def test_export_edf_annotations(tmp_path):
"""Test that exporting EDF preserves annotations."""
rng = np.random.RandomState(123456)
format = 'edf'
ch_types = [
'eeg', 'eeg', 'stim', 'ecog', 'ecog', 'seeg', 'eog', 'ecg', 'emg',
'dbs', 'bio'
]
ch_names = np.arange(len(ch_types)).astype(str).tolist()
info = create_info(ch_names, sfreq=1000, ch_types=ch_types)
data = rng.random(size=(len(ch_names), 2000)) * 1.e-5
raw = RawArray(data, info)
annotations = Annotations(onset=[0.01, 0.05, 0.90, 1.05],
duration=[0, 1, 0, 0],
description=['test1', 'test2', 'test3', 'test4'])
raw.set_annotations(annotations)
# export
temp_fname = op.join(str(tmp_path), f'test.{format}')
raw.export(temp_fname)
# read in the file
raw_read = read_raw_edf(temp_fname, preload=True)
assert_array_equal(raw.annotations.onset, raw_read.annotations.onset)
assert_array_equal(raw.annotations.duration, raw_read.annotations.duration)
assert_array_equal(raw.annotations.description,
raw_read.annotations.description)
def test_chunk_duration():
"""Test chunk_duration."""
# create dummy raw
raw = RawArray(data=np.empty([10, 10], dtype=np.float64),
info=create_info(ch_names=10, sfreq=1.),
first_samp=0)
raw.info['meas_date'] = 0
raw.set_annotations(Annotations(description='foo', onset=[0],
duration=[10], orig_time=None))
# expected_events = [[0, 0, 1], [0, 0, 1], [1, 0, 1], [1, 0, 1], ..
# [9, 0, 1], [9, 0, 1]]
expected_events = np.atleast_2d(np.repeat(range(10), repeats=2)).T
expected_events = np.insert(expected_events, 1, 0, axis=1)
expected_events = np.insert(expected_events, 2, 1, axis=1)
events, events_id = events_from_annotations(raw, chunk_duration=.5,
use_rounding=False)
assert_array_equal(events, expected_events)
# test chunk durations that do not fit equally in annotation duration
expected_events = np.zeros((3, 3))
expected_events[:, -1] = 1
expected_events[:, 0] = np.arange(0, 9, step=3)
events, events_id = events_from_annotations(raw, chunk_duration=3.)
assert_array_equal(events, expected_events)
def test_annotation_omit():
"""Test raw.get_data with annotations."""
data = np.concatenate([np.ones((1, 1000)), 2 * np.ones((1, 1000))], -1)
info = create_info(1, 1000., 'eeg')
raw = RawArray(data, info)
raw.set_annotations(Annotations([0.5], [1], ['bad']))
expected = raw[0][0]
assert_allclose(raw.get_data(reject_by_annotation=None), expected)
# nan
expected[0, 500:1500] = np.nan
assert_allclose(raw.get_data(reject_by_annotation='nan'), expected)
got = np.concatenate([
raw.get_data(start=start, stop=stop, reject_by_annotation='nan')
for start, stop in ((0, 1000), (1000, 2000))
], -1)
assert_allclose(got, expected)
# omit
expected = expected[:, np.isfinite(expected[0])]
assert_allclose(raw.get_data(reject_by_annotation='omit'), expected)
got = np.concatenate([
raw.get_data(start=start, stop=stop, reject_by_annotation='omit')
for start, stop in ((0, 1000), (1000, 2000))
], -1)
assert_allclose(got, expected)
pytest.raises(ValueError, raw.get_data, reject_by_annotation='foo')
def _raw_annot(meas_date, orig_time):
info = create_info(ch_names=10, sfreq=10.)
raw = RawArray(data=np.empty((10, 10)), info=info, first_samp=10)
raw.info['meas_date'] = meas_date
annot = Annotations([.5], [.2], ['dummy'], orig_time)
raw.set_annotations(annotations=annot)
return raw
def _raw_annot(meas_date, orig_time, sync_orig=True):
info = create_info(ch_names=10, sfreq=10.)
raw = RawArray(data=np.empty((10, 10)), info=info, first_samp=10)
raw.info['meas_date'] = meas_date
annot = Annotations([.5], [.2], ['dummy'], orig_time)
raw.set_annotations(annotations=annot, sync_orig=sync_orig)
return raw
def raw_factory(meas_date):
raw = RawArray(data=np.empty((10, 10)),
info=create_info(ch_names=10, sfreq=10.),
first_samp=10)
raw.set_meas_date(meas_date)
raw.set_annotations(annotations=Annotations(
onset=[.5], duration=[.2], description='dummy', orig_time=None))
return raw
def raw_factory(meas_date):
raw = RawArray(data=np.empty((10, 10)),
info=create_info(ch_names=10, sfreq=10., ),
first_samp=10)
raw.info['meas_date'] = meas_date
raw.set_annotations(annotations=Annotations(onset=[.5],
duration=[.2],
description='dummy',
orig_time=None))
return raw
def test_negative_meas_dates(windows_like_datetime):
"""Test meas_date previous to 1970."""
# Regression test for gh-6621
raw = RawArray(data=np.empty((1, 1), dtype=np.float64),
info=create_info(ch_names=1, sfreq=1.))
raw.set_meas_date((-908196946, 988669))
raw.set_annotations(Annotations(description='foo', onset=[0],
duration=[0], orig_time=None))
events, _ = events_from_annotations(raw)
assert events[:, 0] == 0
def test_raw_reject():
"""Test raw data getter with annotation reject."""
sfreq = 100.
info = create_info(['a', 'b', 'c', 'd', 'e'], sfreq, ch_types='eeg')
raw = RawArray(np.ones((5, 15000)), info)
with pytest.warns(RuntimeWarning, match='outside the data range'):
raw.set_annotations(
Annotations([2, 100, 105, 148], [2, 8, 5, 8], 'BAD'))
data, times = raw.get_data(
[0, 1, 3, 4],
100,
11200, # 1-112 sec
'omit',
return_times=True)
bad_times = np.concatenate([
np.arange(200, 400),
np.arange(10000, 10800),
np.arange(10500, 11000)
])
expected_times = np.setdiff1d(np.arange(100, 11200), bad_times) / sfreq
assert_allclose(times, expected_times)
# with orig_time and complete overlap
raw = read_raw_fif(fif_fname)
raw.set_annotations(
Annotations(onset=[1, 4, 5] + raw._first_time,
duration=[1, 3, 1],
description='BAD',
orig_time=raw.info['meas_date']))
t_stop = 18.
assert raw.times[-1] > t_stop
n_stop = int(round(t_stop * raw.info['sfreq']))
n_drop = int(round(4 * raw.info['sfreq']))
assert len(raw.times) >= n_stop
data, times = raw.get_data(range(10), 0, n_stop, 'omit', True)
assert data.shape == (10, n_stop - n_drop)
assert times[-1] == raw.times[n_stop - 1]
assert_array_equal(data[:, -100:], raw[:10, n_stop - 100:n_stop][0])
data, times = raw.get_data(range(10), 0, n_stop, 'NaN', True)
assert_array_equal(data.shape, (10, n_stop))
assert times[-1] == raw.times[n_stop - 1]
t_1, t_2 = raw.time_as_index([1, 2], use_rounding=True)
assert np.isnan(data[:, t_1:t_2]).all() # 1s -2s
assert not np.isnan(data[:, :t_1].any())
assert not np.isnan(data[:, t_2:].any())
assert_array_equal(data[:, -100:], raw[:10, n_stop - 100:n_stop][0])
assert_array_equal(raw.get_data(), raw[:][0])
# Test _sync_onset
times = [10, -88, 190]
onsets = _sync_onset(raw, times)
assert_array_almost_equal(onsets,
times - raw.first_samp / raw.info['sfreq'])
assert_array_almost_equal(times, _sync_onset(raw, onsets, True))
def test_flat_bad_acq_skip():
"""Test that acquisition skips are handled properly."""
# -- file with a couple of skip and flat channels --
raw = read_raw_fif(skip_fname, preload=True)
annots, bads = annotate_amplitude(raw, flat=0)
assert len(annots) == 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()]
# -- overlap of flat segment with bad_acq_skip --
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')
raw = RawArray(data, info, first_samp=0)
raw.info['bads'] = [raw.ch_names[-1]]
bad_acq_skip = Annotations([0.5], [0.2], ['bad_acq_skip'], orig_time=None)
raw.set_annotations(bad_acq_skip)
# add flat channel overlapping with the left edge of bad_acq_skip
raw_ = raw.copy()
raw_._data[0, 400:600] = 0.
annots, bads = annotate_amplitude(raw_, peak=None, flat=0, bad_percent=25)
assert len(annots) == 1
assert len(bads) == 0
# check annotation instance
assert annots[0]['description'] == 'BAD_flat'
_check_annotation(raw_, annots[0], None, 0, 400, 499)
# add flat channel overlapping with the right edge of bad_acq_skip
raw_ = raw.copy()
raw_._data[0, 600:800] = 0.
annots, bads = annotate_amplitude(raw_, peak=None, flat=0, bad_percent=25)
assert len(annots) == 1
assert len(bads) == 0
# check annotation instance
assert annots[0]['description'] == 'BAD_flat'
_check_annotation(raw_, annots[0], None, 0, 700, 799)
# add flat channel overlapping entirely with bad_acq_skip
raw_ = raw.copy()
raw_._data[0, 200:800] = 0.
annots, bads = annotate_amplitude(raw_, peak=None, flat=0, bad_percent=41)
assert len(annots) == 2
assert len(bads) == 0
# check annotation instance
annots = sorted(annots, key=lambda x: x['onset'])
assert all(annot['description'] == 'BAD_flat' for annot in annots)
_check_annotation(raw_, annots[0], None, 0, 200, 500)
_check_annotation(raw_, annots[1], None, 0, 700, 799)
def test_raw_reject():
"""Test raw data getter with annotation reject."""
sfreq = 100.
info = create_info(['a', 'b', 'c', 'd', 'e'], sfreq, ch_types='eeg')
raw = RawArray(np.ones((5, 15000)), info)
with pytest.warns(RuntimeWarning, match='outside the data range'):
raw.set_annotations(Annotations([2, 100, 105, 148],
[2, 8, 5, 8], 'BAD'))
data, times = raw.get_data([0, 1, 3, 4], 100, 11200, # 1-112 sec
'omit', return_times=True)
bad_times = np.concatenate([np.arange(200, 400),
np.arange(10000, 10800),
np.arange(10500, 11000)])
expected_times = np.setdiff1d(np.arange(100, 11200), bad_times) / sfreq
assert_allclose(times, expected_times)
# with orig_time and complete overlap
raw = read_raw_fif(fif_fname)
raw.set_annotations(Annotations(onset=[1, 4, 5] + raw._first_time,
duration=[1, 3, 1],
description='BAD',
orig_time=raw.info['meas_date']))
t_stop = 18.
assert raw.times[-1] > t_stop
n_stop = int(round(t_stop * raw.info['sfreq']))
n_drop = int(round(4 * raw.info['sfreq']))
assert len(raw.times) >= n_stop
data, times = raw.get_data(range(10), 0, n_stop, 'omit', True)
assert data.shape == (10, n_stop - n_drop)
assert times[-1] == raw.times[n_stop - 1]
assert_array_equal(data[:, -100:], raw[:10, n_stop - 100:n_stop][0])
data, times = raw.get_data(range(10), 0, n_stop, 'NaN', True)
assert_array_equal(data.shape, (10, n_stop))
assert times[-1] == raw.times[n_stop - 1]
t_1, t_2 = raw.time_as_index([1, 2], use_rounding=True)
assert np.isnan(data[:, t_1:t_2]).all() # 1s -2s
assert not np.isnan(data[:, :t_1].any())
assert not np.isnan(data[:, t_2:].any())
assert_array_equal(data[:, -100:], raw[:10, n_stop - 100:n_stop][0])
assert_array_equal(raw.get_data(), raw[:][0])
# Test _sync_onset
times = [10, -88, 190]
onsets = _sync_onset(raw, times)
assert_array_almost_equal(onsets, times - raw.first_samp /
raw.info['sfreq'])
assert_array_almost_equal(times, _sync_onset(raw, onsets, True))
def _convert_to_raw(self, data, s):
fs = 500
ch_names = [
'Fp1', 'F5', 'F3', 'F1', 'Fz', 'FC5', 'FC3', 'FC1', 'FCz', 'C5',
'C3', 'C1', 'Cz', 'CP5', 'CP3', 'CP1', 'CPz', 'P5', 'P3', 'P1',
'Pz'
]
ch_types = ['eeg'] * 21
info = create_info(ch_names, fs, ch_types)
info['description'] = 'PGHealthy'
x = data['Signals'] * 1e-6
raw = RawArray(x, info)
epoch_start = np.array(data['Epoch_start']).T
N = len(epoch_start)
events = np.c_[epoch_start, np.zeros(N), np.ones(N) * (s + 1)]
events = events.astype(np.int)
raw.set_montage('standard_1005')
mapping = {v: k for k, v in self.event_id.items()}
onsets = events[:, 0] / raw.info['sfreq']
durations = np.zeros_like(onsets) # assumes instantaneous events
descriptions = [mapping[ev_id] for ev_id in events[:, 2]]
annot_from_events = Annotations(onset=onsets,
duration=durations,
description=descriptions)
raw.notch_filter(50, verbose=False)
return raw.set_annotations(annot_from_events)
def test_allow_nan_durations():
"""Deal with "n/a" strings in BIDS events with nan durations."""
raw = RawArray(data=np.empty([2, 10], dtype=np.float64),
info=create_info(ch_names=2, sfreq=1.),
first_samp=0)
raw.set_meas_date(0)
ons = [1, 2., 15., 17.]
dus = [np.nan, 1., 0.5, np.nan]
descriptions = ['A'] * 4
onsets = np.asarray(ons, dtype=float)
durations = np.asarray(dus, dtype=float)
annot = mne.Annotations(onset=onsets,
duration=durations,
description=descriptions)
with pytest.warns(RuntimeWarning, match='Omitted 2 annotation'):
raw.set_annotations(annot)
def _create_annotation_based_on_descr(description,
annotation_start_sampl=0,
duration=0,
orig_time=0):
"""Create a raw object with annotations from descriptions.
The returning raw object contains as many annotations as description given.
All starting at `annotation_start_sampl`.
"""
# create dummy raw
raw = RawArray(data=np.empty([10, 10], dtype=np.float64),
info=create_info(ch_names=10, sfreq=1000.),
first_samp=0)
raw.info['meas_date'] = 0
# create dummy annotations based on the descriptions
onset = raw.times[annotation_start_sampl]
onset_matching_desc = np.full_like(description, onset, dtype=type(onset))
duration_matching_desc = np.full_like(description,
duration,
dtype=type(duration))
annot = Annotations(description=description,
onset=onset_matching_desc,
duration=duration_matching_desc,
orig_time=orig_time)
if duration != 0:
with pytest.warns(RuntimeWarning, match='Limited.*expanding outside'):
# duration 0.1s is larger than the raw data expand
raw.set_annotations(annot)
else:
raw.set_annotations(annot)
# Make sure that set_annotations(annot) works
assert all(raw.annotations.onset == onset)
if duration != 0:
expected_duration = (len(raw.times) / raw.info['sfreq']) - onset
else:
expected_duration = 0
_duration = raw.annotations.duration[0]
assert _duration == approx(expected_duration)
assert all(raw.annotations.duration == _duration)
assert all(raw.annotations.description == description)
return raw
def _create_annotation_based_on_descr(description, annotation_start_sampl=0,
duration=0, orig_time=0):
"""Create a raw object with annotations from descriptions.
The returning raw object contains as many annotations as description given.
All starting at `annotation_start_sampl`.
"""
# create dummy raw
raw = RawArray(data=np.empty([10, 10], dtype=np.float64),
info=create_info(ch_names=10, sfreq=1000.),
first_samp=0)
raw.info['meas_date'] = 0
# create dummy annotations based on the descriptions
onset = raw.times[annotation_start_sampl]
onset_matching_desc = np.full_like(description, onset, dtype=type(onset))
duration_matching_desc = np.full_like(description, duration,
dtype=type(duration))
annot = Annotations(description=description,
onset=onset_matching_desc,
duration=duration_matching_desc,
orig_time=orig_time)
if duration != 0:
with pytest.warns(RuntimeWarning, match='Limited.*expanding outside'):
# duration 0.1s is larger than the raw data expand
raw.set_annotations(annot)
else:
raw.set_annotations(annot)
# Make sure that set_annotations(annot) works
assert all(raw.annotations.onset == onset)
if duration != 0:
expected_duration = (len(raw.times) / raw.info['sfreq']) - onset
else:
expected_duration = 0
_duration = raw.annotations.duration[0]
assert _duration == approx(expected_duration)
assert all(raw.annotations.duration == _duration)
assert all(raw.annotations.description == description)
return raw
def test_read_ctf_annotations():
"""Test reading CTF marker file."""
EXPECTED_LATENCIES = np.array([
5640, 7950, 9990, 12253, 14171, 16557, 18896, 20846, # noqa
22702, 24990, 26830, 28974, 30906, 33077, 34985, 36907, # noqa
38922, 40760, 42881, 45222, 47457, 49618, 51802, 54227, # noqa
56171, 58274, 60394, 62375, 64444, 66767, 68827, 71109, # noqa
73499, 75807, 78146, 80415, 82554, 84508, 86403, 88426, # noqa
90746, 92893, 94779, 96822, 98996, 99001, 100949, 103325, # noqa
105322, 107678, 109667, 111844, 113682, 115817, 117691, 119663, # noqa
121966, 123831, 126110, 128490, 130521, 132808, 135204, 137210, # noqa
139130, 141390, 143660, 145748, 147889, 150205, 152528, 154646, # noqa
156897, 159191, 161446, 163722, 166077, 168467, 170624, 172519, # noqa
174719, 176886, 179062, 181405, 183709, 186034, 188454, 190330, # noqa
192660, 194682, 196834, 199161, 201035, 203008, 204999, 207409, # noqa
209661, 211895, 213957, 216005, 218040, 220178, 222137, 224305, # noqa
226297, 228654, 230755, 232909, 235205, 237373, 239723, 241762, # noqa
243748, 245762, 247801, 250055, 251886, 254252, 256441, 258354, # noqa
260680, 263026, 265048, 267073, 269235, 271556, 273927, 276197, # noqa
278436, 280536, 282691, 284933, 287061, 288936, 290941, 293183, # noqa
295369, 297729, 299626, 301546, 303449, 305548, 307882, 310124, # noqa
312374, 314509, 316815, 318789, 320981, 322879, 324878, 326959, # noqa
329341, 331200, 331201, 333469, 335584, 337984, 340143, 342034, # noqa
344360, 346309, 348544, 350970, 353052, 355227, 357449, 359603, # noqa
361725, 363676, 365735, 367799, 369777, 371904, 373856, 376204, # noqa
378391, 380800, 382859, 385161, 387093, 389434, 391624, 393785, # noqa
396093, 398214, 400198, 402166, 404104, 406047, 408372, 410686, # noqa
413029, 414975, 416850, 418797, 420824, 422959, 425026, 427215, # noqa
429278, 431668 # noqa
]) - 1 # Fieldtrip has 1 sample difference with MNE
raw = RawArray(
data=np.empty((1, 432000), dtype=np.float64),
info=create_info(ch_names=1, sfreq=1200.0))
raw.set_meas_date(read_raw_ctf(somato_fname).info['meas_date'])
raw.set_annotations(read_annotations(somato_fname))
events, _ = events_from_annotations(raw)
latencies = np.sort(events[:, 0])
assert_allclose(latencies, EXPECTED_LATENCIES, atol=1e-6)
def test_get_data_reject():
"""Test if reject_by_annotation is working correctly."""
fs = 256
ch_names = ["C3", "Cz", "C4"]
info = create_info(ch_names, sfreq=fs)
raw = RawArray(np.zeros((len(ch_names), 10 * fs)), info)
raw.set_annotations(Annotations(onset=[2, 4], duration=[3, 2],
description="bad"))
with catch_logging() as log:
data = raw.get_data(reject_by_annotation="omit", verbose=True)
msg = ('Omitting 1024 of 2560 (40.00%) samples, retaining 1536' +
' (60.00%) samples.')
assert log.getvalue().strip() == msg
assert data.shape == (len(ch_names), 1536)
with catch_logging() as log:
data = raw.get_data(reject_by_annotation="nan", verbose=True)
msg = ('Setting 1024 of 2560 (40.00%) samples to NaN, retaining 1536' +
' (60.00%) samples.')
assert log.getvalue().strip() == msg
assert data.shape == (len(ch_names), 2560) # shape doesn't change
assert np.isnan(data).sum() == 3072 # but NaNs are introduced instead
def test_get_data_reject():
"""Test if reject_by_annotation is working correctly."""
fs = 256
ch_names = ["C3", "Cz", "C4"]
info = create_info(ch_names, sfreq=fs)
raw = RawArray(np.zeros((len(ch_names), 10 * fs)), info)
raw.set_annotations(Annotations(onset=[2, 4], duration=[3, 2],
description="bad"))
with catch_logging() as log:
data = raw.get_data(reject_by_annotation="omit", verbose=True)
msg = ('Omitting 1024 of 2560 (40.00%) samples, retaining 1536' +
' (60.00%) samples.')
assert log.getvalue().strip() == msg
assert data.shape == (len(ch_names), 1536)
with catch_logging() as log:
data = raw.get_data(reject_by_annotation="nan", verbose=True)
msg = ('Setting 1024 of 2560 (40.00%) samples to NaN, retaining 1536' +
' (60.00%) samples.')
assert log.getvalue().strip() == msg
assert data.shape == (len(ch_names), 2560) # shape doesn't change
assert np.isnan(data).sum() == 3072 # but NaNs are introduced instead
def test_annotation_omit():
"""Test raw.get_data with annotations."""
data = np.concatenate([np.ones((1, 1000)), 2 * np.ones((1, 1000))], -1)
info = create_info(1, 1000., 'eeg')
raw = RawArray(data, info)
raw.set_annotations(Annotations([0.5], [1], ['bad']))
expected = raw[0][0]
assert_allclose(raw.get_data(reject_by_annotation=None), expected)
# nan
expected[0, 500:1500] = np.nan
assert_allclose(raw.get_data(reject_by_annotation='nan'), expected)
got = np.concatenate([raw.get_data(start=start, stop=stop,
reject_by_annotation='nan')
for start, stop in ((0, 1000), (1000, 2000))], -1)
assert_allclose(got, expected)
# omit
expected = expected[:, np.isfinite(expected[0])]
assert_allclose(raw.get_data(reject_by_annotation='omit'), expected)
got = np.concatenate([raw.get_data(start=start, stop=stop,
reject_by_annotation='omit')
for start, stop in ((0, 1000), (1000, 2000))], -1)
assert_allclose(got, expected)
pytest.raises(ValueError, raw.get_data, reject_by_annotation='foo')
def raw_epochs_sphere():
"""Get the MATLAB EEG data."""
n_times = 386
mat_contents = sio.loadmat(eeg_fname)
data = mat_contents['data']
n_channels, n_epochs = data.shape[0], data.shape[1] // n_times
sfreq = 250.
ch_names = ['E%i' % i for i in range(1, n_channels + 1, 1)]
ch_types = ['eeg'] * n_channels
info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=sfreq)
raw = RawArray(data=data, info=info)
montage = make_standard_montage('GSN-HydroCel-257')
raw.set_montage(montage)
onset = raw.times[np.arange(50, n_epochs * n_times, n_times)]
raw.set_annotations(Annotations(onset=onset,
duration=np.repeat(0.1, 3),
description=np.repeat('foo', 3)))
events, event_id = events_from_annotations(raw)
epochs = Epochs(raw, events, event_id, tmin=-.2, tmax=1.34,
preload=True, reject=None, picks=None,
baseline=(None, 0), verbose=False)
sphere = (0., 0., 0., 0.095)
return raw, epochs, sphere
def test_raw_array_orig_times():
"""Test combining with RawArray and orig_times."""
data = np.random.randn(2, 1000) * 10e-12
sfreq = 100.
info = create_info(ch_names=['MEG1', 'MEG2'], ch_types=['grad'] * 2,
sfreq=sfreq)
meas_date = _handle_meas_date(np.pi)
info['meas_date'] = meas_date
raws = []
for first_samp in [12300, 100, 12]:
raw = RawArray(data.copy(), info, first_samp=first_samp)
ants = Annotations([1., 2.], [.5, .5], 'x', np.pi + first_samp / sfreq)
raw.set_annotations(ants)
raws.append(raw)
assert_allclose(raws[0].annotations.onset, [124, 125])
raw = RawArray(data.copy(), info)
assert not len(raw.annotations)
raw.set_annotations(Annotations([1.], [.5], 'x', None))
assert_allclose(raw.annotations.onset, [1.])
raws.append(raw)
raw = concatenate_raws(raws, verbose='debug')
assert raw.info['meas_date'] == raw.annotations.orig_time == meas_date
assert_and_remove_boundary_annot(raw, 3)
assert_array_equal(raw.annotations.onset, [124., 125., 134., 135.,
144., 145., 154.])
raw.annotations.delete(2)
assert_array_equal(raw.annotations.onset, [124., 125., 135., 144.,
145., 154.])
raw.annotations.append(5, 1.5, 'y')
assert_array_equal(raw.annotations.onset,
[5., 124., 125., 135., 144., 145., 154.])
assert_array_equal(raw.annotations.duration,
[1.5, .5, .5, .5, .5, .5, .5])
assert_array_equal(raw.annotations.description,
['y', 'x', 'x', 'x', 'x', 'x', 'x'])
# These three things should be equivalent
stamp = _dt_to_stamp(raw.info['meas_date'])
orig_time = _handle_meas_date(stamp)
for empty_annot in (
Annotations([], [], [], stamp),
Annotations([], [], [], orig_time),
Annotations([], [], [], None),
None):
raw.set_annotations(empty_annot)
assert isinstance(raw.annotations, Annotations)
assert len(raw.annotations) == 0
assert raw.annotations.orig_time == orig_time
def test_raw_array_orig_times():
"""Test combining with RawArray and orig_times."""
data = np.random.randn(2, 1000) * 10e-12
sfreq = 100.
info = create_info(ch_names=['MEG1', 'MEG2'], ch_types=['grad'] * 2,
sfreq=sfreq)
info['meas_date'] = (np.pi, 0)
raws = []
for first_samp in [12300, 100, 12]:
raw = RawArray(data.copy(), info, first_samp=first_samp)
ants = Annotations([1., 2.], [.5, .5], 'x', np.pi + first_samp / sfreq)
raw.set_annotations(ants)
raws.append(raw)
raw = RawArray(data.copy(), info)
raw.set_annotations(Annotations([1.], [.5], 'x', None))
raws.append(raw)
raw = concatenate_raws(raws, verbose='debug')
assert_and_remove_boundary_annot(raw, 3)
assert_array_equal(raw.annotations.onset, [124., 125., 134., 135.,
144., 145., 154.])
raw.annotations.delete(2)
assert_array_equal(raw.annotations.onset, [124., 125., 135., 144.,
145., 154.])
raw.annotations.append(5, 1.5, 'y')
assert_array_equal(raw.annotations.onset,
[5., 124., 125., 135., 144., 145., 154.])
assert_array_equal(raw.annotations.duration,
[1.5, .5, .5, .5, .5, .5, .5])
assert_array_equal(raw.annotations.description,
['y', 'x', 'x', 'x', 'x', 'x', 'x'])
# These three things should be equivalent
expected_orig_time = (raw.info['meas_date'][0] +
raw.info['meas_date'][1] / 1000000)
for empty_annot in (
Annotations([], [], [], expected_orig_time),
Annotations([], [], [], None),
None):
raw.set_annotations(empty_annot)
assert isinstance(raw.annotations, Annotations)
assert len(raw.annotations) == 0
assert raw.annotations.orig_time == expected_orig_time
def simulate_nirs_raw(sfreq=3.,
amplitude=1.,
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.):
"""
Create simulated data.
.. warning:: Work in progress: I am trying to think on the best API.
Parameters
----------
sfreq : Number
The sample rate.
amplitude : Number
The amplitude of the signal to simulate in uM.
sig_dur : Number
The length of the signal to generate in seconds.
stim_dur : Number
The length of the stimulus to generate in seconds.
isi_min : Number
The minimum duration of the inter stimulus interval in seconds.
isi_max : Number
The maximum duration of the inter stimulus interval in seconds.
Returns
-------
raw : instance of Raw
The generated raw instance.
"""
from nilearn.stats.first_level_model import make_first_level_design_matrix
from pandas import DataFrame
frame_times = np.arange(sig_dur * sfreq) / sfreq
onset = 0.
onsets = []
conditions = []
durations = []
while onset < sig_dur - 60:
onset += np.random.uniform(isi_min, isi_max) + stim_dur
onsets.append(onset)
conditions.append("A")
durations.append(stim_dur)
events = DataFrame({
'trial_type': conditions,
'onset': onsets,
'duration': durations
})
dm = make_first_level_design_matrix(frame_times,
events,
drift_model='polynomial',
drift_order=0)
annotations = Annotations(onsets, durations, conditions)
info = create_info(ch_names=['Simulated'], sfreq=sfreq, ch_types=['hbo'])
raw = RawArray(dm[["A"]].to_numpy().T * amplitude * 1.e-6,
info,
verbose=False)
raw.set_annotations(annotations)
return raw
def simulate_nirs_raw(sfreq=3.,
amplitude=1.,
annot_desc='A',
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.,
ch_name='Simulated',
hrf_model='glover'):
"""
Create simulated fNIRS data.
The returned data is of type `hbo`.
One or more conditions can be simulated.
To simulate multiple conditions pass in a description and amplitude
for each
`amplitude=[0., 2., 4.], annot_desc=['Control', 'Cond_A', 'Cond_B']`.
Parameters
----------
sfreq : Number
The sample rate.
amplitude : Number, Array of numbers
The amplitude of the signal to simulate in uM.
Pass in an array to simulate multiple conditions.
annot_desc : str, Array of str
The name of the annotations for simulated amplitudes.
Pass in an array to simulate multiple conditions,
must be the same length as amplitude.
sig_dur : Number
The length of the boxcar signal to generate in seconds that will
be convolved with the HRF.
stim_dur : Number, Array of numbers
The length of the stimulus to generate in seconds.
isi_min : Number
The minimum duration of the inter stimulus interval in seconds.
isi_max : Number
The maximum duration of the inter stimulus interval in seconds.
ch_name : str
Channel name to be used in returned raw instance.
hrf_model : str
Specifies the hemodynamic response function. See nilearn docs.
Returns
-------
raw : instance of Raw
The generated raw instance.
"""
from nilearn.glm.first_level import make_first_level_design_matrix
from pandas import DataFrame
if type(amplitude) is not list:
amplitude = [amplitude]
if type(annot_desc) is not list:
annot_desc = [annot_desc]
if type(stim_dur) is not list:
stim_dur = [stim_dur]
frame_times = np.arange(sig_dur * sfreq) / sfreq
assert len(amplitude) == len(annot_desc), "Same number of amplitudes as " \
"annotations required."
assert len(amplitude) == len(stim_dur), "Same number of amplitudes as " \
"durations required."
onset = 0.
onsets = []
conditions = []
durations = []
while onset < sig_dur - 60:
c_idx = np.random.randint(0, len(amplitude))
onset += np.random.uniform(isi_min, isi_max) + stim_dur[c_idx]
onsets.append(onset)
conditions.append(annot_desc[c_idx])
durations.append(stim_dur[c_idx])
events = DataFrame({
'trial_type': conditions,
'onset': onsets,
'duration': durations
})
dm = make_first_level_design_matrix(frame_times,
events,
hrf_model=hrf_model,
drift_model='polynomial',
drift_order=0)
dm = dm.drop(columns='constant')
annotations = Annotations(onsets, durations, conditions)
info = create_info(ch_names=[ch_name], sfreq=sfreq, ch_types=['hbo'])
for idx, annot in enumerate(annot_desc):
if annot in dm.columns:
dm[annot] *= amplitude[idx]
a = np.sum(dm.to_numpy(), axis=1) * 1.e-6
a = a.reshape(-1, 1).T
raw = RawArray(a, info, verbose=False)
raw.set_annotations(annotations)
return raw
def test_find_events():
"""Test find events in raw file."""
events = read_events(fname)
raw = read_raw_fif(raw_fname, preload=True)
# let's test the defaulting behavior while we're at it
extra_ends = ['', '_1']
orig_envs = [os.getenv('MNE_STIM_CHANNEL%s' % s) for s in extra_ends]
os.environ['MNE_STIM_CHANNEL'] = 'STI 014'
if 'MNE_STIM_CHANNEL_1' in os.environ:
del os.environ['MNE_STIM_CHANNEL_1']
events2 = find_events(raw)
assert_array_almost_equal(events, events2)
# now test with mask
events11 = find_events(raw, mask=3, mask_type='not_and')
with pytest.warns(RuntimeWarning, match='events masked'):
events22 = read_events(fname, mask=3, mask_type='not_and')
assert_array_equal(events11, events22)
# Reset some data for ease of comparison
raw._first_samps[0] = 0
raw.info['sfreq'] = 1000
stim_channel = 'STI 014'
stim_channel_idx = pick_channels(raw.info['ch_names'],
include=[stim_channel])
# test digital masking
raw._data[stim_channel_idx, :5] = np.arange(5)
raw._data[stim_channel_idx, 5:] = 0
# 1 == '0b1', 2 == '0b10', 3 == '0b11', 4 == '0b100'
pytest.raises(TypeError, find_events, raw, mask="0", mask_type='and')
pytest.raises(ValueError, find_events, raw, mask=0, mask_type='blah')
# testing mask_type. default = 'not_and'
assert_array_equal(
find_events(raw, shortest_event=1, mask=1, mask_type='not_and'),
[[2, 0, 2], [4, 2, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=2, mask_type='not_and'),
[[1, 0, 1], [3, 0, 1], [4, 1, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=3, mask_type='not_and'),
[[4, 0, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=4, mask_type='not_and'),
[[1, 0, 1], [2, 1, 2], [3, 2, 3]])
# testing with mask_type = 'and'
assert_array_equal(
find_events(raw, shortest_event=1, mask=1, mask_type='and'),
[[1, 0, 1], [3, 0, 1]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=2, mask_type='and'),
[[2, 0, 2]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=3, mask_type='and'),
[[1, 0, 1], [2, 1, 2], [3, 2, 3]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=4, mask_type='and'),
[[4, 0, 4]])
# test empty events channel
raw._data[stim_channel_idx, :] = 0
assert_array_equal(find_events(raw), np.empty((0, 3), dtype='int32'))
raw._data[stim_channel_idx, :4] = 1
assert_array_equal(find_events(raw), np.empty((0, 3), dtype='int32'))
raw._data[stim_channel_idx, -1:] = 9
assert_array_equal(find_events(raw), [[14399, 0, 9]])
# Test that we can handle consecutive events with no gap
raw._data[stim_channel_idx, 10:20] = 5
raw._data[stim_channel_idx, 20:30] = 6
raw._data[stim_channel_idx, 30:32] = 5
raw._data[stim_channel_idx, 40] = 6
assert_array_equal(find_events(raw, consecutive=False),
[[10, 0, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(
find_events(raw, consecutive=True),
[[10, 0, 5], [20, 5, 6], [30, 6, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw),
[[10, 0, 5], [20, 5, 6], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw, output='offset', consecutive=False),
[[31, 0, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(
find_events(raw, output='offset', consecutive=True),
[[19, 6, 5], [29, 5, 6], [31, 0, 5], [40, 0, 6], [14399, 0, 9]])
pytest.raises(ValueError,
find_events,
raw,
output='step',
consecutive=True)
assert_array_equal(
find_events(raw, output='step', consecutive=True, shortest_event=1),
[[10, 0, 5], [20, 5, 6], [30, 6, 5], [32, 5, 0], [40, 0, 6],
[41, 6, 0], [14399, 0, 9], [14400, 9, 0]])
assert_array_equal(find_events(raw, output='offset'),
[[19, 6, 5], [31, 0, 6], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw, consecutive=False, min_duration=0.002),
[[10, 0, 5]])
assert_array_equal(find_events(raw, consecutive=True, min_duration=0.002),
[[10, 0, 5], [20, 5, 6], [30, 6, 5]])
assert_array_equal(
find_events(raw,
output='offset',
consecutive=False,
min_duration=0.002), [[31, 0, 5]])
assert_array_equal(
find_events(raw, output='offset', consecutive=True,
min_duration=0.002), [[19, 6, 5], [29, 5, 6], [31, 0, 5]])
assert_array_equal(find_events(raw, consecutive=True, min_duration=0.003),
[[10, 0, 5], [20, 5, 6]])
# test find_stim_steps merge parameter
raw._data[stim_channel_idx, :] = 0
raw._data[stim_channel_idx, 0] = 1
raw._data[stim_channel_idx, 10] = 4
raw._data[stim_channel_idx, 11:20] = 5
assert_array_equal(
find_stim_steps(raw, pad_start=0, merge=0, stim_channel=stim_channel),
[[0, 0, 1], [1, 1, 0], [10, 0, 4], [11, 4, 5], [20, 5, 0]])
assert_array_equal(
find_stim_steps(raw, merge=-1, stim_channel=stim_channel),
[[1, 1, 0], [10, 0, 5], [20, 5, 0]])
assert_array_equal(
find_stim_steps(raw, merge=1, stim_channel=stim_channel),
[[1, 1, 0], [11, 0, 5], [20, 5, 0]])
# put back the env vars we trampled on
for s, o in zip(extra_ends, orig_envs):
if o is not None:
os.environ['MNE_STIM_CHANNEL%s' % s] = o
# Test with list of stim channels
raw._data[stim_channel_idx, 1:101] = np.zeros(100)
raw._data[stim_channel_idx, 10:11] = 1
raw._data[stim_channel_idx, 30:31] = 3
stim_channel2 = 'STI 015'
stim_channel2_idx = pick_channels(raw.info['ch_names'],
include=[stim_channel2])
raw._data[stim_channel2_idx, :] = 0
raw._data[stim_channel2_idx, :100] = raw._data[stim_channel_idx, 5:105]
events1 = find_events(raw, stim_channel='STI 014')
events2 = events1.copy()
events2[:, 0] -= 5
events = find_events(raw, stim_channel=['STI 014', stim_channel2])
assert_array_equal(events[::2], events2)
assert_array_equal(events[1::2], events1)
# test initial_event argument
info = create_info(['MYSTI'], 1000, 'stim')
data = np.zeros((1, 1000))
raw = RawArray(data, info)
data[0, :10] = 100
data[0, 30:40] = 200
assert_array_equal(find_events(raw, 'MYSTI'), [[30, 0, 200]])
assert_array_equal(find_events(raw, 'MYSTI', initial_event=True),
[[0, 0, 100], [30, 0, 200]])
# test error message for raw without stim channels
raw = read_raw_fif(raw_fname, preload=True)
raw.pick_types(meg=True, stim=False)
# raw does not have annotations
with pytest.raises(ValueError, match="'stim_channel'"):
find_events(raw)
# if raw has annotations, we show a different error message
raw.set_annotations(Annotations(0, 2, "test"))
with pytest.raises(ValueError, match="mne.events_from_annotations"):
find_events(raw)
def test_basics():
"""Test annotation class."""
raw = read_raw_fif(fif_fname)
assert raw.annotations is not None # XXX to be fixed in #5416
assert len(raw.annotations.onset) == 0 # XXX to be fixed in #5416
pytest.raises(IOError, read_annotations, fif_fname)
onset = np.array(range(10))
duration = np.ones(10)
description = np.repeat('test', 10)
dt = datetime.utcnow()
meas_date = raw.info['meas_date']
# Test time shifts.
for orig_time in [None, dt, meas_date[0], meas_date]:
annot = Annotations(onset, duration, description, orig_time)
pytest.raises(ValueError, Annotations, onset, duration, description[:9])
pytest.raises(ValueError, Annotations, [onset, 1], duration, description)
pytest.raises(ValueError, Annotations, onset, [duration, 1], description)
# Test combining annotations with concatenate_raws
raw2 = raw.copy()
delta = raw.times[-1] + 1. / raw.info['sfreq']
orig_time = (meas_date[0] + meas_date[1] * 1e-6 + raw2._first_time)
offset = orig_time - _handle_meas_date(raw2.info['meas_date'])
annot = Annotations(onset, duration, description, orig_time)
assert ' segments' in repr(annot)
raw2.set_annotations(annot)
assert_array_equal(raw2.annotations.onset, onset + offset)
assert id(raw2.annotations) != id(annot)
concatenate_raws([raw, raw2])
raw.annotations.delete(-1) # remove boundary annotations
raw.annotations.delete(-1)
assert_allclose(onset + offset + delta, raw.annotations.onset, rtol=1e-5)
assert_array_equal(annot.duration, raw.annotations.duration)
assert_array_equal(raw.annotations.description, np.repeat('test', 10))
# Test combining with RawArray and orig_times
data = np.random.randn(2, 1000) * 10e-12
sfreq = 100.
info = create_info(ch_names=['MEG1', 'MEG2'], ch_types=['grad'] * 2,
sfreq=sfreq)
info['meas_date'] = (np.pi, 0)
raws = []
for first_samp in [12300, 100, 12]:
raw = RawArray(data.copy(), info, first_samp=first_samp)
ants = Annotations([1., 2.], [.5, .5], 'x', np.pi + first_samp / sfreq)
raw.set_annotations(ants)
raws.append(raw)
raw = RawArray(data.copy(), info)
raw.set_annotations(Annotations([1.], [.5], 'x', None))
raws.append(raw)
raw = concatenate_raws(raws, verbose='debug')
boundary_idx = np.where(raw.annotations.description == 'BAD boundary')[0]
assert len(boundary_idx) == 3
raw.annotations.delete(boundary_idx)
boundary_idx = np.where(raw.annotations.description == 'EDGE boundary')[0]
assert len(boundary_idx) == 3
raw.annotations.delete(boundary_idx)
assert_array_equal(raw.annotations.onset, [124., 125., 134., 135.,
144., 145., 154.])
raw.annotations.delete(2)
assert_array_equal(raw.annotations.onset, [124., 125., 135., 144.,
145., 154.])
raw.annotations.append(5, 1.5, 'y')
assert_array_equal(raw.annotations.onset, [124., 125., 135., 144.,
145., 154., 5.])
assert_array_equal(raw.annotations.duration, [.5, .5, .5, .5, .5, .5, 1.5])
assert_array_equal(raw.annotations.description, ['x', 'x', 'x', 'x', 'x',
'x', 'y'])
def simulate_nirs_raw(sfreq=3.,
amplitude=1.,
annot_desc='A',
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.,
ch_name='Simulated'):
"""
Create simulated data.
.. warning:: Work in progress: I am trying to think on the best API.
Parameters
----------
sfreq : Number
The sample rate.
amplitude : Number, Array of numbers
The amplitude of the signal to simulate in uM.
annot_desc : String, Array of strings
The name of the annotations for simulated amplitudes.
sig_dur : Number
The length of the signal to generate in seconds.
stim_dur : Number, Array of numbers
The length of the stimulus to generate in seconds.
isi_min : Number
The minimum duration of the inter stimulus interval in seconds.
isi_max : Number
The maximum duration of the inter stimulus interval in seconds.
ch_name : String
Channel name to be used in returned raw instance.
Returns
-------
raw : instance of Raw
The generated raw instance.
"""
from nilearn.glm.first_level import make_first_level_design_matrix
from pandas import DataFrame
if type(amplitude) is not list:
amplitude = [amplitude]
if type(annot_desc) is not list:
annot_desc = [annot_desc]
if type(stim_dur) is not list:
stim_dur = [stim_dur]
frame_times = np.arange(sig_dur * sfreq) / sfreq
assert len(amplitude) == len(annot_desc), "Same number of amplitudes as " \
"annotations required."
assert len(amplitude) == len(stim_dur), "Same number of amplitudes as " \
"durations required."
onset = 0.
onsets = []
conditions = []
durations = []
while onset < sig_dur - 60:
c_idx = np.random.randint(0, len(amplitude))
onset += np.random.uniform(isi_min, isi_max) + stim_dur[c_idx]
onsets.append(onset)
conditions.append(annot_desc[c_idx])
durations.append(stim_dur[c_idx])
events = DataFrame({
'trial_type': conditions,
'onset': onsets,
'duration': durations
})
dm = make_first_level_design_matrix(frame_times,
events,
drift_model='polynomial',
drift_order=0)
dm = dm.drop(columns='constant')
annotations = Annotations(onsets, durations, conditions)
info = create_info(ch_names=[ch_name], sfreq=sfreq, ch_types=['hbo'])
for idx, annot in enumerate(annot_desc):
if annot in dm.columns:
dm[annot] *= amplitude[idx]
a = np.sum(dm.to_numpy(), axis=1) * 1.e-6
a = a.reshape(-1, 1).T
raw = RawArray(a, info, verbose=False)
raw.set_annotations(annotations)
return raw
def test_basics():
"""Test annotation class."""
raw = read_raw_fif(fif_fname)
assert raw.annotations is not None # XXX to be fixed in #5416
assert len(raw.annotations.onset) == 0 # XXX to be fixed in #5416
pytest.raises(IOError, read_annotations, fif_fname)
onset = np.array(range(10))
duration = np.ones(10)
description = np.repeat('test', 10)
dt = datetime.utcnow()
meas_date = raw.info['meas_date']
# Test time shifts.
for orig_time in [None, dt, meas_date[0], meas_date]:
annot = Annotations(onset, duration, description, orig_time)
pytest.raises(ValueError, Annotations, onset, duration, description[:9])
pytest.raises(ValueError, Annotations, [onset, 1], duration, description)
pytest.raises(ValueError, Annotations, onset, [duration, 1], description)
# Test combining annotations with concatenate_raws
raw2 = raw.copy()
delta = raw.times[-1] + 1. / raw.info['sfreq']
orig_time = (meas_date[0] + meas_date[1] * 1e-6 + raw2._first_time)
offset = orig_time - _handle_meas_date(raw2.info['meas_date'])
annot = Annotations(onset, duration, description, orig_time)
assert ' segments' in repr(annot)
raw2.set_annotations(annot)
assert_array_equal(raw2.annotations.onset, onset + offset)
assert id(raw2.annotations) != id(annot)
concatenate_raws([raw, raw2])
assert_and_remove_boundary_annot(raw)
assert_allclose(onset + offset + delta, raw.annotations.onset, rtol=1e-5)
assert_array_equal(annot.duration, raw.annotations.duration)
assert_array_equal(raw.annotations.description, np.repeat('test', 10))
# Test combining with RawArray and orig_times
data = np.random.randn(2, 1000) * 10e-12
sfreq = 100.
info = create_info(ch_names=['MEG1', 'MEG2'],
ch_types=['grad'] * 2,
sfreq=sfreq)
info['meas_date'] = (np.pi, 0)
raws = []
for first_samp in [12300, 100, 12]:
raw = RawArray(data.copy(), info, first_samp=first_samp)
ants = Annotations([1., 2.], [.5, .5], 'x', np.pi + first_samp / sfreq)
raw.set_annotations(ants)
raws.append(raw)
raw = RawArray(data.copy(), info)
raw.set_annotations(Annotations([1.], [.5], 'x', None))
raws.append(raw)
raw = concatenate_raws(raws, verbose='debug')
assert_and_remove_boundary_annot(raw, 3)
assert_array_equal(raw.annotations.onset,
[124., 125., 134., 135., 144., 145., 154.])
raw.annotations.delete(2)
assert_array_equal(raw.annotations.onset,
[124., 125., 135., 144., 145., 154.])
raw.annotations.append(5, 1.5, 'y')
assert_array_equal(raw.annotations.onset,
[5., 124., 125., 135., 144., 145., 154.])
assert_array_equal(raw.annotations.duration, [1.5, .5, .5, .5, .5, .5, .5])
assert_array_equal(raw.annotations.description,
['y', 'x', 'x', 'x', 'x', 'x', 'x'])
