def save_rois_connectivity(subject, args):
# atlas, mat_fname, mat_field, conditions, stat=STAT_DIFF, windows=0,
# labels_exclude=['unknown', 'corpuscallosum'], threshold=0, threshold_percentile=0,
# color_map='jet', norm_by_percentile=True, norm_percs=(1, 99), symetric_colors=True):
# args.atlas, args.mat_fname, args.mat_field, args.conditions, args.stat,
# args.windows, args.labels_exclude, args.threshold, args.threshold_percentile,
# args.color_map, args.norm_by_percentile, args.norm_percs)
d = dict()
data = sio.loadmat(args.mat_fname)[args.mat_field]
d['labels'] = lu.read_labels(subject,
SUBJECTS_DIR,
args.atlas,
exclude=args.labels_exclude,
sorted_according_to_annot_file=True)
d['locations'] = lu.calc_center_of_mass(d['labels'], ret_mat=True) * 1000
d['hemis'] = ['rh' if l.hemi == 'rh' else 'lh' for l in d['labels']]
d['labels'] = [l.name for l in d['labels']]
(d['con_colors'], d['con_indices'], d['con_names'], d['con_values'],
d['con_types'], d['data_max'],
d['data_min']) = calc_connections_colors(data, d['labels'], d['hemis'],
args)
# args.stat, args.conditions, args.windows, args.threshold,
# args.threshold_percentile, args.color_map, args.norm_by_percentile, args.norm_percs, args.symetric_colors)
d['conditions'] = args.conditions
np.savez(op.join(BLENDER_ROOT_DIR, subject, 'rois_con'), **d)
def calc_labels_center_of_mass(subject, atlas, read_from_annotation=True, surf_name='pial', labels_fol='', labels=None):
import csv
# if (read_from_annotation):
# labels = mne.read_labels_from_annot(subject, atlas, 'both', surf_name, subjects_dir=SUBJECTS_DIR)
# if len(labels) == 0:
# print('No labels were found in {} annotation file!'.format(atlas))
# else:
# labels = []
# if labels_fol == '':
# labels_fol = op.join(SUBJECTS_DIR, subject, 'label', atlas)
# for label_file in glob.glob(op.join(labels_fol, '*.label')):
# label = mne.read_label(label_file)
# labels.append(label)
# if len(labels) == 0:
# print('No labels were found in {}!'.format(labels_fol))
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas)
if len(labels) > 0:
center_of_mass = lu.calc_center_of_mass(labels)
with open(op.join(SUBJECTS_DIR, subject, 'label', '{}_center_of_mass.csv'.format(atlas)), 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
for label in labels:
writer.writerow([label.name, *center_of_mass[label.name]])
com_fname = op.join(SUBJECTS_DIR, subject, 'label', '{}_center_of_mass.pkl'.format(atlas))
blend_fname = op.join(MMVT_DIR, subject, '{}_center_of_mass.pkl'.format(atlas))
utils.save(center_of_mass, com_fname)
shutil.copyfile(com_fname, blend_fname)
return len(labels) > 0 and op.isfile(com_fname) and op.isfile(blend_fname)
def init(subject, atlas, n_jobs):
from src.utils import geometry_utils as gu
if not utils.both_hemi_files_exist(
op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format(
'{hemi}', atlas))):
anat.create_annotation(subject, atlas)
if not utils.both_hemi_files_exist(
op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format(
'{hemi}', atlas))):
raise Exception(
'Can\'t find the cortical atlas {} for subject {}'.format(
atlas, subject))
labels_vertices = find_rois.read_labels_vertices(SUBJECTS_DIR, subject,
atlas, n_jobs)
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas)
labels_names = [l.name for l in labels]
aseg_atlas_fname = op.join(SUBJECTS_DIR, subject, 'mri', 'aseg.mgz')
aseg_data = nib.load(aseg_atlas_fname).get_data()
lut = fu.import_freesurfer_lut()
pia_verts = {}
for hemi in ['rh', 'lh']:
pia_verts[hemi], _ = gu.read_surface(
op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial'.format(hemi)))
# pia_verts[hemi], _ = nib.freesurfer.read_geometry(
# op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial'.format(hemi)))
subs_center_of_mass, subs_names = calc_subcorticals_pos(
subject, aseg_data, lut)
labels_center_of_mass = lu.calc_center_of_mass(labels, ret_mat=True) * 1000
regions_center_of_mass = np.concatenate(
(labels_center_of_mass, subs_center_of_mass))
regions_names = labels_names + subs_names
# save_com_as_elecs(subject, regions_center_of_mass, regions_names, atlas)
# save_com_as_elecs(subject, subs_center_of_mass, subs_names, atlas)
return labels_vertices, regions_center_of_mass, regions_names, aseg_data, lut, pia_verts,
def calc_labels_center_of_mass(subject, atlas, read_from_annotation=True, surf_name='pial', labels_fol='', labels=None):
import csv
# if (read_from_annotation):
# labels = mne.read_labels_from_annot(subject, atlas, 'both', surf_name, subjects_dir=SUBJECTS_DIR)
# if len(labels) == 0:
# print('No labels were found in {} annotation file!'.format(atlas))
# else:
# labels = []
# if labels_fol == '':
# labels_fol = op.join(SUBJECTS_DIR, subject, 'label', atlas)
# for label_file in glob.glob(op.join(labels_fol, '*.label')):
# label = mne.read_label(label_file)
# labels.append(label)
# if len(labels) == 0:
# print('No labels were found in {}!'.format(labels_fol))
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas)
if len(labels) > 0:
center_of_mass = lu.calc_center_of_mass(labels)
with open(op.join(SUBJECTS_DIR, subject, 'label', '{}_center_of_mass.csv'.format(atlas)), 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
for label in labels:
writer.writerow([label.name, *center_of_mass[label.name]])
com_fname = op.join(SUBJECTS_DIR, subject, 'label', '{}_center_of_mass.pkl'.format(atlas))
blend_fname = op.join(MMVT_DIR, subject, '{}_center_of_mass.pkl'.format(atlas))
utils.save(center_of_mass, com_fname)
shutil.copyfile(com_fname, blend_fname)
return len(labels) > 0 and op.isfile(com_fname) and op.isfile(blend_fname)
def save_connectivity_to_blender(subject, atlas, data, conditions, stat, w=0, threshold=0, threshold_percentile=0):
d = {}
d['labels'] = lu.read_labels(subject, SUBJECTS_DIR, atlas, exclude=('unknown', 'corpuscallosum'),
sorted_according_to_annot_file=True)
d['locations'] = lu.calc_center_of_mass(d['labels'], ret_mat=True)
d['hemis'] = ['rh' if l.hemi == 'rh' else 'lh' for l in d['labels']]
d['labels'] = [l.name for l in d['labels']]
d['con_colors'], d['con_indices'], d['con_names'], d['con_values'], d['con_types'] = \
calc_connections_colors(data, d['labels'], d['hemis'], stat, w, threshold_percentile=threshold_percentile)
d['conditions'] = conditions
np.savez(op.join(BLENDER_ROOT_DIR, subject, 'rois_con'), **d)
def save_connectivity_to_blender(subject, atlas, data, conditions, stat, w=0, threshold=0, threshold_percentile=0):
d = {}
d['labels'] = lu.read_labels(subject, SUBJECTS_DIR, atlas, exclude=['unknown', 'corpuscallosum'],
sorted_according_to_annot_file=True)
d['locations'] = lu.calc_center_of_mass(d['labels'], ret_mat=True)
d['hemis'] = ['rh' if l.hemi == 'rh' else 'lh' for l in d['labels']]
d['labels'] = [l.name for l in d['labels']]
d['con_colors'], d['con_indices'], d['con_names'], d['con_values'], d['con_types'] = \
calc_connections_colors(data, d['labels'], d['hemis'], stat, w, threshold_percentile=threshold_percentile)
d['conditions'] = conditions
np.savez(op.join(BLENDER_ROOT_DIR, subject, 'rois_con'), **d)
def calc_distances_from_rois(subject, dist_threshold=0.05):
from scipy.spatial.distance import cdist
import nibabel as nib
dipoles_dict = utils.load(op.join(MMVT_DIR, subject, 'meg', 'dipoles.pkl'))
labels_times_fol = op.join(MMVT_DIR, subject, 'meg', 'time_accumulate')
labels = lu.read_labels(subject, SUBJECTS_DIR, 'laus125')
labels_center_of_mass = lu.calc_center_of_mass(labels)
labels_pos = np.array([labels_center_of_mass[l.name] for l in labels])
labels_dict = {l.name: labels_center_of_mass[l.name] for l in labels}
outer_skin_surf_fname = op.join(SUBJECTS_DIR, subject, 'surf', 'lh.seghead')
outer_skin_surf_verts, _ = nib.freesurfer.read_geometry(outer_skin_surf_fname)
for dipole_name, dipoles in dipoles_dict.items():
dipole_pos = np.array([dipoles[0][2], dipoles[0][3], dipoles[0][4]])
lables_times_fname = op.join(labels_times_fol, '{}_labels_times.txt'.format(dipole_name))
if not op.isfile(lables_times_fname):
print('Can\'t find {}!'.format(lables_times_fname))
continue
dists_from_outer_skin = np.min(cdist(outer_skin_surf_verts * 0.001, [dipole_pos]), 0)[0]
output_fname = op.join(labels_times_fol, '{}_labels_times_dists.txt'.format(dipole_name))
lines = utils.csv_file_reader(lables_times_fname, delimiter=':', skip_header=1)
output, dists = [], []
labels_dists = cdist(labels_pos, [dipole_pos])
dists_argmin = np.argmin(labels_dists, 0)[0]
dists_min = np.min(labels_dists, 0)[0]
closest_label = labels[dists_argmin].name
print('Parsing {} ({})'.format(dipole_name, closest_label))
for line in lines:
if len(line) == 0:
continue
elif len(line) != 2:
print('{}: Problem parsing "{}"'.format(lables_times_fname, line))
continue
label_name, label_time = line
label_pos = labels_dict.get(label_name, None)
if label_pos is not None:
dist_from_dipole = np.linalg.norm(dipole_pos - label_pos)
dists.append(dist_from_dipole)
else:
dist_from_dipole = -1
dists.append(np.nan)
output.append('{}: {} ({:.4f})'.format(label_name, label_time, dist_from_dipole))
for ind, dist in enumerate(dists):
if dist < dist_threshold:
output[ind] = '{} ***'.format(output[ind])
title = '{}: {} {:.4f} dist from outer skin: {:.4f} '.format(
dipole_name, closest_label, dists_min, dists_from_outer_skin)
utils.save_arr_to_file(output, output_fname, title)
def calc_labels_center_of_mass(subject, atlas, read_from_annotation=True, surf_name='pial', labels_fol='', labels=None):
import csv
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas)
if len(labels) > 0:
if np.all(labels[0].pos == 0):
verts = {}
for hemi in utils.HEMIS:
verts[hemi], _ = utils.read_pial_npz(subject, MMVT_DIR, hemi)
for label in labels:
label.pos = verts[label.hemi][label.vertices]
center_of_mass = lu.calc_center_of_mass(labels)
with open(op.join(SUBJECTS_DIR, subject, 'label', '{}_center_of_mass.csv'.format(atlas)), 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
for label in labels:
writer.writerow([label.name, *center_of_mass[label.name]])
com_fname = op.join(SUBJECTS_DIR, subject, 'label', '{}_center_of_mass.pkl'.format(atlas))
blend_fname = op.join(MMVT_DIR, subject, '{}_center_of_mass.pkl'.format(atlas))
utils.save(center_of_mass, com_fname)
shutil.copyfile(com_fname, blend_fname)
return len(labels) > 0 and op.isfile(com_fname) and op.isfile(blend_fname)
def save_rois_connectivity(subject, args):
# atlas, mat_fname, mat_field, conditions, stat=STAT_DIFF, windows=0,
# labels_exclude=['unknown', 'corpuscallosum'], threshold=0, threshold_percentile=0,
# color_map='jet', norm_by_percentile=True, norm_percs=(1, 99), symetric_colors=True):
# args.atlas, args.mat_fname, args.mat_field, args.conditions, args.stat,
# args.windows, args.labels_exclude, args.threshold, args.threshold_percentile,
# args.color_map, args.norm_by_percentile, args.norm_percs)
d = dict()
data = sio.loadmat(args.mat_fname)[args.mat_field]
d['labels'] = lu.read_labels(
subject, SUBJECTS_DIR, args.atlas, exclude=args.labels_exclude,sorted_according_to_annot_file=True)
d['locations'] = lu.calc_center_of_mass(d['labels'], ret_mat=True) * 1000
d['hemis'] = ['rh' if l.hemi == 'rh' else 'lh' for l in d['labels']]
d['labels'] = [l.name for l in d['labels']]
(d['con_colors'], d['con_indices'], d['con_names'], d['con_values'], d['con_types'],
d['data_max'], d['data_min']) = calc_connections_colors(data, d['labels'], d['hemis'], args)
# args.stat, args.conditions, args.windows, args.threshold,
# args.threshold_percentile, args.color_map, args.norm_by_percentile, args.norm_percs, args.symetric_colors)
d['conditions'] = args.conditions
np.savez(op.join(MMVT_DIR, subject, 'rois_con'), **d)
