def test_compute_proj_epochs():
"""Test SSP computation on epochs"""
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, 'proj.fif.gz'), projs)
for p_fname in [proj_fname, proj_gz_fname,
op.join(tempdir, '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)
# 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.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)
projs = activate_proj(projs)
proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)
def test_compute_proj_raw():
"""Test SSP computation on raw"""
# Test that the raw projectors work
raw_time = 2.5 # Do shorter amount for speed
raw = Raw(raw_fname, preload=True).crop(0, raw_time, False)
for ii in (0.25, 0.5, 1, 2):
with warnings.catch_warnings(record=True) as w:
projs = compute_proj_raw(raw, duration=ii - 0.1, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
assert_true(len(w) == 1)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
raw.info['projs'] += projs
raw.save(op.join(tempdir, 'foo_%d_raw.fif' % ii), overwrite=True)
# Test that purely continuous (no duration) raw projection works
with warnings.catch_warnings(record=True) as w:
projs = compute_proj_raw(raw, duration=None, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
assert_true(len(w) == 1)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
raw.info['projs'] += projs
raw.save(op.join(tempdir, 'foo_rawproj_continuous_raw.fif'))
# test resampled-data projector, upsampling instead of downsampling
# here to save an extra filtering (raw would have to be LP'ed to be equiv)
raw_resamp = cp.deepcopy(raw)
raw_resamp.resample(raw.info['sfreq'] * 2, n_jobs=2)
with warnings.catch_warnings(record=True) as w:
projs = compute_proj_raw(raw_resamp, duration=None, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
projs = activate_proj(projs)
proj_new, _, _ = make_projector(projs, raw.ch_names, bads=[])
assert_array_almost_equal(proj_new, proj, 4)
# test with bads
raw.load_bad_channels(bads_fname) # adds 2 bad mag channels
with warnings.catch_warnings(record=True) as w:
projs = compute_proj_raw(raw, n_grad=0, n_mag=0, n_eeg=1)
# test that bad channels can be excluded
proj, nproj, U = make_projector(projs, raw.ch_names,
bads=raw.ch_names)
assert_array_almost_equal(proj, np.eye(len(raw.ch_names)))
def test_compute_proj_raw():
"""Test SSP computation on raw."""
tempdir = _TempDir()
# Test that the raw projectors work
raw_time = 2.5 # Do shorter amount for speed
raw = read_raw_fif(raw_fname).crop(0, raw_time)
raw.load_data()
for ii in (0.25, 0.5, 1, 2):
with pytest.warns(RuntimeWarning, match='Too few samples'):
projs = compute_proj_raw(raw, duration=ii - 0.1, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])
assert nproj == 2
assert U.shape[1] == 2
# test that you can save them
raw.info['projs'] += projs
raw.save(op.join(tempdir, 'foo_%d_raw.fif' % ii), overwrite=True)
# Test that purely continuous (no duration) raw projection works
with pytest.warns(RuntimeWarning, match='Too few samples'):
projs = compute_proj_raw(raw, duration=None, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])
assert nproj == 2
assert U.shape[1] == 2
# test that you can save them
raw.info['projs'] += projs
raw.save(op.join(tempdir, 'foo_rawproj_continuous_raw.fif'))
# test resampled-data projector, upsampling instead of downsampling
# here to save an extra filtering (raw would have to be LP'ed to be equiv)
raw_resamp = cp.deepcopy(raw)
raw_resamp.resample(raw.info['sfreq'] * 2, n_jobs=2, npad='auto')
projs = compute_proj_raw(raw_resamp, duration=None, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
projs = activate_proj(projs)
proj_new, _, _ = make_projector(projs, raw.ch_names, bads=[])
assert_array_almost_equal(proj_new, proj, 4)
# test with bads
raw.load_bad_channels(bads_fname) # adds 2 bad mag channels
with pytest.warns(RuntimeWarning, match='Too few samples'):
projs = compute_proj_raw(raw, n_grad=0, n_mag=0, n_eeg=1)
# test that bad channels can be excluded
proj, nproj, U = make_projector(projs, raw.ch_names,
bads=raw.ch_names)
assert_array_almost_equal(proj, np.eye(len(raw.ch_names)))
def _prepare_beamformer_input(info, forward, label, picks, pick_ori):
"""Input preparation common for all beamformer functions.
Check input values, prepare channel list and gain matrix. For documentation
of parameters, please refer to _apply_lcmv.
"""
is_free_ori = forward['source_ori'] == FIFF.FIFFV_MNE_FREE_ORI
if pick_ori in ['normal', 'max-power'] and not is_free_ori:
raise ValueError('Normal or max-power orientation can only be picked '
'when a forward operator with free orientation is '
'used.')
if pick_ori == 'normal' and not forward['surf_ori']:
# XXX eventually this could just call convert_forward_solution
raise ValueError('Normal orientation can only be picked when a '
'forward operator oriented in surface coordinates is '
'used.')
if pick_ori == 'normal' and not forward['src'][0]['type'] == 'surf':
raise ValueError('Normal orientation can only be picked when a '
'forward operator with a surface-based source space '
'is used.')
# Restrict forward solution to selected channels
info_ch_names = [ch['ch_name'] for ch in info['chs']]
ch_names = [info_ch_names[k] for k in picks]
fwd_ch_names = forward['sol']['row_names']
# Keep channels in forward present in info:
fwd_ch_names = [ch for ch in fwd_ch_names if ch in info_ch_names]
# This line takes ~48 milliseconds on kernprof
# forward = pick_channels_forward(forward, fwd_ch_names, verbose='ERROR')
picks_forward = [fwd_ch_names.index(ch) for ch in ch_names]
# Get gain matrix (forward operator)
if label is not None:
vertno, src_sel = label_src_vertno_sel(label, forward['src'])
if is_free_ori:
src_sel = 3 * src_sel
src_sel = np.c_[src_sel, src_sel + 1, src_sel + 2]
src_sel = src_sel.ravel()
G = forward['sol']['data'][:, src_sel]
else:
vertno = _get_vertno(forward['src'])
G = forward['sol']['data']
# Apply SSPs
proj, ncomp, _ = make_projector(info['projs'], fwd_ch_names)
if info['projs']:
G = np.dot(proj, G)
# Pick after applying the projections
G = G[picks_forward]
proj = proj[np.ix_(picks_forward, picks_forward)]
return is_free_ori, ch_names, proj, vertno, G
def test_compute_proj_parallel():
"""Test computation of ExG projectors using parallelization"""
raw_0 = Raw(raw_fname).crop(0, 10, copy=False)
raw_0.load_data()
raw = raw_0.copy()
projs, _ = compute_proj_eog(raw,
n_mag=2,
n_grad=2,
n_eeg=2,
bads=['MEG 2443'],
average=False,
avg_ref=True,
no_proj=False,
n_jobs=1,
l_freq=None,
h_freq=None,
reject=None,
tmax=dur_use,
filter_length=6000)
raw_2 = raw_0.copy()
projs_2, _ = compute_proj_eog(raw_2,
n_mag=2,
n_grad=2,
n_eeg=2,
bads=['MEG 2443'],
average=False,
avg_ref=True,
no_proj=False,
n_jobs=2,
l_freq=None,
h_freq=None,
reject=None,
tmax=dur_use,
filter_length=6000)
projs = activate_proj(projs)
projs_2 = activate_proj(projs_2)
projs, _, _ = make_projector(projs,
raw_2.info['ch_names'],
bads=['MEG 2443'])
projs_2, _, _ = make_projector(projs_2,
raw_2.info['ch_names'],
bads=['MEG 2443'])
assert_array_almost_equal(projs, projs_2, 10)
def test_compute_proj_parallel(short_raw):
"""Test computation of ExG projectors using parallelization."""
short_raw = short_raw.copy().pick(('eeg', 'eog')).resample(100)
raw = short_raw.copy()
with pytest.warns(RuntimeWarning, match='Attenuation'):
projs, _ = compute_proj_eog(raw,
n_eeg=2,
bads=raw.ch_names[1:2],
average=False,
avg_ref=True,
no_proj=False,
n_jobs=1,
l_freq=None,
h_freq=None,
reject=None,
tmax=dur_use,
filter_length=100)
raw_2 = short_raw.copy()
with pytest.warns(RuntimeWarning, match='Attenuation'):
projs_2, _ = compute_proj_eog(raw_2,
n_eeg=2,
bads=raw.ch_names[1:2],
average=False,
avg_ref=True,
no_proj=False,
n_jobs=2,
l_freq=None,
h_freq=None,
reject=None,
tmax=dur_use,
filter_length=100)
projs = activate_proj(projs)
projs_2 = activate_proj(projs_2)
projs, _, _ = make_projector(projs,
raw_2.info['ch_names'],
bads=['MEG 2443'])
projs_2, _, _ = make_projector(projs_2,
raw_2.info['ch_names'],
bads=['MEG 2443'])
assert_array_almost_equal(projs, projs_2, 10)
def test_compute_proj_parallel():
"""Test computation of ExG projectors using parallelization"""
raw_0 = Raw(raw_fname).crop(0, 10, copy=False)
raw_0.load_data()
raw = raw_0.copy()
projs, _ = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
bads=['MEG 2443'], average=False,
avg_ref=True, no_proj=False, n_jobs=1,
l_freq=None, h_freq=None, reject=None,
tmax=dur_use)
raw_2 = raw_0.copy()
projs_2, _ = compute_proj_eog(raw_2, n_mag=2, n_grad=2, n_eeg=2,
bads=['MEG 2443'], average=False,
avg_ref=True, no_proj=False, n_jobs=2,
l_freq=None, h_freq=None, reject=None,
tmax=dur_use)
projs = activate_proj(projs)
projs_2 = activate_proj(projs_2)
projs, _, _ = make_projector(projs, raw_2.info['ch_names'],
bads=['MEG 2443'])
projs_2, _, _ = make_projector(projs_2, raw_2.info['ch_names'],
bads=['MEG 2443'])
assert_array_almost_equal(projs, projs_2, 10)
def test_inverse_residual(evoked):
"""Test MNE inverse application."""
# use fname_inv as it will be faster than fname_full (fewer verts and chs)
evoked = evoked.pick_types()
inv = read_inverse_operator(fname_inv_fixed_depth)
fwd = read_forward_solution(fname_fwd)
pick_channels_forward(fwd, evoked.ch_names, copy=False)
fwd = convert_forward_solution(fwd, force_fixed=True, surf_ori=True)
matcher = re.compile(r'.* ([0-9]?[0-9]?[0-9]?\.[0-9])% variance.*')
for method in ('MNE', 'dSPM', 'sLORETA'):
with catch_logging() as log:
stc, residual = apply_inverse(evoked,
inv,
method=method,
return_residual=True,
verbose=True)
log = log.getvalue()
match = matcher.match(log.replace('\n', ' '))
assert match is not None
match = float(match.group(1))
assert 45 < match < 50
if method == 'MNE': # must be first!
recon = apply_forward(fwd, stc, evoked.info)
proj_op = make_projector(evoked.info['projs'], evoked.ch_names)[0]
recon.data[:] = np.dot(proj_op, recon.data)
residual_fwd = evoked.copy()
residual_fwd.data -= recon.data
corr = np.corrcoef(residual_fwd.data.ravel(), residual.data.ravel())[0,
1]
assert corr > 0.999
with catch_logging() as log:
_, residual = apply_inverse(evoked,
inv,
0.,
'MNE',
return_residual=True,
verbose=True)
log = log.getvalue()
match = matcher.match(log.replace('\n', ' '))
assert match is not None
match = float(match.group(1))
assert match == 100.
assert_array_less(np.abs(residual.data), 1e-15)
# Degenerate: we don't have the right representation for eLORETA for this
with pytest.raises(ValueError, match='eLORETA does not .* support .*'):
apply_inverse(evoked, inv, method="eLORETA", return_residual=True)
def test_inverse_residual(evoked, method):
"""Test MNE inverse application."""
# use fname_inv as it will be faster than fname_full (fewer verts and chs)
evoked = evoked.pick_types(meg=True)
inv = read_inverse_operator(fname_inv_fixed_depth)
fwd = read_forward_solution(fname_fwd)
pick_channels_forward(fwd, evoked.ch_names, copy=False)
fwd = convert_forward_solution(fwd, force_fixed=True, surf_ori=True)
matcher = re.compile(r'.* ([0-9]?[0-9]?[0-9]?\.[0-9])% variance.*')
# make it complex to ensure we handle it properly
evoked.data = 1j * evoked.data
with catch_logging() as log:
stc, residual = apply_inverse(
evoked, inv, method=method, return_residual=True, verbose=True)
# revert the complex-ification (except STC, allow that to be complex still)
assert_array_equal(residual.data.real, 0)
residual.data = (-1j * residual.data).real
evoked.data = (-1j * evoked.data).real
# continue testing
log = log.getvalue()
match = matcher.match(log.replace('\n', ' '))
assert match is not None
match = float(match.group(1))
assert 45 < match < 50
if method not in ('dSPM', 'sLORETA'):
# revert effects of STC being forced to be complex
recon = apply_forward(fwd, stc, evoked.info)
recon.data = (-1j * recon.data).real
proj_op = make_projector(evoked.info['projs'], evoked.ch_names)[0]
recon.data[:] = np.dot(proj_op, recon.data)
residual_fwd = evoked.copy()
residual_fwd.data -= recon.data
corr = np.corrcoef(residual_fwd.data.ravel(),
residual.data.ravel())[0, 1]
assert corr > 0.999
if method != 'sLORETA': # XXX divide by zero error
with catch_logging() as log:
_, residual = apply_inverse(
evoked, inv, 0., method, return_residual=True, verbose=True)
log = log.getvalue()
match = matcher.match(log.replace('\n', ' '))
assert match is not None
match = float(match.group(1))
assert match == 100.
assert_array_less(np.abs(residual.data), 1e-15)
def test_inverse_residual():
"""Test MNE inverse application."""
# use fname_inv as it will be faster than fname_full (fewer verts and chs)
evoked = _get_evoked().pick_types()
inv = read_inverse_operator(fname_inv_fixed_depth)
fwd = read_forward_solution(fname_fwd)
fwd = convert_forward_solution(fwd, force_fixed=True, surf_ori=True)
fwd = pick_channels_forward(fwd, evoked.ch_names)
matcher = re.compile(r'.* ([0-9]?[0-9]?[0-9]?\.[0-9])% variance.*')
for method in ('MNE', 'dSPM', 'sLORETA'):
with catch_logging() as log:
stc, residual = apply_inverse(
evoked, inv, method=method, return_residual=True, verbose=True)
log = log.getvalue()
match = matcher.match(log.replace('\n', ' '))
assert match is not None
match = float(match.group(1))
assert 45 < match < 50
if method == 'MNE': # must be first!
recon = apply_forward(fwd, stc, evoked.info)
proj_op = make_projector(evoked.info['projs'], evoked.ch_names)[0]
recon.data[:] = np.dot(proj_op, recon.data)
residual_fwd = evoked.copy()
residual_fwd.data -= recon.data
corr = np.corrcoef(residual_fwd.data.ravel(),
residual.data.ravel())[0, 1]
assert corr > 0.999
with catch_logging() as log:
_, residual = apply_inverse(
evoked, inv, 0., 'MNE', return_residual=True, verbose=True)
log = log.getvalue()
match = matcher.match(log.replace('\n', ' '))
assert match is not None
match = float(match.group(1))
assert match == 100.
assert_array_less(np.abs(residual.data), 1e-15)
# Degenerate: we don't have the right representation for eLORETA for this
with pytest.raises(ValueError, match='eLORETA does not .* support .*'):
apply_inverse(evoked, inv, method="eLORETA", return_residual=True)
def test_compute_proj_epochs(tmp_path):
"""Test SSP computation on epochs."""
tempdir = str(tmp_path)
event_id, tmin, tmax = 1, -0.2, 0.3
raw = read_raw_fif(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 len(projs) == len(projs2)
for p1, p2 in zip(projs, projs2):
assert p1['desc'] == p2['desc']
assert p1['data']['col_names'] == p2['data']['col_names']
assert 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=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 nproj == 2
assert U.shape[1] == 2
# test that you can save them
with epochs.info._unlock():
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 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=1,
desc_prefix='foobar')
assert 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
proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-proj.fif'):
write_proj(proj_badname, projs)
with pytest.warns(RuntimeWarning, match='-proj.fif'):
read_proj(proj_badname)
# bad inputs
fname = op.join(tempdir, 'out-proj.fif')
with pytest.raises(TypeError, match='projs'):
write_proj(fname, 'foo')
with pytest.raises(TypeError, match=r'projs\[0\] must be .*'):
write_proj(fname, ['foo'])
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 = read_raw_fif(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
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 = read_raw_fif(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 len(projs) == len(projs2)
for p1, p2 in zip(projs, projs2):
assert p1['desc'] == p2['desc']
assert p1['data']['col_names'] == p2['data']['col_names']
assert 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 nproj == 2
assert 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 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=1,
desc_prefix='foobar')
assert 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
proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-proj.fif'):
write_proj(proj_badname, projs)
with pytest.warns(RuntimeWarning, match='-proj.fif'):
read_proj(proj_badname)
