def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model."""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess(['mne_forward_solution', '--meg', '--eeg',
'--meas', fname_raw, '--src', fname_src_small,
'--mri', fname_trans, '--fwd', out_name])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=True, verbose=True)
_compare_forwards(fwd, fwd_py, 366, 108,
meg_rtol=5e-1, meg_atol=1e-6,
eeg_rtol=5e-1, eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0, rtol=1e-3)
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model"""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, "sample-oct-2-src.fif")
src = setup_source_space("sample", fname_src_small, "oct2", subjects_dir=subjects_dir, add_dist=False)
out_name = op.join(temp_dir, "tmp-fwd.fif")
run_subprocess(
[
"mne_forward_solution",
"--meg",
"--eeg",
"--meas",
fname_raw,
"--src",
fname_src_small,
"--mri",
fname_trans,
"--fwd",
out_name,
]
)
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=True, verbose=True)
_compare_forwards(fwd, fwd_py, 366, 108, meg_rtol=5e-1, meg_atol=1e-6, eeg_rtol=5e-1, eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_["sol"]["data"].ravel(), fwd_py_["sol"]["data"].ravel())[0, 1], 1.0, rtol=1e-3)
def compute_correlation_distance_matix(fwd):
print(' Computing correlation distance matrix...')
distance_matrix = np.zeros(
(fwd['sol']['data'].shape[1], fwd['sol']['data'].shape[1]))
n_ch_tot = fwd['info']['nchan']
ch_types = set(map(lambda x: channel_type(fwd['info'], x),
range(n_ch_tot)))
for _t in ch_types:
print(' Using {} sensors for computation...'.format(_t))
if _t in ['mag', 'grad', 'planar1', 'planar2']:
_fwd_t = pick_types_forward(fwd, meg=_t, ref_meg=False)
elif _t == 'eeg':
_fwd_t = pick_types_forward(fwd, eeg=_t, ref_meg=False)
else:
raise NotImplementedError
n_ch_t = _fwd_t['info']['nchan']
_lf_t = _fwd_t['sol']['data']
_dm_t = ssd.cdist(_lf_t.T, _lf_t.T, metric='correlation')
distance_matrix += (n_ch_t / n_ch_tot) * _dm_t
print(' [done]')
return distance_matrix
def test_psf_ctf():
"""Test computation of PSFs and CTFs for linear estimators
"""
inverse_operator = read_inverse_operator(fname_inv)
forward = read_forward_solution(fname_fwd, force_fixed=False,
surf_ori=True)
forward = pick_types_forward(forward, meg=True, eeg=False)
labels = [mne.read_label(ss) for ss in fname_label]
method = 'MNE'
n_svd_comp = 2
# Test PSFs (then CTFs)
for mode in ('sum', 'svd'):
stc_psf, psf_ev = point_spread_function(inverse_operator,
forward,
method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp)
n_vert, n_samples = stc_psf.shape
should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
inverse_operator['src'][0]['vertno'].shape[0])
if mode == 'svd':
should_n_samples = len(labels) * n_svd_comp + 1
else:
should_n_samples = len(labels) + 1
assert_true(n_vert == should_n_vert)
assert_true(n_samples == should_n_samples)
n_chan, n_samples = psf_ev.data.shape
assert_true(n_chan == forward['nchan'])
forward = read_forward_solution(fname_fwd, force_fixed=True, surf_ori=True)
forward = pick_types_forward(forward, meg=True, eeg=False)
# Test CTFs
for mode in ('sum', 'svd'):
stc_ctf = cross_talk_function(inverse_operator, forward,
labels, method=method,
lambda2=lambda2,
signed=False, mode=mode,
n_svd_comp=n_svd_comp)
n_vert, n_samples = stc_ctf.shape
should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
inverse_operator['src'][0]['vertno'].shape[0])
if mode == 'svd':
should_n_samples = len(labels) * n_svd_comp + 1
else:
should_n_samples = len(labels) + 1
assert_true(n_vert == should_n_vert)
assert_true(n_samples == should_n_samples)
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model."""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample',
'oct2',
subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess([
'mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src',
fname_src_small, '--mri', fname_trans, '--fwd', out_name
])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=True,
verbose=True)
_compare_forwards(fwd,
fwd_py,
366,
108,
meg_rtol=5e-1,
meg_atol=1e-6,
eeg_rtol=5e-1,
eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0,
rtol=1e-3)
# Number of layers in the sphere model doesn't matter for MEG
# (as long as no sources are omitted due to distance)
assert len(sphere['layers']) == 4
fwd = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=False)
sphere_1 = make_sphere_model(head_radius=None)
assert len(sphere_1['layers']) == 0
assert_array_equal(sphere['r0'], sphere_1['r0'])
fwd_1 = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=False)
_compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12)
def test_restrict_forward_to_stc():
"""Test restriction of source space to source SourceEstimate
"""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd,
surf_ori=True,
force_fixed=True,
use_cps=True)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_true(isinstance(fwd_out, Forward))
assert_equal(fwd_out['sol']['ncol'], 20)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_equal(fwd_out['sol']['ncol'], 60)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
# Test saving the restricted forward object. This only works if all fields
# are properly accounted for.
temp_dir = _TempDir()
fname_copy = op.join(temp_dir, 'copy-fwd.fif')
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
write_forward_solution(fname_copy, fwd_out, overwrite=True)
fwd_out_read = read_forward_solution(fname_copy)
fwd_out_read = convert_forward_solution(fwd_out_read,
surf_ori=True,
force_fixed=False)
compare_forwards(fwd_out, fwd_out_read)
def test_restrict_forward_to_label():
"""Test restriction of source space to label
"""
fwd = read_forward_solution(fname_meeg, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True)
label_path = op.join(data_path, 'MEG', 'sample', 'labels')
labels = ['Aud-lh', 'Vis-rh']
label_lh = read_label(op.join(label_path, labels[0] + '.label'))
label_rh = read_label(op.join(label_path, labels[1] + '.label'))
fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])
src_sel_lh = np.intersect1d(fwd['src'][0]['vertno'], label_lh.vertices)
src_sel_lh = np.searchsorted(fwd['src'][0]['vertno'], src_sel_lh)
vertno_lh = fwd['src'][0]['vertno'][src_sel_lh]
nuse_lh = fwd['src'][0]['nuse']
src_sel_rh = np.intersect1d(fwd['src'][1]['vertno'], label_rh.vertices)
src_sel_rh = np.searchsorted(fwd['src'][1]['vertno'], src_sel_rh)
vertno_rh = fwd['src'][1]['vertno'][src_sel_rh]
src_sel_rh += nuse_lh
assert_equal(fwd_out['sol']['ncol'], len(src_sel_lh) + len(src_sel_rh))
assert_equal(fwd_out['src'][0]['nuse'], len(src_sel_lh))
assert_equal(fwd_out['src'][1]['nuse'], len(src_sel_rh))
assert_equal(fwd_out['src'][0]['vertno'], vertno_lh)
assert_equal(fwd_out['src'][1]['vertno'], vertno_rh)
fwd = read_forward_solution(fname_meeg, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
label_path = op.join(data_path, 'MEG', 'sample', 'labels')
labels = ['Aud-lh', 'Vis-rh']
label_lh = read_label(op.join(label_path, labels[0] + '.label'))
label_rh = read_label(op.join(label_path, labels[1] + '.label'))
fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])
src_sel_lh = np.intersect1d(fwd['src'][0]['vertno'], label_lh.vertices)
src_sel_lh = np.searchsorted(fwd['src'][0]['vertno'], src_sel_lh)
vertno_lh = fwd['src'][0]['vertno'][src_sel_lh]
nuse_lh = fwd['src'][0]['nuse']
src_sel_rh = np.intersect1d(fwd['src'][1]['vertno'], label_rh.vertices)
src_sel_rh = np.searchsorted(fwd['src'][1]['vertno'], src_sel_rh)
vertno_rh = fwd['src'][1]['vertno'][src_sel_rh]
src_sel_rh += nuse_lh
assert_equal(fwd_out['sol']['ncol'],
3 * (len(src_sel_lh) + len(src_sel_rh)))
assert_equal(fwd_out['src'][0]['nuse'], len(src_sel_lh))
assert_equal(fwd_out['src'][1]['nuse'], len(src_sel_rh))
assert_equal(fwd_out['src'][0]['vertno'], vertno_lh)
assert_equal(fwd_out['src'][1]['vertno'], vertno_rh)
def test_restrict_forward_to_label():
"""Test restriction of source space to label
"""
fwd = read_forward_solution(fname_meeg, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True)
label_path = op.join(data_path, 'MEG', 'sample', 'labels')
labels = ['Aud-lh', 'Vis-rh']
label_lh = read_label(op.join(label_path, labels[0] + '.label'))
label_rh = read_label(op.join(label_path, labels[1] + '.label'))
fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])
src_sel_lh = np.intersect1d(fwd['src'][0]['vertno'], label_lh.vertices)
src_sel_lh = np.searchsorted(fwd['src'][0]['vertno'], src_sel_lh)
vertno_lh = fwd['src'][0]['vertno'][src_sel_lh]
nuse_lh = fwd['src'][0]['nuse']
src_sel_rh = np.intersect1d(fwd['src'][1]['vertno'], label_rh.vertices)
src_sel_rh = np.searchsorted(fwd['src'][1]['vertno'], src_sel_rh)
vertno_rh = fwd['src'][1]['vertno'][src_sel_rh]
src_sel_rh += nuse_lh
assert_equal(fwd_out['sol']['ncol'], len(src_sel_lh) + len(src_sel_rh))
assert_equal(fwd_out['src'][0]['nuse'], len(src_sel_lh))
assert_equal(fwd_out['src'][1]['nuse'], len(src_sel_rh))
assert_equal(fwd_out['src'][0]['vertno'], vertno_lh)
assert_equal(fwd_out['src'][1]['vertno'], vertno_rh)
fwd = read_forward_solution(fname_meeg, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
label_path = op.join(data_path, 'MEG', 'sample', 'labels')
labels = ['Aud-lh', 'Vis-rh']
label_lh = read_label(op.join(label_path, labels[0] + '.label'))
label_rh = read_label(op.join(label_path, labels[1] + '.label'))
fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])
src_sel_lh = np.intersect1d(fwd['src'][0]['vertno'], label_lh.vertices)
src_sel_lh = np.searchsorted(fwd['src'][0]['vertno'], src_sel_lh)
vertno_lh = fwd['src'][0]['vertno'][src_sel_lh]
nuse_lh = fwd['src'][0]['nuse']
src_sel_rh = np.intersect1d(fwd['src'][1]['vertno'], label_rh.vertices)
src_sel_rh = np.searchsorted(fwd['src'][1]['vertno'], src_sel_rh)
vertno_rh = fwd['src'][1]['vertno'][src_sel_rh]
src_sel_rh += nuse_lh
assert_equal(fwd_out['sol']['ncol'],
3 * (len(src_sel_lh) + len(src_sel_rh)))
assert_equal(fwd_out['src'][0]['nuse'], len(src_sel_lh))
assert_equal(fwd_out['src'][1]['nuse'], len(src_sel_rh))
assert_equal(fwd_out['src'][0]['vertno'], vertno_lh)
assert_equal(fwd_out['src'][1]['vertno'], vertno_rh)
def test_restrict_forward_to_stc(tmpdir):
"""Test restriction of source space to source SourceEstimate."""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd,
surf_ori=True,
force_fixed=True,
use_cps=True)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert (isinstance(fwd_out, Forward))
assert_equal(fwd_out['sol']['ncol'], 20)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_equal(fwd_out['sol']['ncol'], 60)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
# Test saving the restricted forward object. This only works if all fields
# are properly accounted for.
fname_copy = tmpdir.join('copy-fwd.fif')
with pytest.warns(RuntimeWarning, match='stored on disk'):
write_forward_solution(fname_copy, fwd_out, overwrite=True)
fwd_out_read = read_forward_solution(fname_copy)
fwd_out_read = convert_forward_solution(fwd_out_read,
surf_ori=True,
force_fixed=False)
compare_forwards(fwd_out, fwd_out_read)
def test_restrict_forward_to_stc():
"""Test restriction of source space to source SourceEstimate
"""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True,
use_cps=True)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_true(isinstance(fwd_out, Forward))
assert_equal(fwd_out['sol']['ncol'], 20)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_equal(fwd_out['sol']['ncol'], 60)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
# Test saving the restricted forward object. This only works if all fields
# are properly accounted for.
temp_dir = _TempDir()
fname_copy = op.join(temp_dir, 'copy-fwd.fif')
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
write_forward_solution(fname_copy, fwd_out, overwrite=True)
fwd_out_read = read_forward_solution(fname_copy)
fwd_out_read = convert_forward_solution(fwd_out_read, surf_ori=True,
force_fixed=False)
compare_forwards(fwd_out, fwd_out_read)
def test_gamma_map():
"""Test Gamma MAP inverse"""
forward = read_forward_solution(fname_fwd)
forward = convert_forward_solution(forward, surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0),
proj=False)
evoked.resample(50, npad=100)
evoked.crop(tmin=0.1, tmax=0.16) # crop to window around peak
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info)
alpha = 0.5
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=True, update_mode=1)
_check_stc(stc, evoked, 68477)
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1)
_check_stc(stc, evoked, 82010)
dips = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1,
return_as_dipoles=True)
assert_true(isinstance(dips[0], Dipole))
stc_dip = make_stc_from_dipoles(dips, forward['src'])
_check_stcs(stc, stc_dip)
# force fixed orientation
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=2,
loose=0, return_residual=False)
_check_stc(stc, evoked, 85739, 20)
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 test_gamma_map():
"""Test Gamma MAP inverse"""
forward = read_forward_solution(fname_fwd, force_fixed=False,
surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0),
proj=False)
evoked.resample(50, npad=100)
evoked.crop(tmin=0.1, tmax=0.16) # crop to nice window near samp border
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info)
alpha = 0.5
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=True, update_mode=1)
_check_stc(stc, evoked, 68477)
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1)
_check_stc(stc, evoked, 82010)
# force fixed orientation
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=2,
loose=None, return_residual=False)
_check_stc(stc, evoked, 85739, 20)
def read_forward_solution_eeg(fname, **kwargs):
"""Read EEG forward."""
fwd = convert_forward_solution(read_forward_solution(fname),
copy=False,
**kwargs)
fwd = pick_types_forward(fwd, meg=False, eeg=True)
return fwd
def test_gamma_map():
"""Test Gamma MAP inverse."""
forward = read_forward_solution(fname_fwd)
forward = convert_forward_solution(forward, surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0),
proj=False)
evoked.resample(50, npad=100)
evoked.crop(tmin=0.1, tmax=0.14) # crop to window around peak
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info, rank=None)
alpha = 0.5
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=True, update_mode=1)
_check_stc(stc, evoked, 68477, 'lh', fwd=forward)
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1)
_check_stc(stc, evoked, 82010, 'lh', fwd=forward)
dips = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1,
return_as_dipoles=True)
assert (isinstance(dips[0], Dipole))
stc_dip = make_stc_from_dipoles(dips, forward['src'])
_check_stcs(stc, stc_dip)
# force fixed orientation
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=2,
loose=0, return_residual=False)
_check_stc(stc, evoked, 85739, 'lh', fwd=forward, ratio=20.)
def invs():
"""Inverses of various amounts of loose."""
fwd = read_forward_solution(fname_fwd)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
fwd_surf = convert_forward_solution(fwd, surf_ori=True)
evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0]
noise_cov = read_cov(fname_cov)
free = make_inverse_operator(evoked.info, fwd, noise_cov, loose=1.)
free_surf = make_inverse_operator(evoked.info,
fwd_surf,
noise_cov,
loose=1.)
freeish = make_inverse_operator(evoked.info, fwd, noise_cov, loose=0.9999)
fixed = make_inverse_operator(evoked.info, fwd, noise_cov, loose=0.)
fixedish = make_inverse_operator(evoked.info, fwd, noise_cov, loose=0.0001)
assert_allclose(free['source_nn'],
np.kron(np.ones(fwd['nsource']), np.eye(3)).T,
atol=1e-7)
# This is the one exception:
assert not np.allclose(free['source_nn'], free_surf['source_nn'])
assert_allclose(free['source_nn'],
np.tile(np.eye(3), (free['nsource'], 1)),
atol=1e-7)
# All others are similar:
for other in (freeish, fixedish):
assert_allclose(free_surf['source_nn'], other['source_nn'], atol=1e-7)
assert_allclose(free_surf['source_nn'][2::3],
fixed['source_nn'],
atol=1e-7)
expected_nn = np.concatenate([_get_src_nn(s) for s in fwd['src']])
assert_allclose(fixed['source_nn'], expected_nn, atol=1e-7)
return evoked, free, free_surf, freeish, fixed, fixedish
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 test_gamma_map():
"""Test Gamma MAP inverse"""
forward = read_forward_solution(fname_fwd, force_fixed=False,
surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
evoked.resample(50)
evoked.crop(tmin=0, tmax=0.3)
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info)
alpha = 0.2
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
xyz_same_gamma=True, update_mode=1, verbose=False)
idx = np.argmax(np.sum(stc.data ** 2, axis=1))
assert_true(np.concatenate(stc.vertices)[idx] == 96397)
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
xyz_same_gamma=False, update_mode=1, verbose=False)
idx = np.argmax(np.sum(stc.data ** 2, axis=1))
assert_true(np.concatenate(stc.vertices)[idx] == 82010)
# force fixed orientation
stc, res = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
xyz_same_gamma=False, update_mode=2,
loose=None, return_residual=True, verbose=False)
idx = np.argmax(np.sum(stc.data ** 2, axis=1))
# assert_true(np.concatenate(stc.vertices)[idx] == 83398) # XXX FIX
assert_array_almost_equal(evoked.times, res.times)
def test_mxne_inverse_sure():
"""Tests SURE criterion for automatic alpha selection on MEG data."""
def data_fun(times):
data = np.zeros(times.shape)
data[times >= 0] = 50e-9
return data
n_dipoles = 2
raw = mne.io.read_raw_fif(fname_raw)
info = mne.io.read_info(fname_data)
with info._unlock():
info['projs'] = []
noise_cov = mne.make_ad_hoc_cov(info)
label_names = ['Aud-lh', 'Aud-rh']
labels = [mne.read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
for ln in label_names]
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
forward = mne.read_forward_solution(fname_fwd)
forward = mne.pick_types_forward(forward, meg="grad", eeg=False,
exclude=raw.info['bads'])
times = np.arange(100, dtype=np.float64) / raw.info['sfreq'] - 0.1
stc = simulate_sparse_stc(forward['src'], n_dipoles=n_dipoles, times=times,
random_state=1, labels=labels, data_fun=data_fun)
nave = 30
evoked = simulate_evoked(forward, stc, info, noise_cov, nave=nave,
use_cps=False, iir_filter=None)
evoked = evoked.crop(tmin=0, tmax=10e-3)
stc_ = mixed_norm(evoked, forward, noise_cov, loose=0.9, n_mxne_iter=5,
depth=0.9)
assert_array_equal(stc_.vertices, stc.vertices)
def test_gamma_map():
"""Test Gamma MAP inverse"""
forward = read_forward_solution(fname_fwd, force_fixed=False,
surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
evoked.resample(50)
evoked.crop(tmin=0, tmax=0.3)
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info)
alpha = 0.2
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
xyz_same_gamma=True, update_mode=1, verbose=False)
idx = np.argmax(np.sum(stc.data ** 2, axis=1))
assert_true(np.concatenate(stc.vertno)[idx] == 96397)
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
xyz_same_gamma=False, update_mode=1, verbose=False)
idx = np.argmax(np.sum(stc.data ** 2, axis=1))
assert_true(np.concatenate(stc.vertno)[idx] == 82010)
# force fixed orientation
stc, res = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
xyz_same_gamma=False, update_mode=2,
loose=None, return_residual=True, verbose=False)
idx = np.argmax(np.sum(stc.data ** 2, axis=1))
# assert_true(np.concatenate(stc.vertno)[idx] == 83398) # XXX FIX
assert_array_almost_equal(evoked.times, res.times)
def test_simulate_evoked():
"""Test simulation of evoked data."""
raw = read_raw_fif(raw_fname, add_eeg_ref=False)
fwd = read_forward_solution(fwd_fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
snr = 6 # dB
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series for 2 dipoles
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times)
stc._data *= 1e-9
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov, snr,
tmin=0.0, tmax=0.2, iir_filter=iir_filter)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
assert_raises(RuntimeError, simulate_evoked, fwd, stc_bad,
evoked_template.info, cov, snr, tmin=0.0, tmax=0.2)
evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
tmin=0.0, tmax=0.2)
evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
tmin=0.0, tmax=0.2)
assert_array_equal(evoked_1.data, evoked_2.data)
# test snr definition in dB
evoked_noise = simulate_evoked(fwd, stc, evoked_template.info, cov,
snr=snr, tmin=None, tmax=None,
iir_filter=None)
evoked_clean = simulate_evoked(fwd, stc, evoked_template.info, cov,
snr=np.inf, tmin=None, tmax=None,
iir_filter=None)
noise = evoked_noise.data - evoked_clean.data
empirical_snr = 10 * np.log10(np.mean((evoked_clean.data ** 2).ravel()) /
np.mean((noise ** 2).ravel()))
assert_almost_equal(snr, empirical_snr, decimal=5)
cov['names'] = cov.ch_names[:-2] # Error channels are different.
assert_raises(ValueError, simulate_evoked, fwd, stc, evoked_template.info,
cov, snr=3., tmin=None, tmax=None, iir_filter=None)
def apply_inverse(fnevo, method='dSPM', snr=3.0, event='LLst',
baseline=False, btmin=-0.3, btmax=-0.1, min_subject='fsaverage'):
'''
Parameter
---------
fnevo: string or list
The evoked file with ECG, EOG and environmental noise free.
method: inverse method, 'MNE' or 'dSPM'
event: string
The event name related with epochs.
min_subject: string
The subject name as the common brain.
snr: signal to noise ratio for inverse solution.
'''
#Get the default subjects_dir
from mne.minimum_norm import apply_inverse
fnlist = get_files_from_list(fnevo)
# loop across all filenames
for fname in fnlist:
fn_path = os.path.split(fname)[0]
name = os.path.basename(fname)
stc_name = name[:name.rfind('-ave.fif')]
subject = name.split('_')[0]
subject_path = subjects_dir + '/%s' %subject
min_dir = subjects_dir + '/%s' %min_subject
fn_trans = fn_path + '/%s-trans.fif' % subject
fn_cov = fn_path + '/%s_empty,nr-cov.fif' % subject
fn_src = subject_path + '/bem/%s-ico-5-src.fif' % subject
fn_bem = subject_path + '/bem/%s-5120-5120-5120-bem-sol.fif' % subject
snr = snr
lambda2 = 1.0 / snr ** 2
#noise_cov = mne.read_cov(fn_cov)
[evoked] = mne.read_evokeds(fname)
noise_cov = mne.read_cov(fn_cov)
# this path used for ROI definition
stc_path = min_dir + '/%s_ROIs/%s' %(method,subject)
#fn_cov = meg_path + '/%s_empty,fibp1-45,nr-cov.fif' % subject
set_directory(stc_path)
noise_cov = mne.cov.regularize(noise_cov, evoked.info,
mag=0.05, grad=0.05, proj=True)
fwd_ev = mne.make_forward_solution(evoked.info, trans=fn_trans,
src=fn_src, bem=fn_bem,
fname=None, meg=True, eeg=False,
mindist=5.0, n_jobs=2,
overwrite=True)
fwd_ev = mne.convert_forward_solution(fwd_ev, surf_ori=True)
forward_meg_ev = mne.pick_types_forward(fwd_ev, meg=True, eeg=False)
inverse_operator_ev = mne.minimum_norm.make_inverse_operator(
evoked.info, forward_meg_ev, noise_cov,
loose=0.2, depth=0.8)
# Compute inverse solution
stc = apply_inverse(evoked, inverse_operator_ev, lambda2, method,
pick_ori=None)
# Morph STC
stc_morph = mne.morph_data(subject, min_subject, stc, grade=5, smooth=5)
stc_morph.save(stc_path + '/%s' % (stc_name), ftype='stc')
if baseline == True:
stc_base = stc_morph.crop(btmin, btmax)
stc_base.save(stc_path + '/%s_%s_baseline' % (subject, event), ftype='stc')
def test_simulate_evoked():
""" Test simulation of evoked data """
raw = Raw(raw_fname)
fwd = read_forward_solution(fwd_fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
snr = 6 # dB
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series for 2 dipoles
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times)
stc._data *= 1e-9
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov, snr,
tmin=0.0, tmax=0.2, iir_filter=iir_filter)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
assert_raises(RuntimeError, simulate_evoked, fwd, stc_bad,
evoked_template.info, cov, snr, tmin=0.0, tmax=0.2)
evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
tmin=0.0, tmax=0.2)
evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
tmin=0.0, tmax=0.2)
assert_array_equal(evoked_1.data, evoked_2.data)
# test snr definition in dB
evoked_noise = simulate_evoked(fwd, stc, evoked_template.info, cov,
snr=snr, tmin=None, tmax=None,
iir_filter=None)
evoked_clean = simulate_evoked(fwd, stc, evoked_template.info, cov,
snr=np.inf, tmin=None, tmax=None,
iir_filter=None)
noise = evoked_noise.data - evoked_clean.data
empirical_snr = 10 * np.log10(np.mean((evoked_clean.data ** 2).ravel()) /
np.mean((noise ** 2).ravel()))
assert_almost_equal(snr, empirical_snr, decimal=5)
cov['names'] = cov.ch_names[:-2] # Error channels are different.
assert_raises(ValueError, simulate_evoked, fwd, stc, evoked_template.info,
cov, snr=3., tmin=None, tmax=None, iir_filter=None)
def test_restrict_forward_to_label():
"""Test restriction of source space to label
"""
fwd = read_forward_solution(fname_meeg, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True)
label_path = op.join(data_path, "MEG", "sample", "labels")
labels = ["Aud-lh", "Vis-rh"]
label_lh = read_label(op.join(label_path, labels[0] + ".label"))
label_rh = read_label(op.join(label_path, labels[1] + ".label"))
fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])
src_sel_lh = np.intersect1d(fwd["src"][0]["vertno"], label_lh.vertices)
src_sel_lh = np.searchsorted(fwd["src"][0]["vertno"], src_sel_lh)
src_sel_rh = np.intersect1d(fwd["src"][1]["vertno"], label_rh.vertices)
src_sel_rh = np.searchsorted(fwd["src"][1]["vertno"], src_sel_rh) + len(fwd["src"][0]["vertno"])
assert_equal(fwd_out["sol"]["ncol"], len(src_sel_lh) + len(src_sel_rh))
assert_equal(fwd_out["src"][0]["nuse"], len(src_sel_lh))
assert_equal(fwd_out["src"][1]["nuse"], len(src_sel_rh))
assert_equal(fwd_out["src"][0]["vertno"], src_sel_lh)
assert_equal(fwd_out["src"][1]["vertno"], src_sel_rh)
fwd = read_forward_solution(fname_meeg, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
label_path = op.join(data_path, "MEG", "sample", "labels")
labels = ["Aud-lh", "Vis-rh"]
label_lh = read_label(op.join(label_path, labels[0] + ".label"))
label_rh = read_label(op.join(label_path, labels[1] + ".label"))
fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])
src_sel_lh = np.intersect1d(fwd["src"][0]["vertno"], label_lh.vertices)
src_sel_lh = np.searchsorted(fwd["src"][0]["vertno"], src_sel_lh)
src_sel_rh = np.intersect1d(fwd["src"][1]["vertno"], label_rh.vertices)
src_sel_rh = np.searchsorted(fwd["src"][1]["vertno"], src_sel_rh) + len(fwd["src"][0]["vertno"])
assert_equal(fwd_out["sol"]["ncol"], 3 * (len(src_sel_lh) + len(src_sel_rh)))
assert_equal(fwd_out["src"][0]["nuse"], len(src_sel_lh))
assert_equal(fwd_out["src"][1]["nuse"], len(src_sel_rh))
assert_equal(fwd_out["src"][0]["vertno"], src_sel_lh)
assert_equal(fwd_out["src"][1]["vertno"], src_sel_rh)
def _create_testing_brain(hemi, surf='inflated', src='surface',
size=300, n_time=5, diverging=False, **kwargs):
assert src in ('surface', 'vector', 'mixed', 'volume')
meth = 'plot'
if src in ('surface', 'mixed'):
sample_src = read_source_spaces(src_fname)
klass = MixedSourceEstimate if src == 'mixed' else SourceEstimate
if src == 'vector':
fwd = read_forward_solution(fname_fwd)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0]
noise_cov = read_cov(fname_cov)
free = make_inverse_operator(
evoked.info, fwd, noise_cov, loose=1.)
stc = apply_inverse(evoked, free, pick_ori='vector')
return stc.plot(
subject=subject_id, hemi=hemi, size=size,
subjects_dir=subjects_dir, colormap='auto',
**kwargs)
if src in ('volume', 'mixed'):
vol_src = setup_volume_source_space(
subject_id, 7., mri='aseg.mgz',
volume_label='Left-Cerebellum-Cortex',
subjects_dir=subjects_dir, add_interpolator=False)
assert len(vol_src) == 1
assert vol_src[0]['nuse'] == 150
if src == 'mixed':
sample_src = sample_src + vol_src
else:
sample_src = vol_src
klass = VolSourceEstimate
meth = 'plot_3d'
assert sample_src.kind == src
# dense version
rng = np.random.RandomState(0)
vertices = [s['vertno'] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_size = stc_data.size
stc_data[(rng.rand(stc_size // 20) * stc_size).astype(int)] = \
rng.rand(stc_data.size // 20)
stc_data.shape = (n_verts, n_time)
if diverging:
stc_data -= 0.5
stc = klass(stc_data, vertices, 1, 1)
clim = dict(kind='value', lims=[0.1, 0.2, 0.3])
if diverging:
clim['pos_lims'] = clim.pop('lims')
brain_data = getattr(stc, meth)(
subject=subject_id, hemi=hemi, surface=surf, size=size,
subjects_dir=subjects_dir, colormap='auto',
clim=clim, src=sample_src,
**kwargs)
return brain_data
def test_simulate_evoked():
"""Test simulation of evoked data."""
raw = read_raw_fif(raw_fname)
fwd = read_forward_solution(fwd_fname)
fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=False)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
cov = regularize(cov, evoked_template.info)
nave = evoked_template.nave
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series for 2 dipoles
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
random_state=42)
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov,
iir_filter=iir_filter, nave=nave)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
assert_equal(evoked.nave, nave)
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
assert_raises(RuntimeError, simulate_evoked, fwd, stc_bad,
evoked_template.info, cov)
evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov,
nave=np.inf)
evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov,
nave=np.inf)
assert_array_equal(evoked_1.data, evoked_2.data)
# Test the equivalence snr to nave
with warnings.catch_warnings(record=True): # deprecation
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov,
snr=6, random_state=42)
assert_allclose(np.linalg.norm(evoked.data, ord='fro'),
0.00078346820226502716)
cov['names'] = cov.ch_names[:-2] # Error channels are different.
assert_raises(ValueError, simulate_evoked, fwd, stc, evoked_template.info,
cov, nave=nave, iir_filter=None)
def test_apply_forward():
"""Test projection of source space data to sensor space."""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True,
use_cps=True)
fwd = pick_types_forward(fwd, meg=True)
assert isinstance(fwd, Forward)
vertno = [fwd['src'][0]['vertno'], fwd['src'][1]['vertno']]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
gain_sum = np.sum(fwd['sol']['data'], axis=1)
# Evoked
evoked = read_evokeds(fname_evoked, condition=0)
evoked.pick_types(meg=True)
with pytest.warns(RuntimeWarning, match='only .* positive values'):
evoked = apply_forward(fwd, stc, evoked.info, start=start, stop=stop)
data = evoked.data
times = evoked.times
# do some tests
assert_array_almost_equal(evoked.info['sfreq'], sfreq)
assert_array_almost_equal(np.sum(data, axis=1), n_times * gain_sum)
assert_array_almost_equal(times[0], t_start)
assert_array_almost_equal(times[-1], t_start + (n_times - 1) / sfreq)
# vector
stc_vec = VectorSourceEstimate(
fwd['source_nn'][:, :, np.newaxis] * stc.data[:, np.newaxis],
stc.vertices, stc.tmin, stc.tstep)
with pytest.warns(RuntimeWarning, match='very large'):
evoked_2 = apply_forward(fwd, stc_vec, evoked.info)
assert np.abs(evoked_2.data).mean() > 1e-5
assert_allclose(evoked.data, evoked_2.data, atol=1e-10)
# Raw
with pytest.warns(RuntimeWarning, match='only .* positive values'):
raw_proj = apply_forward_raw(fwd, stc, evoked.info, start=start,
stop=stop)
data, times = raw_proj[:, :]
# do some tests
assert_array_almost_equal(raw_proj.info['sfreq'], sfreq)
assert_array_almost_equal(np.sum(data, axis=1), n_times * gain_sum)
atol = 1. / sfreq
assert_allclose(raw_proj.first_samp / sfreq, t_start, atol=atol)
assert_allclose(raw_proj.last_samp / sfreq,
t_start + (n_times - 1) / sfreq, atol=atol)
def test_apply_forward():
"""Test projection of source space data to sensor space
"""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd,
surf_ori=True,
force_fixed=True,
use_cps=True)
fwd = pick_types_forward(fwd, meg=True)
assert_true(isinstance(fwd, Forward))
vertno = [fwd['src'][0]['vertno'], fwd['src'][1]['vertno']]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
gain_sum = np.sum(fwd['sol']['data'], axis=1)
# Evoked
with warnings.catch_warnings(record=True) as w:
evoked = read_evokeds(fname_evoked, condition=0)
evoked.pick_types(meg=True)
evoked = apply_forward(fwd, stc, evoked.info, start=start, stop=stop)
assert_equal(len(w), 2)
data = evoked.data
times = evoked.times
# do some tests
assert_array_almost_equal(evoked.info['sfreq'], sfreq)
assert_array_almost_equal(np.sum(data, axis=1), n_times * gain_sum)
assert_array_almost_equal(times[0], t_start)
assert_array_almost_equal(times[-1], t_start + (n_times - 1) / sfreq)
# Raw
raw_proj = apply_forward_raw(fwd,
stc,
evoked.info,
start=start,
stop=stop)
data, times = raw_proj[:, :]
# do some tests
assert_array_almost_equal(raw_proj.info['sfreq'], sfreq)
assert_array_almost_equal(np.sum(data, axis=1), n_times * gain_sum)
atol = 1. / sfreq
assert_allclose(raw_proj.first_samp / sfreq, t_start, atol=atol)
assert_allclose(raw_proj.last_samp / sfreq,
t_start + (n_times - 1) / sfreq,
atol=atol)
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 test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model."""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess(['mne_forward_solution', '--meg', '--eeg',
'--meas', fname_raw, '--src', fname_src_small,
'--mri', fname_trans, '--fwd', out_name])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=True, verbose=True)
_compare_forwards(fwd, fwd_py, 366, 108,
meg_rtol=5e-1, meg_atol=1e-6,
eeg_rtol=5e-1, eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0, rtol=1e-3)
# Number of layers in the sphere model doesn't matter for MEG
# (as long as no sources are omitted due to distance)
assert len(sphere['layers']) == 4
fwd = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=False)
sphere_1 = make_sphere_model(head_radius=None)
assert len(sphere_1['layers']) == 0
assert_array_equal(sphere['r0'], sphere_1['r0'])
fwd_1 = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=False)
_compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12)
# Homogeneous model
sphere = make_sphere_model(head_radius=None)
with pytest.raises(RuntimeError, match='zero shells.*EEG'):
make_forward_solution(fname_raw, fname_trans, src, sphere)
def test_restrict_forward_to_stc():
"""Test restriction of source space to source SourceEstimate
"""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_true(isinstance(fwd_out, Forward))
assert_equal(fwd_out['sol']['ncol'], 20)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
fwd = read_forward_solution(fname, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_equal(fwd_out['sol']['ncol'], 60)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model"""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample',
fname_src_small,
'oct2',
subjects_dir=subjects_dir,
add_dist=False)
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess([
'mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src',
fname_src_small, '--mri', fname_trans, '--fwd', out_name
])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=True,
verbose=True)
_compare_forwards(fwd,
fwd_py,
366,
108,
meg_rtol=5e-1,
meg_atol=1e-6,
eeg_rtol=5e-1,
eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0,
rtol=1e-3)
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)
label = mne.read_label(fname_label)
return fwd_free, fwd_surf, fwd_fixed, fwd_vol, label
def test_restrict_forward_to_stc():
"""Test restriction of source space to source SourceEstimate
"""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_true(isinstance(fwd_out, Forward))
assert_equal(fwd_out['sol']['ncol'], 20)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
fwd = read_forward_solution(fname_meeg, force_fixed=False)
fwd = pick_types_forward(fwd, meg=True)
vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
fwd_out = restrict_forward_to_stc(fwd, stc)
assert_equal(fwd_out['sol']['ncol'], 60)
assert_equal(fwd_out['src'][0]['nuse'], 15)
assert_equal(fwd_out['src'][1]['nuse'], 5)
assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])
def test_simulate_evoked():
"""Test simulation of evoked data."""
raw = read_raw_fif(raw_fname)
fwd = read_forward_solution(fwd_fname)
fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=False)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
cov = regularize(cov, evoked_template.info)
nave = evoked_template.nave
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series for 2 dipoles
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
random_state=42)
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov,
iir_filter=iir_filter, nave=nave)
assert_array_almost_equal(evoked.times, stc.times)
assert len(evoked.data) == len(fwd['sol']['data'])
assert_equal(evoked.nave, nave)
assert len(evoked.info['projs']) == len(cov['projs'])
evoked_white = whiten_evoked(evoked, cov)
assert abs(evoked_white.data[:, 0].std() - 1.) < 0.1
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
pytest.raises(RuntimeError, simulate_evoked, fwd, stc_bad,
evoked_template.info, cov)
evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov,
nave=np.inf)
evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov,
nave=np.inf)
assert_array_equal(evoked_1.data, evoked_2.data)
cov['names'] = cov.ch_names[:-2] # Error channels are different.
with pytest.raises(RuntimeError, match='Not all channels present'):
simulate_evoked(fwd, stc, evoked_template.info, cov)
def test_simulate_evoked():
"""Test simulation of evoked data."""
raw = read_raw_fif(raw_fname)
fwd = read_forward_solution(fwd_fname)
fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=False)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
cov = regularize(cov, evoked_template.info)
nave = evoked_template.nave
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series for 2 dipoles
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
random_state=42)
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov,
iir_filter=iir_filter, nave=nave)
assert_array_almost_equal(evoked.times, stc.times)
assert len(evoked.data) == len(fwd['sol']['data'])
assert_equal(evoked.nave, nave)
assert len(evoked.info['projs']) == len(cov['projs'])
evoked_white = whiten_evoked(evoked, cov)
assert abs(evoked_white.data[:, 0].std() - 1.) < 0.1
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
pytest.raises(ValueError, simulate_evoked, fwd, stc_bad,
evoked_template.info, cov)
evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov,
nave=np.inf)
evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov,
nave=np.inf)
assert_array_equal(evoked_1.data, evoked_2.data)
cov['names'] = cov.ch_names[:-2] # Error channels are different.
with pytest.raises(RuntimeError, match='Not all channels present'):
simulate_evoked(fwd, stc, evoked_template.info, cov)
def test_simulate_evoked():
""" Test simulation of evoked data """
raw = Raw(raw_fname)
fwd = read_forward_solution(fwd_fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
label_names = ['Aud-lh', 'Aud-rh']
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template = pick_types_evoked(evoked_template, meg=True, eeg=True,
exclude=raw.info['bads'])
snr = 6 # dB
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series from 2 Morlet wavelets
stc_data = np.zeros((len(labels), len(times)))
Ws = morlet(sfreq, [3, 10], n_cycles=[1, 1.5])
stc_data[0][:len(Ws[0])] = np.real(Ws[0])
stc_data[1][:len(Ws[1])] = np.real(Ws[1])
stc_data *= 100 * 1e-9 # use nAm as unit
# time translation
stc_data[1] = np.roll(stc_data[1], 80)
stc = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep,
random_state=0)
# Generate noisy evoked data
iir_filter = [1, -0.9]
with warnings.catch_warnings(record=True):
warnings.simplefilter('always') # positive semidefinite warning
evoked = generate_evoked(fwd, stc, evoked_template, cov, snr,
tmin=0.0, tmax=0.2, iir_filter=iir_filter)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
assert_raises(RuntimeError, generate_evoked, fwd, stc_bad,
evoked_template, cov, snr, tmin=0.0, tmax=0.2)
def test_apply_forward():
"""Test projection of source space data to sensor space
"""
start = 0
stop = 5
n_times = stop - start - 1
sfreq = 10.0
t_start = 0.123
fwd = read_forward_solution(fname_meeg)
fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True,
use_cps=True)
fwd = pick_types_forward(fwd, meg=True)
assert_true(isinstance(fwd, Forward))
vertno = [fwd['src'][0]['vertno'], fwd['src'][1]['vertno']]
stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)
gain_sum = np.sum(fwd['sol']['data'], axis=1)
# Evoked
with warnings.catch_warnings(record=True) as w:
evoked = read_evokeds(fname_evoked, condition=0)
evoked.pick_types(meg=True)
evoked = apply_forward(fwd, stc, evoked.info, start=start, stop=stop)
assert_equal(len(w), 2)
data = evoked.data
times = evoked.times
# do some tests
assert_array_almost_equal(evoked.info['sfreq'], sfreq)
assert_array_almost_equal(np.sum(data, axis=1), n_times * gain_sum)
assert_array_almost_equal(times[0], t_start)
assert_array_almost_equal(times[-1], t_start + (n_times - 1) / sfreq)
# Raw
raw_proj = apply_forward_raw(fwd, stc, evoked.info, start=start,
stop=stop)
data, times = raw_proj[:, :]
# do some tests
assert_array_almost_equal(raw_proj.info['sfreq'], sfreq)
assert_array_almost_equal(np.sum(data, axis=1), n_times * gain_sum)
atol = 1. / sfreq
assert_allclose(raw_proj.first_samp / sfreq, t_start, atol=atol)
assert_allclose(raw_proj.last_samp / sfreq,
t_start + (n_times - 1) / sfreq, atol=atol)
def test_gamma_map():
"""Test Gamma MAP inverse"""
forward = read_forward_solution(fname_fwd,
force_fixed=False,
surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked,
condition=0,
baseline=(None, 0),
proj=False)
evoked.resample(50, npad=100)
evoked.crop(tmin=0.1, tmax=0.16) # crop to nice window near samp border
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info)
alpha = 0.5
stc = gamma_map(evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=True,
update_mode=1)
_check_stc(stc, evoked, 68477)
stc = gamma_map(evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=False,
update_mode=1)
_check_stc(stc, evoked, 82010)
# force fixed orientation
stc = gamma_map(evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=False,
update_mode=2,
loose=None,
return_residual=False)
_check_stc(stc, evoked, 85739, 20)
def calculate_forward_solution(self, data):
"""
Calculates the forward solution
:param data:
:return:
"""
print("Calculating forward solution...")
self.fwd = mne.make_forward_solution(data.info,
self.trans_path,
self.src,
self.bem_path,
n_jobs=CPU_THREADS,
verbose=self.verbose)
self.fwd = mne.pick_types_forward(self.fwd,
meg=self.channel_types['meg'],
eeg=self.channel_types['eeg'],
exclude=data.info['bads'])
return self.fwd
def test_mxne_inverse_empty():
"""Tests solver with too high alpha."""
evoked = read_evokeds(fname_data, condition=0, baseline=(None, 0))
evoked.pick("grad", exclude="bads")
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
forward = mne.read_forward_solution(fname_fwd)
forward = mne.pick_types_forward(forward, meg="grad", eeg=False,
exclude=evoked.info['bads'])
cov = read_cov(fname_cov)
with pytest.warns(RuntimeWarning, match='too big'):
stc, residual = mixed_norm(
evoked, forward, cov, n_mxne_iter=3, alpha=99,
return_residual=True)
assert stc.data.size == 0
assert stc.vertices[0].size == 0
assert stc.vertices[1].size == 0
assert_allclose(evoked.data, residual.data)
def _load_restricted_forward(source_vertno1, source_vertno2):
"""Load forward models and restrict them so that they include source
vertices"""
fwd_free = mne.read_forward_solution(fname_fwd)
fwd_free = mne.pick_types_forward(fwd_free, meg='grad', eeg=False)
# Restrict forward
vertno_lh = np.random.choice(fwd_free['src'][0]['vertno'], 40,
replace=False)
if source_vertno1 not in vertno_lh:
vertno_lh = np.append(vertno_lh, source_vertno1)
vertno_rh = np.random.choice(fwd_free['src'][1]['vertno'], 40,
replace=False)
if source_vertno2 not in vertno_rh:
vertno_rh = np.append(vertno_rh, source_vertno2)
fwd_free = restrict_forward_to_vertices(fwd_free, [vertno_lh, vertno_rh])
fwd_fixed = mne.convert_forward_solution(fwd_free, force_fixed=True,
use_cps=False)
return fwd_free, fwd_fixed
def test_simulate_evoked():
""" Test simulation of evoked data """
raw = Raw(raw_fname)
fwd = read_forward_solution(fwd_fname, force_fixed=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
snr = 6 # dB
tmin = -0.1
sfreq = 1000. # Hz
tstep = 1. / sfreq
n_samples = 600
times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
# Generate times series for 2 dipoles
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times)
stc._data *= 1e-9
# Generate noisy evoked data
iir_filter = [1, -0.9]
evoked = simulate_evoked(fwd, stc, evoked_template.info, cov, snr,
tmin=0.0, tmax=0.2, iir_filter=iir_filter)
assert_array_almost_equal(evoked.times, stc.times)
assert_true(len(evoked.data) == len(fwd['sol']['data']))
# make a vertex that doesn't exist in fwd, should throw error
stc_bad = stc.copy()
mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
stc_bad.vertices[0][0] = mv + 1
assert_raises(RuntimeError, simulate_evoked, fwd, stc_bad,
evoked_template.info, cov, snr, tmin=0.0, tmax=0.2)
evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
tmin=0.0, tmax=0.2)
evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
tmin=0.0, tmax=0.2)
assert_array_equal(evoked_1.data, evoked_2.data)
def read_forward_solution_meg(*args, **kwargs):
"""Read MEG forward."""
fwd = read_forward_solution(*args, **kwargs)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
return fwd
###############################################################################
# Load real data as templates
data_path = sample.data_path()
raw = Raw(data_path + '/MEG/sample/sample_audvis_raw.fif')
proj = read_proj(data_path + '/MEG/sample/sample_audvis_ecg_proj.fif')
raw.info['projs'] += proj
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
fwd = read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = read_cov(cov_fname)
condition = 'Left Auditory'
evoked_template = read_evokeds(ave_fname, condition=condition, baseline=None)
evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])
label_names = ['Aud-lh', 'Aud-rh']
labels = [
read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
for ln in label_names
]
###############################################################################
# Generate source time courses and the correspond evoked data
###############################################################################
# Load real data as templates
data_path = sample.data_path()
raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif')
proj = mne.read_proj(data_path + '/MEG/sample/sample_audvis_ecg_proj.fif')
raw.info['projs'] += proj
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
fwd = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = mne.read_cov(cov_fname)
info = mne.io.read_info(ave_fname)
label_names = ['Aud-lh', 'Aud-rh']
labels = [mne.read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
for ln in label_names]
###############################################################################
# Generate source time courses from 2 dipoles and the correspond evoked data
times = np.arange(300, dtype=np.float) / raw.info['sfreq'] - 0.1
rng = np.random.RandomState(42)
def data_fun(times):
def read_forward_solution_meg(*args, **kwargs):
fwd = read_forward_solution(*args, **kwargs)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
return fwd
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag')
# Show whitening
evoked.plot_white(noise_cov)
###############################################################################
# Inverse modeling: MNE/dSPM on evoked and raw data
# -------------------------------------------------
# Read the forward solution and compute the inverse operator
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Restrict forward solution as necessary for MEG
fwd = mne.pick_types_forward(fwd, meg=True, eeg=False)
# make an MEG inverse operator
info = evoked.info
inverse_operator = make_inverse_operator(info, fwd, noise_cov,
loose=0.2, depth=0.8)
write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',
inverse_operator)
###############################################################################
# Compute inverse solution
# ------------------------
method = "dSPM"
snr = 3.
data_path = sample.data_path()
subjects_dir = data_path + '/subjects/'
fname_fwd_emeg = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'
fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif'
# read forward solution with EEG and MEG
forward_emeg = mne.read_forward_solution(fname_fwd_emeg)
# forward operator with fixed source orientations
forward_emeg = mne.convert_forward_solution(forward_emeg,
surf_ori=True,
force_fixed=True)
# create a forward solution with MEG only
forward_meg = mne.pick_types_forward(forward_emeg, meg=True, eeg=False)
# noise covariance matrix
noise_cov = mne.read_cov(fname_cov)
# evoked data for info
evoked = mne.read_evokeds(fname_evo, 0)
# make inverse operator from forward solution for MEG and EEGMEG
inv_emeg = mne.minimum_norm.make_inverse_operator(info=evoked.info,
forward=forward_emeg,
noise_cov=noise_cov,
loose=0.,
depth=None)
inv_meg = mne.minimum_norm.make_inverse_operator(info=evoked.info,
# covariance of the temporal noise for each source point
stc_covar = np.zeros([n_times, n_times], dtype = np.float)
a = 1e-3
for i in range(n_times):
for j in range(n_times):
stc_covar[i,j] = np.exp(- a*(i-j)**2)
if i == j:
stc_covar[i,j] *= 1.01
amp = 10.0*1e-9
stc_data *= amp # use nAm as unit
# load the forward solution
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = mne.pick_types_forward(fwd, meg=True, eeg=False, exclude=bads)
# get the indices of source points for each ROI
src = fwd['src']
label_ind = list()
for i in range(len(labels)):
_, sel = mne.source_space.label_src_vertno_sel(labels[i],src)
label_ind.append(sel)
# stc0_data: source time series template with no noise
n_source = fwd['sol']['ncol']
stc0_data = np.zeros([n_source, n_times])
for i in range(len(labels)):
stc0 = mne.simulation.simulate_stc(fwd['src'], labels[i:i+1], stc_data[i:i+1,:], -0.1, 1.0/sfreq)
stc0_data[label_ind[i],:] = stc0.data
del(stc0)
def read_forward_solution_meg(*args, **kwargs):
"""Read forward MEG."""
fwd = read_forward_solution(*args)
fwd = convert_forward_solution(fwd, **kwargs)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
return fwd
def read_forward_solution_eeg(fname, **kwargs):
"""Read EEG forward."""
fwd = convert_forward_solution(read_forward_solution(fname), copy=False,
**kwargs)
fwd = pick_types_forward(fwd, meg=False, eeg=True)
return fwd
from my_settings import (mne_folder, epochs_folder)
import sys
import mne
from mne.minimum_norm import make_inverse_operator
subject = sys.argv[1]
fwd = mne.read_forward_solution(mne_folder + "%s-fwd.fif" % subject,
surf_ori=False)
fwd = mne.pick_types_forward(fwd, meg="grad", eeg=False)
cov = mne.read_cov(mne_folder + "%s-cov.fif" % subject)
epochs = mne.read_epochs(epochs_folder +
"%s_trial_start-epo.fif" % subject,
preload=False)
inv = make_inverse_operator(epochs.info, fwd, cov,
loose=0.2, depth=0.8)
mne.minimum_norm.write_inverse_operator(mne_folder +
"%s_grad-inv.fif" % subject,
inv)
def _read_forward_solution_meg(*args, **kwargs):
fwd = mne.read_forward_solution(*args)
fwd = mne.convert_forward_solution(fwd, **kwargs)
return mne.pick_types_forward(fwd, meg=True, eeg=False)
def read_forward_solution_eeg(*args, **kwargs):
fwd = read_forward_solution(*args, **kwargs)
fwd = pick_types_forward(fwd, meg=False, eeg=True)
return fwd
def apply_inverse(fnepo, method='dSPM', event='LLst', min_subject='fsaverage', STC_US='ROI',
snr=5.0):
'''
Parameter
---------
fnepo: string or list
The epochs file with ECG, EOG and environmental noise free.
method: inverse method, 'MNE' or 'dSPM'
event: string
The event name related with epochs.
min_subject: string
The subject name as the common brain.
STC_US: string
The using of the inversion for further analysis.
'ROI' stands for ROIs definition, 'CAU' stands for causality analysis.
snr: signal to noise ratio for inverse solution.
'''
#Get the default subjects_dir
from mne.minimum_norm import (apply_inverse, apply_inverse_epochs)
subjects_dir = os.environ['SUBJECTS_DIR']
fnlist = get_files_from_list(fnepo)
# loop across all filenames
for fname in fnlist:
fn_path = os.path.split(fname)[0]
name = os.path.basename(fname)
stc_name = name[:name.rfind('-epo.fif')]
subject = name.split('_')[0]
subject_path = subjects_dir + '/%s' %subject
min_dir = subjects_dir + '/%s' %min_subject
fn_trans = fn_path + '/%s-trans.fif' % subject
fn_cov = fn_path + '/%s_empty,nr-cov.fif' % subject
fn_src = subject_path + '/bem/%s-ico-4-src.fif' % subject
fn_bem = subject_path + '/bem/%s-5120-5120-5120-bem-sol.fif' % subject
snr = snr
lambda2 = 1.0 / snr ** 2
#noise_cov = mne.read_cov(fn_cov)
epochs = mne.read_epochs(fname)
noise_cov = mne.read_cov(fn_cov)
if STC_US == 'ROI':
# this path used for ROI definition
stc_path = min_dir + '/%s_ROIs/%s' %(method,subject)
#fn_cov = meg_path + '/%s_empty,fibp1-45,nr-cov.fif' % subject
evoked = epochs.average()
set_directory(stc_path)
noise_cov = mne.cov.regularize(noise_cov, evoked.info,
mag=0.05, grad=0.05, proj=True)
fwd_ev = mne.make_forward_solution(evoked.info, trans=fn_trans,
src=fn_src, bem=fn_bem,
fname=None, meg=True, eeg=False,
mindist=5.0, n_jobs=2,
overwrite=True)
fwd_ev = mne.convert_forward_solution(fwd_ev, surf_ori=True)
forward_meg_ev = mne.pick_types_forward(fwd_ev, meg=True, eeg=False)
inverse_operator_ev = mne.minimum_norm.make_inverse_operator(
evoked.info, forward_meg_ev, noise_cov,
loose=0.2, depth=0.8)
# Compute inverse solution
stc = apply_inverse(evoked, inverse_operator_ev, lambda2, method,
pick_ori=None)
# Morph STC
subject_id = min_subject
stc_morph = mne.morph_data(subject, subject_id, stc, grade=5, smooth=5)
stc_morph.save(stc_path + '/%s' % (stc_name), ftype='stc')
elif STC_US == 'CAU':
stcs_path = min_dir + '/stcs/%s/%s/' % (subject,event)
reset_directory(stcs_path)
noise_cov = mne.cov.regularize(noise_cov, epochs.info,
mag=0.05, grad=0.05, proj=True)
fwd = mne.make_forward_solution(epochs.info, trans=fn_trans,
src=fn_src, bem=fn_bem,
meg=True, eeg=False, mindist=5.0,
n_jobs=2, overwrite=True)
fwd = mne.convert_forward_solution(fwd, surf_ori=True)
forward_meg = mne.pick_types_forward(fwd, meg=True, eeg=False)
inverse_operator = mne.minimum_norm.make_inverse_operator(
epochs.info, forward_meg, noise_cov, loose=0.2,
depth=0.8)
# Compute inverse solution
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2,
method=method, pick_ori='normal')
s = 0
while s < len(stcs):
stc_morph = mne.morph_data(
subject, min_subject, stcs[s], grade=5, smooth=5)
stc_morph.save(stcs_path + '/trial%s_fsaverage'
% (subject, str(s)), ftype='stc')
s = s + 1
from tqdm import tqdm
import config
from config import dics_settings
from config import fname
from utils import set_directory
###############################################################################
# Load volume source space
###############################################################################
info = mne.io.read_info(fname.sample_raw)
info = mne.pick_info(info, mne.pick_types(info, meg=True, eeg=False))
fwd = mne.read_forward_solution(fname.fwd_man)
fwd = mne.pick_types_forward(fwd, meg=True, eeg=False)
vsrc = fwd['src']
vertno = vsrc[0]['vertno']
# needs to be set for plot_vstc_sliced_old to work
if vsrc[0]['subject_his_id'] is None:
vsrc[0]['subject_his_id'] = 'sample'
###############################################################################
# Get data from csv files
###############################################################################
dfs = []
for vertex in tqdm(range(3756), total=3756):
try:
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
snr = 3.0
lambda2 = 1.0 / snr ** 2
# Load data
evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
forward_meeg = mne.read_forward_solution(fname_fwd_meeg, surf_ori=True)
noise_cov = mne.read_cov(fname_cov)
# regularize noise covariance
noise_cov = mne.cov.regularize(noise_cov, evoked.info,
mag=0.05, grad=0.05, eeg=0.1, proj=True)
# Restrict forward solution as necessary for MEG
forward_meg = mne.pick_types_forward(forward_meeg, meg=True, eeg=False)
# Alternatively, you can just load a forward solution that is restricted
forward_eeg = mne.read_forward_solution(fname_fwd_eeg, surf_ori=True)
# make an M/EEG, MEG-only, and EEG-only inverse operators
info = evoked.info
inverse_operator_meeg = make_inverse_operator(info, forward_meeg, noise_cov,
loose=0.2, depth=0.8)
inverse_operator_meg = make_inverse_operator(info, forward_meg, noise_cov,
loose=0.2, depth=0.8)
inverse_operator_eeg = make_inverse_operator(info, forward_eeg, noise_cov,
loose=0.2, depth=0.8)
write_inverse_operator('sample_audvis-meeg-oct-6-inv.fif',
inverse_operator_meeg)
write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',
