def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject"""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
# Morph the data over using standard methods
stc_morph = stc_fs.morph('sample', [s['vertno'] for s in src_morph],
smooth=1, subjects_dir=subjects_dir)
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# Compare to the original data
stc_morph_morph = stc_morph.morph('fsaverage', stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert_true(corr > 0.99, corr)
# Degenerate cases
stc_morph.subject = None # no .subject provided
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
assert_raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject"""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
# Morph the data over using standard methods
stc_morph = stc_fs.morph('sample', [s['vertno'] for s in src_morph],
smooth=1, subjects_dir=subjects_dir)
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# Compare to the original data
stc_morph_morph = stc_morph.morph('fsaverage', stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert_true(corr > 0.99, corr)
# Degenerate cases
stc_morph.subject = None # no .subject provided
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
assert_raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject."""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs], tmin, tstep,
'fsaverage')
n_verts_fs = sum(len(s['vertno']) for s in src_fs)
# Create our morph source space
src_morph = morph_source_spaces(src_fs,
'sample',
subjects_dir=subjects_dir)
n_verts_sample = sum(len(s['vertno']) for s in src_morph)
assert n_verts_fs == n_verts_sample
# Morph the data over using standard methods
stc_morph = compute_source_morph(src_fs,
'fsaverage',
'sample',
spacing=[s['vertno'] for s in src_morph],
smooth=1,
subjects_dir=subjects_dir,
warn=False).apply(stc_fs)
assert stc_morph.data.shape[0] == n_verts_sample
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [
np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices
]
stc_morph = SourceEstimate(
np.concatenate([
stc_morph.lh_data[keeps[0]], stc_morph.rh_data[keeps[1]]
]), [v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(src_fs,
subjects_dir=subjects_dir)
# This should fail (has too many verts in SourceMorph)
with pytest.warns(RuntimeWarning, match='vertices not included'):
morph = compute_source_morph(src_morph,
subject_from='sample',
spacing=stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir)
with pytest.raises(ValueError, match='vertices do not match'):
morph.apply(stc_morph)
# Compare to the original data
with pytest.warns(RuntimeWarning, match='vertices not included'):
stc_morph_morph = compute_source_morph(
src=stc_morph,
subject_from='sample',
spacing=stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir).apply(stc_morph)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Explicitly test having two vertices map to the same target vertex. We
# simulate this by having two vertices be at the same position.
src_fs2 = src_fs.copy()
vert1, vert2 = src_fs2[0]['vertno'][:2]
src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2]
stc_morph_return = stc_morph.to_original_src(src_fs2,
subjects_dir=subjects_dir)
# test to_original_src method result equality
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Degenerate cases
stc_morph.subject = None # no .subject provided
pytest.raises(ValueError,
stc_morph.to_original_src,
src_fs,
subject_orig='fsaverage',
subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
pytest.raises(ValueError,
stc_morph.to_original_src,
src_fs,
subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
pytest.raises(ValueError,
stc_morph.to_original_src,
src_fs,
subject_orig='foo',
subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
pytest.raises(RuntimeError,
stc_morph.to_original_src,
src,
subjects_dir=subjects_dir)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject."""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
n_verts_fs = sum(len(s['vertno']) for s in src_fs)
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
n_verts_sample = sum(len(s['vertno']) for s in src_morph)
assert n_verts_fs == n_verts_sample
# Morph the data over using standard methods
stc_morph = compute_source_morph(
src_fs, 'fsaverage', 'sample',
spacing=[s['vertno'] for s in src_morph], smooth=1,
subjects_dir=subjects_dir, warn=False).apply(stc_fs)
assert stc_morph.data.shape[0] == n_verts_sample
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# This should fail (has too many verts in SourceMorph)
with pytest.warns(RuntimeWarning, match='vertices not included'):
morph = compute_source_morph(
src_morph, subject_from='sample',
spacing=stc_morph_return.vertices, smooth=1,
subjects_dir=subjects_dir)
with pytest.raises(ValueError, match='vertices do not match'):
morph.apply(stc_morph)
# Compare to the original data
with pytest.warns(RuntimeWarning, match='vertices not included'):
stc_morph_morph = compute_source_morph(
src=stc_morph, subject_from='sample',
spacing=stc_morph_return.vertices, smooth=1,
subjects_dir=subjects_dir).apply(stc_morph)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Explicitly test having two vertices map to the same target vertex. We
# simulate this by having two vertices be at the same position.
src_fs2 = src_fs.copy()
vert1, vert2 = src_fs2[0]['vertno'][:2]
src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2]
stc_morph_return = stc_morph.to_original_src(
src_fs2, subjects_dir=subjects_dir)
# test to_original_src method result equality
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Degenerate cases
stc_morph.subject = None # no .subject provided
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
pytest.raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
