def test_labels_to_stc():
"""Test labels_to_stc."""
labels = read_labels_from_annot(
'sample', 'aparc', subjects_dir=subjects_dir)
values = np.random.RandomState(0).randn(len(labels))
with pytest.raises(ValueError, match='1 or 2 dim'):
labels_to_stc(labels, values[:, np.newaxis, np.newaxis])
with pytest.raises(ValueError, match=r'values\.shape'):
labels_to_stc(labels, values[np.newaxis])
stc = labels_to_stc(labels, values)
for value, label in zip(values, labels):
stc_label = stc.in_label(label)
assert (stc_label.data == value).all()
stc = read_source_estimate(stc_fname, 'sample')
def test_labels_to_stc():
"""Test labels_to_stc."""
labels = read_labels_from_annot(
'sample', 'aparc', subjects_dir=subjects_dir)
values = np.random.RandomState(0).randn(len(labels))
with pytest.raises(ValueError, match='1 or 2 dim'):
labels_to_stc(labels, values[:, np.newaxis, np.newaxis])
with pytest.raises(ValueError, match=r'values\.shape'):
labels_to_stc(labels, values[np.newaxis])
stc = labels_to_stc(labels, values)
for value, label in zip(values, labels):
stc_label = stc.in_label(label)
assert (stc_label.data == value).all()
stc = read_source_estimate(stc_fname, 'sample')
def run_degreeMapping(subjects_dir, subject, volume_spacing, freq):
frequency = str(freq)
DATA_DIR = Path(f'{subjects_dir}', f'{subject}', 'mne_files')
fwd_fname = f'{DATA_DIR}/{subject}_{volume_spacing}-fwd-label.fif.gz'
label_degree = f'{DATA_DIR}/{subject}_degreeMasked_{volume_spacing}_{frequency}.npy'
degree_mapped = f'{DATA_DIR}/{subject}_degreeMapped_{volume_spacing}_{frequency}.npy'
degree = np.load(label_degree)
degree = np.sum(degree > 0, axis=0)
fwd = mne.read_forward_solution(fwd_fname)
fname_aseg = f'{subjects_dir}/{subject}/mri/aparc.a2009s+aseg.mgz'
stc = mne.labels_to_stc(fname_aseg, degree, src=fwd['src'])
degree_values = stc.data[:, 0]
print(f'Degree saved to {degree_mapped}')
np.save(degree_mapped, degree_values)
return_generator=True)
corr = envelope_correlation(label_ts, verbose=True)
# let's plot this matrix
fig, ax = plt.subplots(figsize=(4, 4))
ax.imshow(corr, cmap='viridis', clim=np.percentile(corr, [5, 95]))
fig.tight_layout()
##############################################################################
# Compute the degree and plot it
# ------------------------------
# sphinx_gallery_thumbnail_number = 2
threshold_prop = 0.15 # percentage of strongest edges to keep in the graph
degree = mne.connectivity.degree(corr, threshold_prop=threshold_prop)
stc = mne.labels_to_stc(labels, degree)
stc = stc.in_label(
mne.Label(inv['src'][0]['vertno'], hemi='lh') +
mne.Label(inv['src'][1]['vertno'], hemi='rh'))
brain = stc.plot(clim=dict(kind='percent', lims=[75, 85, 95]),
colormap='gnuplot',
subjects_dir=subjects_dir,
views='dorsal',
hemi='both',
smoothing_steps=25,
time_label='Beta band')
##############################################################################
# References
# ----------
# .. [1] Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012)
xytext = xy + [0.01, 1]
ax.annotate(label_names[lidx],
xy,
xytext,
arrowprops=dict(arrowstyle='->'),
color='r')
ax.set(xlim=stc.times[[0, -1]], xlabel='Time (s)', ylabel='Activation')
for key in ('right', 'top'):
ax.spines[key].set_visible(False)
fig.tight_layout()
###############################################################################
# And we can project these label time courses back to their original
# locations and see how the plot has been smoothed:
stc_back = mne.labels_to_stc(fname_aseg, label_tc, src=src)
stc_back.plot(src, subjects_dir=subjects_dir, mode='glass_brain')
###############################################################################
# Vector Source Estimates
# -----------------------
# If we choose to use ``pick_ori='vector'`` in
# :func:`apply_inverse <mne.minimum_norm.apply_inverse>`
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inv = read_inverse_operator(fname_inv)
stc = apply_inverse(evoked, inv, lambda2, 'dSPM', pick_ori='vector')
brain = stc.plot(subject='sample',
subjects_dir=subjects_dir,
initial_time=initial_time,
brain_kwargs=dict(silhouette=True))
# compute ROI degree
degree[si, ix] = mne.connectivity.degree(aec, threshold_prop=0.2)
# if not np.allclose(laplacian, degree[si, ix]):
# warnings.warn("mne.connectivity.degree NOT equal to laplacian")
print(f" Completed in {(time.time() - t0) / 60:0.1f} min")
h5io.write_hdf5(
out_fname,
dict(degree=degree[si], laplacian=laplacian[si]),
overwrite=True,
)
# visualize connectivity on fsaverage
for ix, (kk, vv) in enumerate(defaults.bands.items()):
grab = np.mean(degree[:, ix], axis=0) # avg across subjects for this band
this_stc = mne.labels_to_stc(rois, grab, src=src_fs)
assert this_stc.data.shape == (20484, 1)
brain = this_stc.plot(
subject="fsaverage",
clim=dict(kind="percent", lims=[75, 85, 95]),
colormap="gnuplot",
subjects_dir=defaults.subjects_dir,
views="dorsal",
hemi="both",
time_viewer=False,
show_traces=False,
smoothing_steps="nearest",
time_label="%s band" % kk,
)
brain.save_image(
op.join(
cv=cv,
n_permutations=n_permutations,
n_jobs=n_jobs,
random_state=0,
verbose=1)
print('Completed in %0.1f min:' % ((time.time() - t0) / 60., ))
print('Accuracy: %0.3f%%' % (100 * perf, ))
print('Significance: %f' % (p, ))
reg = clf.steps[1][1]
reg.fit(clf.steps[0][1].fit_transform(X), y=y)
coef = reg.coef_
coef.shape = (len(ages), len(measures), len(freq_idx), 448)
coef = np.abs(coef).max(0).max(0)
assert len(labels) == coef.shape[-1]
for ii, fi in enumerate(freq_idx):
stc = mne.labels_to_stc(labels, coef[ii])
brain = stc.plot(views=['lat', 'med'],
hemi='split',
smoothing_steps=1,
clim=dict(kind='value', lims=[0.012, 0.024, 0.036]),
colormap='viridis',
colorbar=False,
size=(800, 800),
time_label=freqs[fi])
brain.save_image('maxs_%s.png' % (freqs[fi], ))
brain.close()
if plot_correlations:
# naively compute correlation coefficients and plot a few
this_X = X - X.mean(0)
this_X /= np.linalg.norm(this_X, axis=0)
stcs, rois, fwd["src"], return_generator=True, verbose=True
)
# compute ROI level envelop power
aec = envelope_correlation(label_ts)
assert aec.shape == (len(rois), len(rois))
# compute ROI laplacian as per Ginset
# TODO cite paper
_, deg_lap = csgraph.laplacian(aec, return_diag=True)
data[si, ix] = deg_lap
# compute ROI degree
degree = mne.connectivity.degree(aec, threshold_prop=0.2)
# if not np.allclose(deg_lap, degree):
# warnings.warn("mne.connectivity.degree NOT equal to laplacian")
stc = mne.labels_to_stc(rois, degree)
stc = stc.in_label(
mne.Label(inv["src"][0]["vertno"], hemi="lh")
+ mne.Label(inv["src"][1]["vertno"], hemi="rh")
)
morph = mne.compute_source_morph(
stc,
subject_from=None,
subject_to="fsaverage",
subjects_dir=defaults.subjects_dir,
)
a_lst[kk].append(morph.apply(stc))
A_lst.append(a_lst)
arrayostcs = np.array([[v[ii] for ii in defaults.bands] for v in A_lst]).squeeze()
src = mne.read_source_spaces(
