_fname = raw_fname_mat
montage = op.join(base_dir, 'test_chans.locs')
event_id = {'rt': 1, 'square': 2}
raw1 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
preload=False)
annotations = read_annotations(raw_fname)
assert len(raw1.annotations) == 154
raw1.set_annotations(annotations)
events_b, _ = events_from_annotations(raw1, event_id=event_id)
assert len(events_b) == 154
def test_eeglab_event_from_annot():
"""Test all forms of obtaining annotations."""
base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
raw_fname_mat = op.join(base_dir, 'test_raw.set')
raw_fname = raw_fname_mat
montage = op.join(base_dir, 'test_chans.locs')
event_id = {'rt': 1, 'square': 2}
raw1 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
preload=False)
annotations = read_annotations(raw_fname)
assert len(raw1.annotations) == 154
raw1.set_annotations(annotations)
events_b, _ = events_from_annotations(raw1, event_id=event_id)
assert len(events_b) == 154
def test_read_curry_annotations(fname):
"""Test reading for Curry events file."""
EXPECTED_ONSET = [0.484, 0.486, 0.62, 0.622, 1.904, 1.906, 3.212, 3.214,
4.498, 4.5, 5.8, 5.802, 7.074, 7.076, 8.324, 8.326, 9.58,
9.582]
EXPECTED_DURATION = np.zeros_like(EXPECTED_ONSET)
EXPECTED_DESCRIPTION = ['4', '50000', '2', '50000', '1', '50000', '1',
'50000', '1', '50000', '1', '50000', '1', '50000',
'1', '50000', '1', '50000']
annot = read_annotations(fname, sfreq='auto')
assert annot.orig_time is None
assert_array_equal(annot.onset, EXPECTED_ONSET)
assert_array_equal(annot.duration, EXPECTED_DURATION)
assert_array_equal(annot.description, EXPECTED_DESCRIPTION)
def test_find_events_and_events_from_annot_are_the_same():
"""Test that old behaviour and new produce the same events."""
EXPECTED_EVENTS = [[68, 0, 2], [199, 0, 2], [1024, 0, 3], [1280, 0, 2]]
raw = read_raw_edf(edf_path, preload=True, stim_channel='auto')
raw_shell = _get_empty_raw_with_valid_annot(edf_path)
assert raw_shell.info['meas_date'] == raw.info['meas_date']
assert raw_shell.info['sfreq'] == raw.info['sfreq']
assert raw_shell.first_samp == raw.first_samp
events_from_find_events = find_events(raw)
assert_array_equal(events_from_find_events, EXPECTED_EVENTS)
annot = read_annotations(edf_path)
raw_shell.set_annotations(annot)
event_id = _get_edf_default_event_id(annot.description)
event_id.pop('start')
events_from_EFA, _ = events_from_annotations(raw_shell,
event_id=event_id,
use_rounding=False)
assert_array_equal(events_from_EFA, events_from_find_events)
def test_find_events_and_events_from_annot_are_the_same():
"""Test that old behaviour and new produce the same events."""
EXPECTED_EVENTS = [[68, 0, 2],
[199, 0, 2],
[1024, 0, 3],
[1280, 0, 2]]
raw = read_raw_edf(edf_path, preload=True, stim_channel='auto')
raw_shell = _get_empty_raw_with_valid_annot(edf_path)
assert raw_shell.info['meas_date'] == raw.info['meas_date']
assert raw_shell.info['sfreq'] == raw.info['sfreq']
assert raw_shell.first_samp == raw.first_samp
events_from_find_events = find_events(raw)
assert_array_equal(events_from_find_events, EXPECTED_EVENTS)
annot = read_annotations(edf_path)
raw_shell.set_annotations(annot)
event_id = _get_edf_default_event_id(annot.description)
event_id.pop('start')
events_from_EFA, _ = events_from_annotations(raw_shell, event_id=event_id,
use_rounding=False)
assert_array_equal(events_from_EFA, events_from_find_events)
def test_eeglab_event_from_annot():
"""Test all forms of obtaining annotations."""
base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
raw_fname_mat = op.join(base_dir, 'test_raw.set')
raw_fname = raw_fname_mat
montage = op.join(base_dir, 'test_chans.locs')
event_id = {'rt': 1, 'square': 2}
with pytest.deprecated_call(match='stim_channel'):
raw1 = read_raw_eeglab(input_fname=raw_fname,
montage=montage,
event_id=event_id,
preload=False)
events_a = find_events(raw1)
with pytest.deprecated_call(match='read_events_eeglab'):
events_b = read_events_eeglab(raw_fname, event_id=event_id)
annotations = read_annotations(raw_fname)
assert len(raw1.annotations) == 154
raw1.set_annotations(annotations)
events_c, _ = events_from_annotations(raw1, event_id=event_id)
assert_array_equal(events_a, events_b)
assert_array_equal(events_a, events_c)
def test_read_annotations(fname, recwarn):
"""Test IO of annotations from edf and bdf files via regexp."""
annot = read_annotations(fname)
assert len(annot.onset) == 2
def test_read_annotations(fname, recwarn):
"""Test IO of annotations from edf and bdf files via regexp."""
annot = read_annotations(fname)
assert len(annot.onset) == 2
