def check_ply_files(subject):
ply_subject = op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial.ply')
npz_subject = op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial.npz')
ply_blender = op.join(MMVT_DIR, subject, 'surf', '{}.pial.ply')
npz_blender = op.join(MMVT_DIR, subject, 'surf', '{}.pial.npz')
ok = True
for hemi in HEMIS:
# print('reading {}'.format(ply_subject.format(hemi)))
verts1, faces1 = utils.read_ply_file(ply_subject.format(hemi), npz_subject.format(hemi))
# print('reading {}'.format(ply_blender.format(hemi)))
verts2, faces2 = utils.read_ply_file(ply_blender.format(hemi), npz_blender.format(hemi))
print('vertices: ply: {}, blender: {}'.format(verts1.shape[0], verts2.shape[0]))
print('faces: ply: {}, blender: {}'.format(faces1.shape[0], faces2.shape[0]))
ok = ok and verts1.shape[0] == verts2.shape[0] and faces1.shape[0]==faces2.shape[0]
return ok
def check_ply_files(subject):
ply_subject = op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial.ply')
npz_subject = op.join(SUBJECTS_DIR, subject, 'mmvt', '{}.pial.npz')
ply_blender = op.join(MMVT_DIR, subject, '{}.pial.ply')
npz_blender = op.join(MMVT_DIR, subject, '{}.pial.npz')
ok = True
for hemi in HEMIS:
# print('reading {}'.format(ply_subject.format(hemi)))
verts1, faces1 = utils.read_ply_file(ply_subject.format(hemi), npz_subject.format(hemi))
# print('reading {}'.format(ply_blender.format(hemi)))
verts2, faces2 = utils.read_ply_file(ply_blender.format(hemi), npz_blender.format(hemi))
print('vertices: ply: {}, blender: {}'.format(verts1.shape[0], verts2.shape[0]))
print('faces: ply: {}, blender: {}'.format(faces1.shape[0], faces2.shape[0]))
ok = ok and verts1.shape[0] == verts2.shape[0] and faces1.shape[0]==faces2.shape[0]
return ok
def _save_fmri_colors(subject,
hemi,
x,
threshold,
output_file='',
verts=None,
surf_name='pial'):
if verts is None:
# Try to read the hemi ply file to check if the vertices number is correct
ply_file = op.join(SUBJECTS_DIR, subject, 'surf',
'{}.{}.ply'.format(hemi, surf_name))
if op.isfile(ply_file):
verts, _ = utils.read_ply_file(ply_file)
if len(x) != verts.shape[0]:
raise Exception(
"fMRI contrast map and the hemi doens't have the same vertices number!"
)
else:
print("No ply file, Can't check the vertices number")
colors = utils.arr_to_colors_two_colors_maps(x,
cm_big='YlOrRd',
cm_small='PuBu',
threshold=threshold,
default_val=1)
colors = np.hstack((x.reshape((len(x), 1)), colors))
if output_file != '':
op.join(BLENDER_ROOT_DIR, subject, 'fmri_{}.npy'.format(hemi))
print('Saving {}'.format(output_file))
np.save(output_file, colors)
def init_clusters(subject, contrast_name, input_fol):
input_fname = op.join(input_fol,
'fmri_{}_{}.npy'.format(contrast_name, '{hemi}'))
contrast_per_hemi, verts_per_hemi = {}, {}
for hemi in utils.HEMIS:
fmri_fname = input_fname.format(hemi=hemi)
if utils.file_type(input_fname) == 'npy':
x = np.load(fmri_fname)
contrast_per_hemi[hemi] = x[:, 0]
else:
# try nibabel
x = nib.load(fmri_fname)
contrast_per_hemi[hemi] = x.get_data().ravel()
pial_npz_fname = op.join(BLENDER_ROOT_DIR, subject,
'{}.pial.npz'.format(hemi))
if not op.isfile(pial_npz_fname):
print('No pial npz file (), creating one'.format(pial_npz_fname))
verts, faces = utils.read_ply_file(
op.join(BLENDER_ROOT_DIR, subject, '{}.pial.ply'.format(hemi)))
np.savez(pial_npz_fname[:-4], verts=verts, faces=faces)
d = np.load(pial_npz_fname)
verts_per_hemi[hemi] = d['verts']
connectivity_fname = op.join(BLENDER_ROOT_DIR, subject,
'spatial_connectivity.pkl')
if not op.isfile(connectivity_fname):
from src.preproc import anatomy_preproc
anatomy_preproc.create_spatial_connectivity(subject)
connectivity_per_hemi = utils.load(connectivity_fname)
return contrast_per_hemi, connectivity_per_hemi, verts_per_hemi
def calc_faces_verts_dic(subject, atlas, overwrite=False):
# hemis_plus = HEMIS + ['cortex']
ply_files = [op.join(MMVT_DIR, subject, 'surf', '{}.pial.npz'.format(hemi)) for hemi in utils.HEMIS]
out_files = [op.join(MMVT_DIR, subject, 'faces_verts_{}.npy'.format(hemi)) for hemi in utils.HEMIS]
subcortical_plys = glob.glob(op.join(MMVT_DIR, subject, 'subcortical', '*.ply'))
errors = {}
if len(subcortical_plys) > 0:
faces_verts_dic_fnames = [op.join(MMVT_DIR, subject, 'subcortical', '{}_faces_verts.npy'.format(
utils.namebase(ply))) for ply in subcortical_plys]
ply_files.extend(subcortical_plys)
out_files.extend(faces_verts_dic_fnames)
for hemi in utils.HEMIS:
labels_plys = glob.glob(op.join(MMVT_DIR, subject, '{}.pial.{}'.format(atlas, hemi), '*.ply'))
if len(labels_plys) > 0:
faces_verts_dic_fnames = [op.join(MMVT_DIR, subject, '{}.pial.{}'.format(atlas, hemi), '{}_faces_verts.npy'.format(
utils.namebase(ply))) for ply in labels_plys]
ply_files.extend(labels_plys)
out_files.extend(faces_verts_dic_fnames)
for ply_file, out_file in zip(ply_files, out_files):
if not overwrite and op.isfile(out_file):
# print('{} already exist.'.format(out_file))
continue
# ply_file = op.join(SUBJECTS_DIR, subject,'surf', '{}.pial.ply'.format(hemi))
# print('preparing a lookup table for {}'.format(ply_file))
verts, faces = utils.read_ply_file(ply_file)
errors = utils.calc_ply_faces_verts(verts, faces, out_file, overwrite, utils.namebase(ply_file), errors)
if len(errors) > 0:
for k, message in errors.items():
print('{}: {}'.format(k, message))
return len(errors) == 0
def freesurfer_surface_to_blender_surface(subject, hemi='both', overwrite=False):
for hemi in utils.get_hemis(hemi):
utils.make_dir(op.join(MMVT_DIR, subject, 'surf'))
for surf_type in ['inflated', 'pial']:
surf_name = op.join(SUBJECTS_DIR, subject, 'surf', '{}.{}'.format(hemi, surf_type))
surf_wavefront_name = '{}.asc'.format(surf_name)
surf_new_name = '{}.srf'.format(surf_name)
hemi_ply_fname = '{}.ply'.format(surf_name)
mmvt_hemi_ply_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_type))
mmvt_hemi_npz_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.npz'.format(hemi, surf_type))
if overwrite or not op.isfile(mmvt_hemi_ply_fname) and not op.isfile(mmvt_hemi_npz_fname):
print('{} {}: convert srf to asc'.format(hemi, surf_type))
utils.run_script('mris_convert {} {}'.format(surf_name, surf_wavefront_name))
os.rename(surf_wavefront_name, surf_new_name)
print('{} {}: convert asc to ply'.format(hemi, surf_type))
convert_hemis_srf_to_ply(subject, hemi, surf_type)
if op.isfile(mmvt_hemi_ply_fname):
os.remove(mmvt_hemi_ply_fname)
shutil.copy(hemi_ply_fname, mmvt_hemi_ply_fname)
ply_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_type))
if not op.isfile(mmvt_hemi_npz_fname):
verts, faces = utils.read_ply_file(ply_fname)
np.savez(mmvt_hemi_npz_fname, verts=verts, faces=faces)
return utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.pial.ply')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.pial.npz')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.inflated.ply')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.inflated.npz'))
def init_clusters(subject, contrast_name, input_fol):
input_fname = op.join(input_fol, 'fmri_{}_{}.npy'.format(contrast_name, '{hemi}'))
contrast_per_hemi, verts_per_hemi = {}, {}
for hemi in utils.HEMIS:
fmri_fname = input_fname.format(hemi=hemi)
if utils.file_type(input_fname) == 'npy':
x = np.load(fmri_fname)
contrast_per_hemi[hemi] = x[:, 0]
else:
# try nibabel
x = nib.load(fmri_fname)
contrast_per_hemi[hemi] = x.get_data().ravel()
pial_npz_fname = op.join(BLENDER_ROOT_DIR, subject, '{}.pial.npz'.format(hemi))
if not op.isfile(pial_npz_fname):
print('No pial npz file (), creating one'.format(pial_npz_fname))
verts, faces = utils.read_ply_file(op.join(BLENDER_ROOT_DIR, subject, '{}.pial.ply'.format(hemi)))
np.savez(pial_npz_fname[:-4], verts=verts, faces=faces)
d = np.load(pial_npz_fname)
verts_per_hemi[hemi] = d['verts']
connectivity_fname = op.join(BLENDER_ROOT_DIR, subject, 'spatial_connectivity.pkl')
if not op.isfile(connectivity_fname):
from src.preproc import anatomy_preproc
anatomy_preproc.create_spatial_connectivity(subject)
connectivity_per_hemi = utils.load(connectivity_fname)
return contrast_per_hemi, connectivity_per_hemi, verts_per_hemi
def transform_to_another_subject(subject, region, subjects_dir):
colin27_xfm = tu.get_talxfm('colin27', subjects_dir)
xfm = tu.get_talxfm('colin27', subjects_dir)
for hemi in ['lh', 'rh']:
verts, faces = utils.read_ply_file(op.join(MMVT_DIR, 'colin27', 'subcortical', '{}_{}.ply'.format(region, hemi)))
verts = tu.apply_trans(colin27_xfm, verts)
verts = tu.apply_trans(np.linalg.inv(xfm), verts)
utils.write_ply_file(verts, faces, op.join(MMVT_DIR, subject, 'subcortical', '{}_{}.ply'.format(region, hemi)))
def transform_to_another_subject(subject, region, subjects_dir):
colin27_xfm = tu.get_talxfm('colin27', subjects_dir)
xfm = tu.get_talxfm('colin27', subjects_dir)
for hemi in ['lh', 'rh']:
verts, faces = utils.read_ply_file(op.join(MMVT_DIR, 'colin27', 'subcortical', '{}_{}.ply'.format(region, hemi)))
verts = tu.apply_trans(colin27_xfm, verts)
verts = tu.apply_trans(np.linalg.inv(xfm), verts)
utils.write_ply_file(verts, faces, op.join(MMVT_DIR, subject, 'subcortical', '{}_{}.ply'.format(region, hemi)))
def transform_to_another_subject(subject, region, subjects_dir):
import mne.source_space
import mne.transforms
colin27_xfm = mne.source_space._read_talxfm('colin27', subjects_dir, 'nibabel')
xfm = mne.source_space._read_talxfm(subject, subjects_dir, 'nibabel')
for hemi in ['lh', 'rh']:
verts, faces = utils.read_ply_file(op.join(MMVT_DIR, 'colin27', 'subcortical', '{}_{}.ply'.format(region, hemi)))
verts = apply_trans(colin27_xfm['trans'], verts)
verts = apply_trans(np.linalg.inv(xfm['trans']), verts)
utils.write_ply_file(verts, faces, op.join(MMVT_DIR, subject, 'subcortical', '{}_{}.ply'.format(region, hemi)))
def convert_and_rename_subcortical_files(subject, fol, new_fol, lookup):
obj_files = glob.glob(op.join(fol, '*.srf'))
utils.delete_folder_files(new_fol)
for obj_file in obj_files:
num = int(op.basename(obj_file)[:-4].split('_')[-1])
new_name = lookup.get(num, '')
if new_name != '':
utils.srf2ply(obj_file, op.join(new_fol, '{}.ply'.format(new_name)))
verts, faces = utils.read_ply_file(op.join(new_fol, '{}.ply'.format(new_name)))
np.savez(op.join(new_fol, '{}.npz'.format(new_name)), verts=verts, faces=faces)
copy_subcorticals_to_blender(new_fol, subject)
def convert_and_rename_subcortical_files(subject, fol, new_fol, lookup, mmvt_subcorticals_fol_name='subcortical'):
obj_files = glob.glob(op.join(fol, '*.srf'))
utils.delete_folder_files(new_fol)
for obj_file in obj_files:
num = int(op.basename(obj_file)[:-4].split('_')[-1])
new_name = lookup.get(num, '')
if new_name != '':
utils.srf2ply(obj_file, op.join(new_fol, '{}.ply'.format(new_name)))
verts, faces = utils.read_ply_file(op.join(new_fol, '{}.ply'.format(new_name)))
np.savez(op.join(new_fol, '{}.npz'.format(new_name)), verts=verts, faces=faces)
copy_subcorticals_to_mmvt(new_fol, subject, mmvt_subcorticals_fol_name)
def create_faces_verts(subject):
skull_fol = op.join(MMVT_DIR, subject, 'skull')
errors = {}
for skull_surf in ['inner_skull', 'outer_skull']:
ply_fname = op.join(skull_fol, '{}.ply'.format(skull_surf))
verts, faces = utils.read_ply_file(ply_fname)
faces_verts_fname = op.join(skull_fol,
'faces_verts_{}.npy'.format(skull_surf))
errors = utils.calc_ply_faces_verts(verts, faces,
faces_verts_fname, False,
utils.namebase(ply_fname), errors)
if len(errors) > 0:
for k, message in errors.items():
print('{}: {}'.format(k, message))
def convert_and_rename_subcortical_files(subject, fol, new_fol, lookup):
obj_files = glob.glob(op.join(fol, '*.srf'))
utils.delete_folder_files(new_fol)
for obj_file in obj_files:
num = int(op.basename(obj_file)[:-4].split('_')[-1])
new_name = lookup.get(num, '')
if new_name != '':
utils.srf2ply(obj_file, op.join(new_fol, '{}.ply'.format(new_name)))
verts, faces = utils.read_ply_file(op.join(new_fol, '{}.ply'.format(new_name)))
np.savez(op.join(new_fol, '{}.npz'.format(new_name)), verts=verts, faces=faces)
blender_fol = op.join(MMVT_DIR, subject, 'subcortical')
if op.isdir(blender_fol):
shutil.rmtree(blender_fol)
shutil.copytree(new_fol, blender_fol)
def calculate_subcorticals_activity(subject, volume_file, subcortical_codes_file='', aseg_stats_file_name='',
method='max', k_points=100, do_plot=False):
x = nib.load(volume_file)
x_data = x.get_data()
if do_plot:
fig = plt.figure()
ax = Axes3D(fig)
sig_subs = []
if subcortical_codes_file != '':
subcortical_codes = np.genfromtxt(subcortical_codes_file, dtype=str, delimiter=',')
seg_labels = map(str, subcortical_codes[:, 0])
elif aseg_stats_file_name != '':
aseg_stats = np.genfromtxt(aseg_stats_file_name, dtype=str, delimiter=',', skip_header=1)
seg_labels = map(str, aseg_stats[:, 0])
else:
raise Exception('No segmentation file!')
# Find the segmentation file
aseg_fname = op.join(SUBJECTS_DIR, subject, 'mri', 'aseg.mgz')
aseg = nib.load(aseg_fname)
aseg_hdr = aseg.get_header()
out_folder = op.join(SUBJECTS_DIR, subject, 'subcortical_fmri_activity')
if not op.isdir(out_folder):
os.mkdir(out_folder)
sub_cortical_generator = utils.sub_cortical_voxels_generator(aseg, seg_labels, 5, False, FREE_SURFER_HOME)
for pts, seg_name, seg_id in sub_cortical_generator:
print(seg_name)
verts, _ = utils.read_ply_file(op.join(SUBJECTS_DIR, subject, 'subcortical', '{}.ply'.format(seg_name)))
vals = np.array([x_data[i, j, k] for i, j, k in pts])
is_sig = np.max(np.abs(vals)) >= 2
print(seg_name, seg_id, np.mean(vals), is_sig)
pts = utils.transform_voxels_to_RAS(aseg_hdr, pts)
# plot_points(verts,pts)
verts_vals = calc_vert_vals(verts, pts, vals, method=method, k_points=k_points)
print('verts vals: {}+-{}'.format(verts_vals.mean(), verts_vals.std()))
if sum(abs(verts_vals)>2) > 0:
sig_subs.append(seg_name)
verts_colors = utils.arr_to_colors_two_colors_maps(verts_vals, threshold=2)
verts_data = np.hstack((np.reshape(verts_vals, (len(verts_vals), 1)), verts_colors))
np.save(op.join(out_folder, seg_name), verts_data)
if do_plot:
plot_points(verts, colors=verts_colors, fig_name=seg_name, ax=ax)
# print(pts)
utils.rmtree(op.join(BLENDER_ROOT_DIR, subject, 'subcortical_fmri_activity'))
shutil.copytree(out_folder, op.join(BLENDER_ROOT_DIR, subject, 'subcortical_fmri_activity'))
if do_plot:
plt.savefig('/home/noam/subjects/mri/mg78/subcortical_fmri_activity/figures/brain.jpg')
plt.show()
def calc_faces_verts_dic(subject, atlas, overwrite=False):
# hemis_plus = HEMIS + ['cortex']
ply_files = [op.join(MMVT_DIR, subject, 'surf', '{}.pial.npz'.format(hemi)) for hemi in utils.HEMIS]
out_files = [op.join(MMVT_DIR, subject, 'faces_verts_{}.npy'.format(hemi)) for hemi in utils.HEMIS]
subcortical_plys = glob.glob(op.join(MMVT_DIR, subject, 'subcortical', '*.ply'))
errors = []
if len(subcortical_plys) > 0:
faces_verts_dic_fnames = [op.join(MMVT_DIR, subject, 'subcortical', '{}_faces_verts.npy'.format(
utils.namebase(ply))) for ply in subcortical_plys]
ply_files.extend(subcortical_plys)
out_files.extend(faces_verts_dic_fnames)
for hemi in utils.HEMIS:
labels_plys = glob.glob(op.join(MMVT_DIR, subject, '{}.pial.{}'.format(atlas, hemi), '*.ply'))
if len(labels_plys) > 0:
faces_verts_dic_fnames = [op.join(MMVT_DIR, subject, '{}.pial.{}'.format(atlas, hemi), '{}_faces_verts.npy'.format(
utils.namebase(ply))) for ply in labels_plys]
ply_files.extend(labels_plys)
out_files.extend(faces_verts_dic_fnames)
for ply_file, out_file in zip(ply_files, out_files):
if not overwrite and op.isfile(out_file):
# print('{} already exist.'.format(out_file))
continue
# ply_file = op.join(SUBJECTS_DIR, subject,'surf', '{}.pial.ply'.format(hemi))
# print('preparing a lookup table for {}'.format(ply_file))
verts, faces = utils.read_ply_file(ply_file)
_faces = faces.ravel()
print('{}: verts: {}, faces: {}, faces ravel: {}'.format(utils.namebase(ply_file), verts.shape[0], faces.shape[0], len(_faces)))
faces_arg_sort = np.argsort(_faces)
faces_sort = np.sort(_faces)
faces_count = Counter(faces_sort)
max_len = max([v for v in faces_count.values()])
lookup = np.ones((verts.shape[0], max_len)) * -1
diff = np.diff(faces_sort)
n = 0
for ind, (k, v) in enumerate(zip(faces_sort, faces_arg_sort)):
lookup[k, n] = v
n = 0 if ind < len(diff) and diff[ind] > 0 else n+1
# print('writing {}'.format(out_file))
np.save(out_file, lookup.astype(np.int))
print('{} max lookup val: {}'.format(utils.namebase(ply_file), int(np.max(lookup))))
if len(_faces) != int(np.max(lookup)) + 1:
errors[utils.namebase(ply_file)] = 'Wrong values in lookup table! ' + \
'faces ravel: {}, max looup val: {}'.format(len(_faces), int(np.max(lookup)))
if len(errors) > 0:
for k, message in errors.items():
print('{}: {}'.format(k, message))
return len(errors) == 0
def _save_fmri_colors(subject, hemi, x, threshold, output_file='', verts=None, surf_name='pial'):
if verts is None:
# Try to read the hemi ply file to check if the vertices number is correct
ply_file = op.join(SUBJECTS_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_name))
if op.isfile(ply_file):
verts, _ = utils.read_ply_file(ply_file)
if len(x) != verts.shape[0]:
raise Exception("fMRI contrast map and the hemi doens't have the same vertices number!")
else:
print("No ply file, Can't check the vertices number")
colors = utils.arr_to_colors_two_colors_maps(x, cm_big='YlOrRd', cm_small='PuBu',
threshold=threshold, default_val=1)
colors = np.hstack((x.reshape((len(x), 1)), colors))
if output_file != '':
op.join(BLENDER_ROOT_DIR, subject, 'fmri_{}.npy'.format(hemi))
print('Saving {}'.format(output_file))
np.save(output_file, colors)
def load_surf(subject, mmvt_dir, subjects_dir):
verts = {}
for hemi in HEMIS:
if op.isfile(
op.join(mmvt_dir, subject, 'surf',
'{}.pial.npz'.format(hemi))):
hemi_verts, _ = utils.read_pial(subject, mmvt_dir, hemi)
elif op.isfile(
op.join(subjects_dir, subject, 'surf',
'{}.pial.ply'.format(hemi))):
hemis_verts, _ = utils.read_ply_file(
op.join(subjects_dir, subject, 'surf',
'{}.pial.ply'.format(hemi)))
else:
print("Can't find {} pial ply/npz files!".format(hemi))
return False
verts[hemi] = hemi_verts
return verts
def convert_perecelated_cortex(subject, aparc_name, surf_type='pial', overwrite_ply_files=False, hemi='both'):
lookup = {}
for hemi in utils.get_hemis(hemi):
lookup[hemi] = create_labels_lookup(subject, hemi, aparc_name)
if len(lookup[hemi]) == 0:
continue
srf_fol = op.join(SUBJECTS_DIR, subject,'{}.{}.{}'.format(aparc_name, surf_type, hemi))
ply_fol = op.join(SUBJECTS_DIR, subject,'{}_{}_{}_ply'.format(aparc_name, surf_type, hemi))
blender_fol = op.join(MMVT_DIR, subject,'{}.{}.{}'.format(aparc_name, surf_type, hemi))
utils.convert_srf_files_to_ply(srf_fol, overwrite_ply_files)
rename_cortical(lookup, srf_fol, ply_fol)
if surf_type == 'inflated':
for ply_fname in glob.glob(op.join(ply_fol, '*.ply')):
verts, faces = utils.read_ply_file(ply_fname)
verts_offset = 5.5 if hemi == 'rh' else -5.5
verts[:, 0] = verts[:, 0] + verts_offset
utils.write_ply_file(verts, faces, ply_fname)
utils.rmtree(blender_fol)
shutil.copytree(ply_fol, blender_fol)
return lookup
def convert_perecelated_cortex(subject, aparc_name, surf_type='pial', overwrite_ply_files=False, hemi='both'):
lookup = {}
for hemi in utils.get_hemis(hemi):
lookup[hemi] = create_labels_lookup(subject, hemi, aparc_name)
if len(lookup[hemi]) == 0:
continue
srf_fol = op.join(SUBJECTS_DIR, subject,'{}.{}.{}'.format(aparc_name, surf_type, hemi))
ply_fol = op.join(SUBJECTS_DIR, subject,'{}_{}_{}_ply'.format(aparc_name, surf_type, hemi))
blender_fol = op.join(MMVT_DIR, subject,'{}.{}.{}'.format(aparc_name, surf_type, hemi))
utils.convert_srf_files_to_ply(srf_fol, overwrite_ply_files)
rename_cortical(lookup, srf_fol, ply_fol)
if surf_type == 'inflated':
for ply_fname in glob.glob(op.join(ply_fol, '*.ply')):
verts, faces = utils.read_ply_file(ply_fname)
verts_offset = 5.5 if hemi == 'rh' else -5.5
verts[:, 0] = verts[:, 0] + verts_offset
utils.write_ply_file(verts, faces, ply_fname)
utils.rmtree(blender_fol)
shutil.copytree(ply_fol, blender_fol)
return lookup
def freesurfer_surface_to_blender_surface(subject, hemi='both', overwrite=False):
# verts, faces = {}, {}
for hemi in utils.get_hemis(hemi):
utils.make_dir(op.join(MMVT_DIR, subject, 'surf'))
for surf_type in ['inflated', 'pial']:
surf_name = op.join(SUBJECTS_DIR, subject, 'surf', '{}.{}'.format(hemi, surf_type))
surf_wavefront_name = '{}.asc'.format(surf_name)
surf_new_name = '{}.srf'.format(surf_name)
hemi_ply_fname = '{}.ply'.format(surf_name)
mmvt_hemi_ply_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_type))
mmvt_hemi_npz_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.npz'.format(hemi, surf_type))
if overwrite or not op.isfile(mmvt_hemi_ply_fname) and not op.isfile(mmvt_hemi_npz_fname):
print('{}: convert srf to asc'.format(hemi))
utils.run_script('mris_convert {} {}'.format(surf_name, surf_wavefront_name))
os.rename(surf_wavefront_name, surf_new_name)
print('{}: convert asc to ply'.format(hemi))
convert_hemis_srf_to_ply(subject, hemi, surf_type)
# if surf_type == 'inflated':
# verts, faces = utils.read_ply_file(hemi_ply_fname)
# verts_offset = 5.5 if hemi == 'rh' else -5.5
# verts[:, 0] = verts[:, 0] + verts_offset
# utils.write_ply_file(verts, faces, '{}_offset.ply'.format(surf_name))
if op.isfile(mmvt_hemi_ply_fname):
os.remove(mmvt_hemi_ply_fname)
shutil.copy(hemi_ply_fname, mmvt_hemi_ply_fname)
ply_fname = op.join(MMVT_DIR, subject, 'surf', '{}.{}.ply'.format(hemi, surf_type))
if not op.isfile(mmvt_hemi_npz_fname):
verts, faces = utils.read_ply_file(ply_fname)
np.savez(mmvt_hemi_npz_fname, verts=verts, faces=faces)
# verts[hemi], faces[hemi] = utils.read_ply_file(mmvt_hemi_npz_fname)
# if not op.isfile(op.join(MMVT_DIR, subject, 'cortex.pial.npz')):
# faces['rh'] += np.max(faces['lh']) + 1
# verts_cortex = np.vstack((verts['lh'], verts['rh']))
# faces_cortex = np.vstack((faces['lh'], faces['rh']))
# utils.write_ply_file(verts_cortex, faces_cortex, op.join(MMVT_DIR, subject, 'cortex.pial.ply'))
# np.savez(op.join(MMVT_DIR, subject, 'cortex.pial.npz'), verts=verts_cortex, faces=faces_cortex)
return utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.pial.ply')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.pial.npz')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.inflated.ply')) and \
utils.both_hemi_files_exist(op.join(MMVT_DIR, subject, 'surf', '{hemi}.inflated.npz'))
def calc_faces_verts_dic(subject, overwrite=False):
ply_files = [op.join(SUBJECTS_DIR, subject,'surf', '{}.pial.ply'.format(hemi)) for hemi in HEMIS]
out_files = [op.join(MMVT_DIR, subject, 'faces_verts_{}.npy'.format(hemi)) for hemi in HEMIS]
subcortical_plys = glob.glob(op.join(MMVT_DIR, subject, 'subcortical', '*.ply'))
errors = []
if len(subcortical_plys) > 0:
faces_verts_dic_fnames = [op.join(MMVT_DIR, subject, 'subcortical', '{}_faces_verts.npy'.format(
utils.namebase(ply))) for ply in subcortical_plys]
ply_files.extend(subcortical_plys)
out_files.extend(faces_verts_dic_fnames)
for ply_file, out_file in zip(ply_files, out_files):
if not overwrite and op.isfile(out_file):
# print('{} already exist.'.format(out_file))
continue
# ply_file = op.join(SUBJECTS_DIR, subject,'surf', '{}.pial.ply'.format(hemi))
# print('preparing a lookup table for {}'.format(ply_file))
verts, faces = utils.read_ply_file(ply_file)
_faces = faces.ravel()
print('{}: verts: {}, faces: {}, faces ravel: {}'.format(utils.namebase(ply_file), verts.shape[0], faces.shape[0], len(_faces)))
faces_arg_sort = np.argsort(_faces)
faces_sort = np.sort(_faces)
faces_count = Counter(faces_sort)
max_len = max([v for v in faces_count.values()])
lookup = np.ones((verts.shape[0], max_len)) * -1
diff = np.diff(faces_sort)
n = 0
for ind, (k, v) in enumerate(zip(faces_sort, faces_arg_sort)):
lookup[k, n] = v
n = 0 if ind<len(diff) and diff[ind] > 0 else n+1
# print('writing {}'.format(out_file))
np.save(out_file, lookup.astype(np.int))
print('{} max lookup val: {}'.format(utils.namebase(ply_file), int(np.max(lookup))))
if len(_faces) != int(np.max(lookup)) + 1:
errors[utils.namebase(ply_file)] = 'Wrong values in lookup table! ' + \
'faces ravel: {}, max looup val: {}'.format(len(_faces), int(np.max(lookup)))
if len(errors) > 0:
for k, message in errors.items():
print('{}: {}'.format(k, message))
return len(errors) == 0
def freesurfer_surface_to_blender_surface(subject, hemi='both', overwrite=False):
for hemi in utils.get_hemis(hemi):
surf_name = op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial'.format(hemi))
surf_wavefront_name = '{}.asc'.format(surf_name)
surf_new_name = '{}.srf'.format(surf_name)
hemi_ply_fname = '{}.ply'.format(surf_name)
mmvt_hemi_ply_fname = op.join(MMVT_DIR, subject, '{}.pial.ply'.format(hemi))
if overwrite or not op.isfile(hemi_ply_fname):
print('{}: convert srf to asc'.format(hemi))
utils.run_script('mris_convert {} {}'.format(surf_name, surf_wavefront_name))
os.rename(surf_wavefront_name, surf_new_name)
print('{}: convert asc to ply'.format(hemi))
convert_hemis_srf_to_ply(subject, hemi)
for hemi in utils.get_hemis(hemi):
if not op.isfile(mmvt_hemi_ply_fname):
shutil.copy(hemi_ply_fname, mmvt_hemi_ply_fname)
ply_fname = op.join(SUBJECTS_DIR, subject, 'surf', '{}.pial.ply'.format(hemi))
verts, faces = utils.read_ply_file(ply_fname)
np.savez(op.join(SUBJECTS_DIR, subject, 'mmvt', '{}.pial'.format(hemi)), verts=verts, faces=faces)
shutil.copyfile(op.join(SUBJECTS_DIR, subject, 'mmvt', '{}.pial.npz'.format(hemi)),
op.join(MMVT_DIR, subject, '{}.pial.npz'.format(hemi)))
return utils.both_hemi_files_exist(op.join(SUBJECTS_DIR, subject, 'surf', '{hemi}.pial.ply'))
def calculate_subcorticals_activity(subject,
volume_file,
subcortical_codes_file='',
aseg_stats_file_name='',
method='max',
k_points=100,
do_plot=False):
x = nib.load(volume_file)
x_data = x.get_data()
if do_plot:
fig = plt.figure()
ax = Axes3D(fig)
sig_subs = []
if subcortical_codes_file != '':
subcortical_codes = np.genfromtxt(subcortical_codes_file,
dtype=str,
delimiter=',')
seg_labels = map(str, subcortical_codes[:, 0])
elif aseg_stats_file_name != '':
aseg_stats = np.genfromtxt(aseg_stats_file_name,
dtype=str,
delimiter=',',
skip_header=1)
seg_labels = map(str, aseg_stats[:, 0])
else:
raise Exception('No segmentation file!')
# Find the segmentation file
aseg_fname = op.join(SUBJECTS_DIR, subject, 'mri', 'aseg.mgz')
aseg = nib.load(aseg_fname)
aseg_hdr = aseg.get_header()
out_folder = op.join(SUBJECTS_DIR, subject, 'subcortical_fmri_activity')
if not op.isdir(out_folder):
os.mkdir(out_folder)
sub_cortical_generator = utils.sub_cortical_voxels_generator(
aseg, seg_labels, 5, False, FREE_SURFER_HOME)
for pts, seg_name, seg_id in sub_cortical_generator:
print(seg_name)
verts, _ = utils.read_ply_file(
op.join(SUBJECTS_DIR, subject, 'subcortical',
'{}.ply'.format(seg_name)))
vals = np.array([x_data[i, j, k] for i, j, k in pts])
is_sig = np.max(np.abs(vals)) >= 2
print(seg_name, seg_id, np.mean(vals), is_sig)
pts = utils.transform_voxels_to_RAS(aseg_hdr, pts)
# plot_points(verts,pts)
verts_vals = calc_vert_vals(verts,
pts,
vals,
method=method,
k_points=k_points)
print('verts vals: {}+-{}'.format(verts_vals.mean(), verts_vals.std()))
if sum(abs(verts_vals) > 2) > 0:
sig_subs.append(seg_name)
verts_colors = utils.arr_to_colors_two_colors_maps(verts_vals,
threshold=2)
verts_data = np.hstack(
(np.reshape(verts_vals, (len(verts_vals), 1)), verts_colors))
np.save(op.join(out_folder, seg_name), verts_data)
if do_plot:
plot_points(verts, colors=verts_colors, fig_name=seg_name, ax=ax)
# print(pts)
utils.rmtree(
op.join(BLENDER_ROOT_DIR, subject, 'subcortical_fmri_activity'))
shutil.copytree(
out_folder,
op.join(BLENDER_ROOT_DIR, subject, 'subcortical_fmri_activity'))
if do_plot:
plt.savefig(
'/home/noam/subjects/mri/mg78/subcortical_fmri_activity/figures/brain.jpg'
)
plt.show()
def recreate_mesh_faces_verts(subject, ply_fname):
verts, faces = utils.read_ply_file(ply_fname)
faces_verts_out_fname = op.join(MMVT_DIR, subject, 'eeg', 'eeg_faces_verts.npy')
utils.calc_ply_faces_verts(verts, faces, faces_verts_out_fname, True,
utils.namebase(faces_verts_out_fname))
def parcelate(subject,
atlas,
hemi,
surface_type,
vertices_labels_ids_lookup=None,
overwrite_vertices_labels_lookup=False):
output_fol = op.join(MMVT_DIR, subject, 'labels',
'{}.{}.{}'.format(atlas, surface_type, hemi))
utils.make_dir(output_fol)
vtx, fac = utils.read_ply_file(
op.join(MMVT_DIR, subject, 'surf',
'{}.{}.ply'.format(hemi, surface_type)))
if vertices_labels_ids_lookup is None or overwrite_vertices_labels_lookup:
vertices_labels_ids_lookup = lu.create_vertices_labels_lookup(
subject, atlas, True, overwrite_vertices_labels_lookup)[hemi]
labels = lu.read_labels(subject, SUBJECTS_DIR, atlas, hemi=hemi)
if 'unknown-{}'.format(hemi) not in [l.name for l in labels]:
labels.append(lu.Label([], name='unknown-{}'.format(hemi), hemi=hemi))
nV = vtx.shape[0]
nF = fac.shape[0]
nL = len(labels)
# print('The number of unique labels is {}'.format(nL))
vtxL = [[] for _ in range(nL)]
facL = [[] for _ in range(nL)]
now = time.time()
for f in range(nF):
utils.time_to_go(now, f, nF, runs_num_to_print=50000)
# Current face & labels
Cfac = fac[f]
Cidx = [vertices_labels_ids_lookup[vert_ind] for vert_ind in Cfac]
# Depending on how many vertices of the current face
# are in different labels, behave differently
# nuCidx = len(np.unique(Cidx))
# if nuCidx == 1: # If all vertices share same label
# same_label = utils.all_items_equall(Cidx)
# if same_label:
if Cidx[0] == Cidx[1] == Cidx[2]:
# Add the current face to the list of faces of the
# respective label, and don't create new faces
facL[Cidx[0]] += [Cfac.tolist()]
else: # If 2 or 3 vertices are in different labels
# Create 3 new vertices at the midpoints of the 3 edges
vtxCfac = vtx[Cfac]
vtxnew = (vtxCfac + vtxCfac[[1, 2, 0]]) / 2
vtx = np.concatenate((vtx, vtxnew))
# Define 4 new faces, with care preserve normals (all CCW)
facnew = [[Cfac[0], nV, nV + 2], [nV, Cfac[1], nV + 1],
[nV + 2, nV + 1, Cfac[2]], [nV, nV + 1, nV + 2]]
# Update nV for the next loop
nV = vtx.shape[0]
# Add the new faces to their respective labels
facL[Cidx[0]] += [facnew[0]]
facL[Cidx[1]] += [facnew[1]]
facL[Cidx[2]] += [facnew[2]]
freq_Cidx = mode(Cidx)
facL[freq_Cidx] += [facnew[3]] # central face
# Having defined new faces and assigned all faces to labels, now
# select the vertices and redefine faces to use the new vertex indices
# Also, create the file for the indices
# fidx = fopen(sprintf('%s.index.csv', srfprefix), 'w');
# params = []
# for lab in range(nL):
# facL_lab = facL[lab]
# facL_lab_flat = utils.list_flatten(facL_lab)
# vidx = list(set(facL_lab_flat))
# vtxL_lab = vtx[vidx]
# params.append((facL_lab, vtxL_lab, vidx, nV, labels[lab].name, hemi, output_fol))
# utils.run_parallel(writing_ply_files_parallel, params, njobs=n_jobs)
#
ret = True
for lab in range(nL):
ret = ret and writing_ply_files(subject, surface_type, lab, facL[lab],
vtx, vtxL, labels, hemi, output_fol)
return ret