def check_voxels_around_electrodes_in_group(ct_data, output_fol, threshold,
ct_header, brain_header):
(electrodes, names, hemis, threshold) = utils.load(
op.join(output_fol, '{}_electrodes.pkl'.format(int(threshold))))
groups, noise = utils.load(
op.join(output_fol, '{}_groups.pkl'.format(int(threshold))))
elcs_group, elcs_colors = [], []
# elcs_nei = np.zeros((len(utils.flat_list_of_lists(groups)), 3))
elcs_nei = []
groups_nei = []
colors = utils.get_distinct_colors(len(groups))
groups_names = ['{}-{}'.format(names[g[0]], names[g[-1]]) for g in groups]
for group_ind, (group, color) in enumerate(zip(groups, colors)):
group_voxels = fect.t1_ras_tkr_to_ct_voxels(
[electrodes[g] for g in group], ct_header, brain_header)
groups_elecs_nei = np.array([
fect.get_voxel_neighbors_ct_values(ct_data, elc_voxel, r=4)
for elc_voxel in group_voxels
])
elcs_group.append(
[np.mean(groups_elecs_nei),
np.var(groups_elecs_nei)])
# for elc_voxel in group_voxels:
# elc_nei = fect.get_voxel_neighbors_ct_values(ct_data, elc_voxel, r=2)
# # elcs_nei.append(elc_nei)
# elcs_nei.append([np.sum(elc_nei), np.mean(elc_nei), np.var(elc_nei)])
# elcs_group.append('{}-{}'.format(names[group[0]], names[group[-1]]))
# elcs_colors.append(color)
elcs_group = np.array(elcs_group)
groups_names = np.array(groups_names)
utils.plot_3d_scatter(elcs_group, names=groups_names)
# elcs_nei = np.array(elcs_nei)
# utils.plot_3d_PCA(elcs_nei, colors=elcs_colors, legend_labels=elcs_group)
print('asdf')
def check_voxels_around_electrodes(ct_data, output_fol, threshold, ct_header,
brain_header):
(electrodes, names, hemis, threshold) = utils.load(
op.join(output_fol, '{}_electrodes.pkl'.format(int(threshold))))
groups, noise = utils.load(
op.join(output_fol, '{}_groups.pkl'.format(int(threshold))))
ct_voxels = fect.t1_ras_tkr_to_ct_voxels(electrodes, ct_header,
brain_header)
nei_features = []
for ct_voxel in ct_voxels:
ct_voxel_nei = fect.get_voxel_neighbors_ct_values(ct_data,
ct_voxel,
r=4)
nei_features.append([np.mean(ct_voxel_nei), np.std(ct_voxel_nei)])
from sklearn import mixture
gmm = mixture.GaussianMixture(n_components=2, covariance_type='full')
gmm.fit(nei_features)
Y = gmm.predict_proba(nei_features)
centroids = gmm.means_
nei_features = np.array(nei_features)
fig = plt.figure()
plt.scatter(nei_features[:, 0], nei_features[:, 1])
plt.scatter(centroids[:, 0], centroids[:, 1], c='r')
plt.xlabel('mean')
plt.ylabel('std')
plt.show()
def mask_voxels_outside_brain(elc_name, output_fol, threshold, ct_header,
brain, aseg, user_fol, subject_fol):
(electrodes, names, hemis, threshold) = utils.load(
op.join(output_fol, '{}_electrodes.pkl'.format(int(threshold))))
# elc_ind = names.index(elc_name)
# t1_tkras_coords = np.array([electrodes[elc_ind]])
# ct_voxels = fect.t1_ras_tkr_to_ct_voxels(t1_tkras_coords, ct_header, brain.header)
ct_voxels = fect.t1_ras_tkr_to_ct_voxels(electrodes, ct_header,
brain.header)
voxels = fect.mask_voxels_outside_brain(ct_voxels, ct_header, brain,
user_fol, subject_fol, aseg)
print(voxels)
def check_if_outside_pial(threshold,
user_fol,
output_fol,
subject_fol,
ct_header,
brain,
aseg,
sigma=2):
(electrodes, names, hemis, threshold) = utils.load(
op.join(output_fol, '{}_electrodes.pkl'.format(int(threshold))))
all_voxels = fect.t1_ras_tkr_to_ct_voxels(electrodes, ct_header,
brain.header)
voxels = fect.t1_ras_tkr_to_ct_voxels(electrodes, ct_header, brain.header)
voxels_in, voxels_in_indices = fect.mask_voxels_outside_brain(
voxels, ct_header, brain, user_fol, subject_fol, aseg, None, sigma)
indices_outside_brain = list(
set(range(len(voxels))) - set(voxels_in_indices))
outside_voxels = all_voxels[indices_outside_brain]
outside_voxels_norm = [np.linalg.norm(v) for v in outside_voxels]
plt.hist(outside_voxels_norm, bins=40)
plt.show()
print('asdf')