raw_fname = op.join(base_dir, "test_raw.fif")
event_name = op.join(base_dir, "test-eve.fif")
evoked_nf_name = op.join(base_dir, "test-nf-ave.fif")
event_id, tmin, tmax = 1, -0.2, 0.5
event_id_2 = 2
raw = io.Raw(raw_fname, add_eeg_ref=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, eeg=True, stim=True, ecg=True, eog=True, include=["STI 014"], exclude="bads")
reject = dict(grad=1000e-12, mag=4e-12, eeg=80e-6, eog=150e-6)
flat = dict(grad=1e-15, mag=1e-15)
tempdir = _TempDir()
clean_warning_registry() # really clean warning stack
def test_epochs_hash():
"""Test epoch hashing
"""
epochs = Epochs(raw, events, event_id, tmin, tmax)
assert_raises(RuntimeError, epochs.__hash__)
epochs = Epochs(raw, events, event_id, tmin, tmax, preload=True)
assert_equal(hash(epochs), hash(epochs))
epochs_2 = Epochs(raw, events, event_id, tmin, tmax, preload=True)
assert_equal(hash(epochs), hash(epochs_2))
# do NOT use assert_equal here, failing output is terrible
assert_true(pickle.dumps(epochs) == pickle.dumps(epochs_2))
epochs_2._data[0, 0, 0] -= 1
def test_compute_proj_epochs():
"""Test SSP computation on epochs"""
tempdir = _TempDir()
event_id, tmin, tmax = 1, -0.2, 0.3
raw = Raw(raw_fname, preload=True)
events = read_events(event_fname)
bad_ch = 'MEG 2443'
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
exclude=[])
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=None, proj=False)
evoked = epochs.average()
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
write_proj(op.join(tempdir, 'test-proj.fif.gz'), projs)
for p_fname in [proj_fname, proj_gz_fname,
op.join(tempdir, 'test-proj.fif.gz')]:
projs2 = read_proj(p_fname)
assert_true(len(projs) == len(projs2))
for p1, p2 in zip(projs, projs2):
assert_true(p1['desc'] == p2['desc'])
assert_true(p1['data']['col_names'] == p2['data']['col_names'])
assert_true(p1['active'] == p2['active'])
# compare with sign invariance
p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0])
p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0])
if bad_ch in p1['data']['col_names']:
bad = p1['data']['col_names'].index('MEG 2443')
mask = np.ones(p1_data.size, dtype=np.bool)
mask[bad] = False
p1_data = p1_data[:, mask]
p2_data = p2_data[:, mask]
corr = np.corrcoef(p1_data, p2_data)[0, 1]
assert_array_almost_equal(corr, 1.0, 5)
if p2['explained_var']:
assert_array_almost_equal(p1['explained_var'],
p2['explained_var'])
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
epochs.info['projs'] += projs
evoked = epochs.average()
evoked.save(op.join(tempdir, 'foo-ave.fif'))
projs = read_proj(proj_fname)
projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
assert_true(len(projs_evoked) == 2)
# XXX : test something
# test parallelization
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2,
desc_prefix='foobar')
assert_true(all('foobar' in x['desc'] for x in projs))
projs = activate_proj(projs)
proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)
# test warnings on bad filenames
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
write_proj(proj_badname, projs)
read_proj(proj_badname)
assert_naming(w, 'test_proj.py', 2)
def test_compute_proj_epochs():
"""Test SSP computation on epochs"""
tempdir = _TempDir()
event_id, tmin, tmax = 1, -0.2, 0.3
raw = Raw(raw_fname, preload=True)
events = read_events(event_fname)
bad_ch = 'MEG 2443'
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
exclude=[])
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=None, proj=False)
evoked = epochs.average()
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
write_proj(op.join(tempdir, 'test-proj.fif.gz'), projs)
for p_fname in [proj_fname, proj_gz_fname,
op.join(tempdir, 'test-proj.fif.gz')]:
projs2 = read_proj(p_fname)
assert_true(len(projs) == len(projs2))
for p1, p2 in zip(projs, projs2):
assert_true(p1['desc'] == p2['desc'])
assert_true(p1['data']['col_names'] == p2['data']['col_names'])
assert_true(p1['active'] == p2['active'])
# compare with sign invariance
p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0])
p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0])
if bad_ch in p1['data']['col_names']:
bad = p1['data']['col_names'].index('MEG 2443')
mask = np.ones(p1_data.size, dtype=np.bool)
mask[bad] = False
p1_data = p1_data[:, mask]
p2_data = p2_data[:, mask]
corr = np.corrcoef(p1_data, p2_data)[0, 1]
assert_array_almost_equal(corr, 1.0, 5)
if p2['explained_var']:
assert_array_almost_equal(p1['explained_var'],
p2['explained_var'])
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
epochs.info['projs'] += projs
evoked = epochs.average()
evoked.save(op.join(tempdir, 'foo-ave.fif'))
projs = read_proj(proj_fname)
projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
assert_true(len(projs_evoked) == 2)
# XXX : test something
# test parallelization
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2,
desc_prefix='foobar')
assert_true(all('foobar' in x['desc'] for x in projs))
projs = activate_proj(projs)
proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)
# test warnings on bad filenames
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
write_proj(proj_badname, projs)
read_proj(proj_badname)
assert_naming(w, 'test_proj.py', 2)
events = read_events(event_name)
picks = fiff.pick_types(raw.info,
meg=True,
eeg=True,
stim=True,
ecg=True,
eog=True,
include=['STI 014'],
exclude='bads')
reject = dict(grad=1000e-12, mag=4e-12, eeg=80e-6, eog=150e-6)
flat = dict(grad=1e-15, mag=1e-15)
tempdir = _TempDir()
clean_warning_registry() # really clean warning stack
def test_event_ordering():
"""Test event order"""
events2 = events.copy()
np.random.shuffle(events2)
for ii, eve in enumerate([events, events2]):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', RuntimeWarning)
Epochs(raw,
eve,
event_id,
tmin,
tmax,
baseline=(None, 0),
# tests in a full nosetest:
warnings.simplefilter("always") # ensure we can verify expected warnings
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')
label = 'Aud-lh'
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)
# bit of a hack to deal with old scipy/numpy throwing warnings in tests
clean_warning_registry()
def read_forward_solution_meg(*args, **kwargs):
fwd = mne.read_forward_solution(*args, **kwargs)
return mne.pick_types_forward(fwd, meg=True, eeg=False)
def _get_data(tmin=-0.11, tmax=0.15, read_all_forward=True, compute_csds=True):
"""Read in data used in tests
"""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)[:10]
raw = mne.io.Raw(fname_raw, preload=False)
forward = mne.read_forward_solution(fname_fwd)
if read_all_forward:
# tests in a full nosetest:
warnings.simplefilter("always") # ensure we can verify expected warnings
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')
label = 'Aud-lh'
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)
# bit of a hack to deal with old scipy/numpy throwing warnings in tests
clean_warning_registry()
def read_forward_solution_meg(*args, **kwargs):
fwd = mne.read_forward_solution(*args, **kwargs)
return mne.pick_types_forward(fwd, meg=True, eeg=False)
def _get_data(tmin=-0.11, tmax=0.15, read_all_forward=True, compute_csds=True):
"""Read in data used in tests
"""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)[:10]
raw = mne.io.read_raw_fif(fname_raw, preload=False)
raw.add_proj([], remove_existing=True) # we'll subselect so remove proj
forward = mne.read_forward_solution(fname_fwd)
