def test_check_tmin_tmax():
raw = create_fake_raw(n_channels=2, n_samples=35, sfreq=10.)
tmin, tmax = -1., 4.
tmin, tmax, sfreq = _check_tmin_tmax(raw, tmin, tmax)
assert tmin == 0.
assert tmax == 3.5
assert sfreq == 10.
def test_get_dropped_epochs():
raw = create_fake_raw(n_channels=1, n_samples=36, sfreq=10.)
events = np.zeros((4, 3), dtype='int')
events[:, -1] = 1
events[:, 0] = [5, 13, 21, 29]
annot = mne.Annotations([2.], [1.6], ['BAD_'])
try:
raw.set_annotations(annot)
except AttributeError:
raw.annotations = annot
epochs = mne.Epochs(raw, events, event_id=1, tmin=-0.1, tmax=0.6,
preload=True)
assert (np.array([2, 3]) == get_dropped_epochs(epochs)).all()
epochs.drop([0])
assert (np.array([0, 2, 3]) == get_dropped_epochs(epochs)).all()
def test_read_write_info():
data_dir = _get_test_data_dir()
raw = create_fake_raw(n_channels=4, n_samples=5, sfreq=25.)
raw.set_channel_types({'a': 'eeg', 'b': 'ecog', 'c': 'eeg', 'd': 'ecog'})
fname = op.join(data_dir, 'temp_info.hdf5')
if op.isfile(fname):
os.remove(op.join(data_dir, fname))
write_info(fname, raw.info)
info = read_info(fname)
compare_info(info, raw.info)
raw = mne.io.read_raw_fif(op.join(data_dir, 'rest_sample_data-raw.fif'))
write_info(fname, raw.info, overwrite=True)
info = read_info(fname)
compare_info(info, raw.info)
def test_compute_rest_psd():
# test on fake raw with 3 channels
raw = create_fake_raw(n_channels=3, n_samples=26, sfreq=4.)
events = np.array([[5, 0, 1], [10, 0, 2], [16, 0, 1], [21, 0, 2]])
zigzag = np.array([-1, 1, -1, 1])
raw._data[1, 5:9] = zigzag
raw._data[1, 16:20] = zigzag
raw._data[2, 10:14] = zigzag * 2
raw._data[2, 21:25] = zigzag * 2
psd, freqs = compute_rest_psd(raw, events=events, tmin=0., tmax=1.5,
winlen=1., step=1.)
assert psd[1][1, -1] > psd[1][0, -1]
assert psd[2][2, -1] > psd[2][0, -1]
assert psd[2][2, -1] > psd[1][1, -1]
psd2, freqs2 = compute_rest_psd(raw, events=events, event_id=1, tmin=0.,
tmax=1.5, winlen=1., step=1.)
assert (psd2 == psd[1]).all()
assert (freqs2 == freqs).all()
def test_valid_windows():
raw = create_fake_raw(n_channels=1, n_samples=30, sfreq=10.)
# 0 5 10 15 20 25 30
# | | | | | | |
# annot coverage (x): ooooxxxxxooooooxxxooooooooooxx
onset = np.array([0.5, 1.5, 28 / 10])
duration = np.array([0.5, 3 / 10, 2 / 10])
description = ['BAD segment'] * 3
annot = mne.Annotations(onset, duration, description)
try:
raw.set_annotations(annot)
except AttributeError:
raw.annotations = annot
T, F = True, False
answer = valid_windows(raw, winlen=0.4, step=0.2)
should_be = np.array([T, F, F, F, F, T, F, F, F, T, T, T, T, F])
assert (answer == should_be).all()
answer = valid_windows(raw, tmin=0.4, tmax=1.8, winlen=0.4, step=0.2)
should_be = should_be[2:-6]
assert (answer == should_be).all()
def test_silent_mne():
from packaging import version
has_new_mne = version.parse(mne.__version__) >= version.parse('0.19.0')
raw = create_fake_raw(n_channels=2, n_samples=10, sfreq=10.)
pos = np.random.random((2, 3))
if has_new_mne:
ch_pos = {'a': pos[0, :], 'b': pos[1, :]}
mntg = mne.channels.make_dig_montage(ch_pos=ch_pos, coord_frame='head')
else:
mntg = mne.channels.Montage(pos, ['a', 'b'], 'eeg', 'fake')
raw.set_montage(mntg)
# adding new reference channel without position gives a warning:
with pytest.warns(Warning):
mne.add_reference_channels(raw.copy(), ['nose'])
# ... but not when using silent_mne() context manager:
with pytest.warns(None) as record:
with silent_mne():
mne.add_reference_channels(raw.copy(), ['nose'])
# new numpy (>= 1.20) raises warnings on older mne (<= 0.20)
mne_version = version.parse(mne.__version__)
if mne_version >= version.parse('0.21'):
assert len(record) == 0
# with `full_silence` no warnings are raised
# (irrespective of mne and numpy)
with pytest.warns(None) as record:
with silent_mne(full_silence=True):
mne.add_reference_channels(raw.copy(), ['nose'])
warn('annoying warning!', DeprecationWarning)
assert len(record) == 0
def test_get_info():
raw = create_fake_raw(n_channels=2, n_samples=5, sfreq=25.)
assert raw.info == get_info(raw)
assert raw.info == get_info(raw.info)