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_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_apply_inverse_sphere(evoked):
"""Test applying an inverse with a sphere model (rank-deficient)."""
evoked.pick_channels(evoked.ch_names[:306:8])
evoked.info['projs'] = []
cov = make_ad_hoc_cov(evoked.info)
sphere = make_sphere_model('auto', 'auto', evoked.info)
fwd = read_forward_solution(fname_fwd)
vertices = [fwd['src'][0]['vertno'][::5], fwd['src'][1]['vertno'][::5]]
stc = SourceEstimate(np.zeros((sum(len(v) for v in vertices), 1)),
vertices, 0., 1.)
fwd = restrict_forward_to_stc(fwd, stc)
fwd = make_forward_solution(evoked.info,
fwd['mri_head_t'],
fwd['src'],
sphere,
mindist=5.)
evoked = EvokedArray(fwd['sol']['data'].copy(), evoked.info)
assert fwd['sol']['nrow'] == 39
assert fwd['nsource'] == 101
assert fwd['sol']['ncol'] == 303
tempdir = _TempDir()
temp_fname = op.join(tempdir, 'temp-inv.fif')
inv = make_inverse_operator(evoked.info, fwd, cov, loose=1.)
# This forces everything to be float32
write_inverse_operator(temp_fname, inv)
inv = read_inverse_operator(temp_fname)
stc = apply_inverse(evoked,
inv,
method='eLORETA',
method_params=dict(eps=1e-2))
# assert zero localization bias
assert_array_equal(np.argmax(stc.data, axis=0),
np.repeat(np.arange(101), 3))
def test_apply_inverse_sphere():
"""Test applying an inverse with a sphere model (rank-deficient)."""
evoked = _get_evoked()
evoked.pick_channels(evoked.ch_names[:306:8])
evoked.info['projs'] = []
cov = make_ad_hoc_cov(evoked.info)
sphere = make_sphere_model('auto', 'auto', evoked.info)
fwd = read_forward_solution(fname_fwd)
vertices = [fwd['src'][0]['vertno'][::5],
fwd['src'][1]['vertno'][::5]]
stc = SourceEstimate(np.zeros((sum(len(v) for v in vertices), 1)),
vertices, 0., 1.)
fwd = restrict_forward_to_stc(fwd, stc)
fwd = make_forward_solution(evoked.info, fwd['mri_head_t'], fwd['src'],
sphere, mindist=5.)
evoked = EvokedArray(fwd['sol']['data'].copy(), evoked.info)
assert fwd['sol']['nrow'] == 39
assert fwd['nsource'] == 101
assert fwd['sol']['ncol'] == 303
tempdir = _TempDir()
temp_fname = op.join(tempdir, 'temp-inv.fif')
inv = make_inverse_operator(evoked.info, fwd, cov, loose=1.)
# This forces everything to be float32
write_inverse_operator(temp_fname, inv)
inv = read_inverse_operator(temp_fname)
stc = apply_inverse(evoked, inv, method='eLORETA',
method_params=dict(eps=1e-2))
# assert zero localization bias
assert_array_equal(np.argmax(stc.data, axis=0),
np.repeat(np.arange(101), 3))
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_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_localization_bias():
"""Test inverse localization bias for minimum-norm solvers."""
# Identity input
evoked = _get_evoked()
evoked.pick_types(meg=True, eeg=True, exclude=())
evoked = EvokedArray(np.eye(len(evoked.data)), evoked.info)
noise_cov = read_cov(fname_cov)
# restrict to limited set of verts (small src here) and one hemi for speed
fwd_orig = read_forward_solution(fname_fwd)
vertices = [fwd_orig['src'][0]['vertno'].copy(), []]
stc = SourceEstimate(np.zeros((sum(len(v) for v in vertices), 1)),
vertices, 0., 1.)
fwd_orig = restrict_forward_to_stc(fwd_orig, stc)
#
# Fixed orientation (not very different)
#
fwd = fwd_orig.copy()
inv_fixed = make_inverse_operator(evoked.info, fwd, noise_cov, loose=0.,
depth=0.8)
fwd = convert_forward_solution(fwd, force_fixed=True, surf_ori=True)
fwd = fwd['sol']['data']
want = np.arange(fwd.shape[1])
for method, lower, upper in (('MNE', 83, 87),
('dSPM', 96, 98),
('sLORETA', 100, 100),
('eLORETA', 100, 100)):
inv_op = apply_inverse(evoked, inv_fixed, lambda2, method).data
loc = np.abs(np.dot(inv_op, fwd))
# Compute the percentage of sources for which there is no localization
# bias:
perc = (want == np.argmax(loc, axis=0)).mean() * 100
assert lower <= perc <= upper, method
#
# Loose orientation
#
fwd = fwd_orig.copy()
inv_free = make_inverse_operator(evoked.info, fwd, noise_cov, loose=0.2,
depth=0.8)
fwd = fwd['sol']['data']
want = np.arange(fwd.shape[1]) // 3
for method, lower, upper in (('MNE', 25, 35),
('dSPM', 25, 35),
('sLORETA', 35, 40),
('eLORETA', 40, 45)):
inv_op = apply_inverse(evoked, inv_free, lambda2, method,
pick_ori='vector').data
loc = np.linalg.norm(np.einsum('vos,sx->vxo', inv_op, fwd), axis=-1)
# Compute the percentage of sources for which there is no localization
# bias:
perc = (want == np.argmax(loc, axis=0)).mean() * 100
assert lower <= perc <= upper, method
#
# Free orientation
#
fwd = fwd_orig.copy()
inv_free = make_inverse_operator(evoked.info, fwd, noise_cov, loose=1.,
depth=0.8)
fwd = fwd['sol']['data']
want = np.arange(fwd.shape[1]) // 3
force_kwargs = dict(method_params=dict(force_equal=True))
for method, lower, upper, kwargs in (('MNE', 45, 55, {}),
('dSPM', 40, 45, {}),
('sLORETA', 90, 95, {}),
('eLORETA', 90, 95, force_kwargs),
('eLORETA', 100, 100, {}),
):
inv_op = apply_inverse(evoked, inv_free, lambda2, method,
pick_ori='vector', **kwargs).data
loc = np.linalg.norm(np.einsum('vos,sx->vxo', inv_op, fwd), axis=-1)
# Compute the percentage of sources for which there is no localization
# bias:
perc = (want == np.argmax(loc, axis=0)).mean() * 100
assert lower <= perc <= upper, method
