@pytest.fixture(scope='session')
def azure_windows():
"""Determine if running on Azure Windows."""
return (os.getenv('AZURE_CI_WINDOWS', 'false').lower() == 'true'
and sys.platform.startswith('win'))
@pytest.fixture()
def check_gui_ci(ci_macos, azure_windows):
"""Skip tests that are not reliable on CIs."""
if azure_windows or ci_macos:
pytest.skip('Skipping GUI tests on MacOS CIs and Azure Windows')
@pytest.fixture(scope='session', params=[testing._pytest_param()])
def _evoked():
# This one is session scoped, so be sure not to modify it (use evoked
# instead)
evoked = mne.read_evokeds(fname_evoked,
condition='Left Auditory',
baseline=(None, 0))
evoked.crop(0, 0.2)
return evoked
@pytest.fixture()
def evoked(_evoked):
"""Get evoked data."""
return _evoked.copy()
with pytest.warns(RuntimeWarning, match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw, stc, trans, src, bem, None)
def _make_stc(raw, src):
"""Make a STC."""
seed = 42
sfreq = raw.info['sfreq'] # Hz
tstep = 1. / sfreq
n_samples = len(raw.times) // 10
times = np.arange(0, n_samples) * tstep
stc = simulate_sparse_stc(src, 10, times, random_state=seed)
return stc
@pytest.fixture(scope='function', params=[testing._pytest_param()])
def raw_data():
"""Get some starting data."""
# raw with ECG channel
raw = read_raw_fif(raw_fname).crop(0., 5.0).load_data()
data_picks = pick_types(raw.info, meg=True, eeg=True)
other_picks = pick_types(raw.info, meg=False, stim=True, eog=True)
picks = np.sort(np.concatenate((data_picks[::16], other_picks)))
raw = raw.pick_channels([raw.ch_names[p] for p in picks])
raw.info.normalize_proj()
ecg = RawArray(np.zeros((1, len(raw.times))),
create_info(['ECG 063'], raw.info['sfreq'], 'ecg'))
for key in ('dev_head_t', 'highpass', 'lowpass', 'dig'):
ecg.info[key] = raw.info[key]
raw.add_channels([ecg])
from mne.time_frequency import CrossSpectralDensity
from mne.time_frequency.csd import _sym_mat_to_vector
data_path = testing.data_path(download=False)
fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
fname_fwd_vol = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-vol-7-fwd.fif')
fname_event = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw-eve.fif')
subjects_dir = op.join(data_path, 'subjects')
@pytest.fixture(scope='module', params=[testing._pytest_param()])
def _load_forward():
"""Load forward models."""
fwd_free = mne.read_forward_solution(fname_fwd)
fwd_free = mne.pick_types_forward(fwd_free, meg=True, eeg=False)
fwd_free = mne.convert_forward_solution(fwd_free, surf_ori=False)
fwd_surf = mne.convert_forward_solution(fwd_free, surf_ori=True,
use_cps=False)
fwd_fixed = mne.convert_forward_solution(fwd_free, force_fixed=True,
use_cps=False)
fwd_vol = mne.read_forward_solution(fname_fwd_vol)
return fwd_free, fwd_surf, fwd_fixed, fwd_vol
def _simulate_data(fwd, idx): # Somewhere on the frontal lobe by default
"""Simulate an oscillator on the cortex."""
for ii in range(2):
# should work with (ii=0) or without (ii=1) the interpolator
if ii:
src[0]['interpolator'] = None
img = stc.as_volume(src, mri_resolution=False)
n_on = np.array(img.dataobj).astype(bool).sum()
# was 20 on `master` before gh-5590
# then 44 before gh-7653, which took it back to 20
assert n_on == n_orig
# without the interpolator, this should fail
assert src[0]['interpolator'] is None
with pytest.raises(RuntimeError, match=r'.*src\[0\], .* mri_resolution'):
stc.as_volume(src, mri_resolution=True)
@pytest.fixture(scope='session', params=[testing._pytest_param()])
def _mixed_morph_srcs():
# create a mixed source space
labels_vol = ['Left-Cerebellum-Cortex', 'Right-Cerebellum-Cortex']
src = mne.setup_source_space('sample', spacing='oct3',
add_dist=False, subjects_dir=subjects_dir)
src += mne.setup_volume_source_space(
'sample', mri=fname_aseg, pos=10.0,
volume_label=labels_vol, subjects_dir=subjects_dir,
add_interpolator=True, verbose=True)
# create the destination space
src_fs = mne.read_source_spaces(
op.join(subjects_dir, 'fsaverage', 'bem', 'fsaverage-ico-5-src.fif'))
src_fs += mne.setup_volume_source_space(
'fsaverage', pos=7., volume_label=labels_vol,
subjects_dir=subjects_dir, add_interpolator=False, verbose=True)
from mne.datasets import testing
from mne import (read_label, read_forward_solution, pick_types_forward,
convert_forward_solution)
from mne.label import Label
from mne.simulation import simulate_stc, simulate_sparse_stc, SourceSimulator
from mne.utils import run_tests_if_main, check_version
data_path = testing.data_path(download=False)
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif')
label_names = ['Aud-lh', 'Aud-rh', 'Vis-rh']
subjects_dir = op.join(data_path, 'subjects')
@pytest.fixture(scope="module", params=[testing._pytest_param()])
def _get_fwd_labels():
fwd = read_forward_solution(fname_fwd)
fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=True)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
labels = [
read_label(
op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label))
for label in label_names
]
return fwd, labels
def _get_idx_label_stc(label, stc):
hemi_idx_mapping = dict(lh=0, rh=1)
convert_forward_solution)
from mne.label import Label
from mne.simulation.source import simulate_stc, simulate_sparse_stc
from mne.simulation.source import SourceSimulator
from mne.utils import run_tests_if_main
data_path = testing.data_path(download=False)
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif')
label_names = ['Aud-lh', 'Aud-rh', 'Vis-rh']
subjects_dir = op.join(data_path, 'subjects')
@pytest.fixture(scope="module", params=[testing._pytest_param()])
def _get_fwd_labels():
fwd = read_forward_solution(fname_fwd)
fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=True)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
return fwd, labels
def _get_idx_label_stc(label, stc):
hemi_idx_mapping = dict(lh=0, rh=1)
hemi_idx = hemi_idx_mapping[label.hemi]
idx = np.intersect1d(stc.vertices[hemi_idx], label.vertices)
@pytest.fixture()
def fnirs_evoked():
"""Create an fnirs evoked structure."""
montage = make_standard_montage('biosemi16')
ch_names = montage.ch_names
ch_types = ['eeg'] * 16
info = create_info(ch_names=ch_names, sfreq=20, ch_types=ch_types)
evoked_data = np.random.randn(16, 30)
evoked = EvokedArray(evoked_data, info=info, tmin=-0.2, nave=4)
evoked.set_montage(montage)
evoked.set_channel_types({'Fp1': 'hbo', 'Fp2': 'hbo', 'F4': 'hbo',
'Fz': 'hbo'}, verbose='error')
return evoked
@pytest.fixture(params=[_pytest_param()])
def fnirs_epochs():
"""Create an fnirs epoch structure."""
fname = op.join(data_path(download=False),
'NIRx', 'nirscout', 'nirx_15_2_recording_w_overlap')
raw_intensity = read_raw_nirx(fname, preload=False)
raw_od = optical_density(raw_intensity)
raw_haemo = beer_lambert_law(raw_od)
evts, _ = events_from_annotations(raw_haemo, event_id={'1.0': 1})
evts_dct = {'A': 1}
tn, tx = -1, 2
epochs = Epochs(raw_haemo, evts, event_id=evts_dct, tmin=tn, tmax=tx)
return epochs
# Create one nbclient and reuse it
data_path = testing.data_path(download=False)
ft_data_path = op.join(data_path, 'fieldtrip', 'beamformer')
fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-cov.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
fname_fwd_vol = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-vol-7-fwd.fif')
fname_event = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw-eve.fif')
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', 'Aud-lh.label')
reject = dict(grad=4000e-13, mag=4e-12)
@pytest.fixture(scope='function', params=[testing._pytest_param()])
def _get_bf_data(save_fieldtrip=False):
raw, epochs, evoked, data_cov, _, _, _, _, _, fwd = _get_data(proj=False)
if save_fieldtrip is True:
# raw needs to be saved with all channels and picked in FieldTrip
raw.save(op.join(ft_data_path, 'raw.fif'), overwrite=True)
# src (tris are not available in fwd['src'] once imported into MATLAB)
src = fwd['src'].copy()
mne.write_source_spaces(op.join(ft_data_path, 'src.fif'), src)
# pick gradiometers only:
epochs.pick_types(meg='grad')
evoked.pick_types(meg='grad')