def make_parcellation_surf_from_files(beta_files, mesh_file, parcellation_file,
nbparcel, method, mu=10., verbose=0):
if method not in ['ward', 'gkm', 'ward_and_gkm', 'kmeans']:
raise ValueError('unknown method')
# step 1: load the data ----------------------------
# 1.1 the domain
logger.info('domain from mesh: %s', mesh_file)
domain = domain_from_mesh(mesh_file)
coord = domain.coord
# 1.3 read the functional data
beta = np.array([read_texture(b)[0] for b in beta_files]).T
logger.info('beta: %s', str(beta.shape))
logger.info('mu * coord / np.std(coord): %s',
(mu * coord / np.std(coord)).shape)
feature = np.hstack((beta, mu * coord / np.std(coord)))
if method is not 'kmeans':
g = field_from_coo_matrix_and_data(domain.topology, feature)
if method == 'kmeans':
_, u, _ = kmeans(feature, nbparcel)
if method == 'ward':
u, _ = g.ward(nbparcel)
if method == 'gkm':
seeds = np.argsort(np.random.rand(g.V))[:nbparcel]
_, u, _ = g.geodesic_kmeans(seeds)
if method == 'ward_and_gkm':
w, _ = g.ward(nbparcel)
_, u, _ = g.geodesic_kmeans(label=w)
lpa = SubDomains(domain, u, 'parcellation')
if verbose:
var_beta = np.array(
[np.var(beta[lpa.label == k], 0).sum() for k in range(lpa.k)])
var_coord = np.array(
[np.var(coord[lpa.label == k], 0).sum() for k in range(lpa.k)])
size = lpa.get_size()
vf = np.dot(var_beta, size) / size.sum()
va = np.dot(var_coord, size) / size.sum()
print nbparcel, "functional variance", vf, "anatomical variance", va
# step3: write the resulting label image
if parcellation_file is not None:
label_image = parcellation_file
else:
label_image = None
if label_image is not None:
write_texture(u.astype(np.int32), label_image)
if verbose:
print "Wrote the parcellation images as %s" % label_image
return u, label_image
def make_parcellation_surf_from_files(beta_files, mesh_file, parcellation_file,
nbparcel, method, mu=10., verbose=0):
if method not in ['ward', 'gkm', 'ward_and_gkm', 'kmeans']:
raise ValueError('unknown method')
# step 1: load the data ----------------------------
# 1.1 the domain
pyhrf.verbose(3, 'domain from mesh: %s' %mesh_file)
domain = domain_from_mesh(mesh_file)
coord = domain.coord
# 1.3 read the functional data
beta = np.array([read_texture(b)[0] for b in beta_files]).T
pyhrf.verbose(3, 'beta: %s' %str(beta.shape))
pyhrf.verbose(3, 'mu * coord / np.std(coord): %s' \
%(mu * coord / np.std(coord)).shape)
feature = np.hstack((beta, mu * coord / np.std(coord)))
if method is not 'kmeans':
# print 'domain.topology:', domain.topology.__class__
# print domain.topology
#print dir(domain.topology)
# print 'feature:', feature.shape
# print feature
g = field_from_coo_matrix_and_data(domain.topology, feature)
# print 'g:', g.__class__
# print g
if method == 'kmeans':
_, u, _ = kmeans(feature, nbparcel)
if method == 'ward':
u, _ = g.ward(nbparcel)
if method == 'gkm':
seeds = np.argsort(np.random.rand(g.V))[:nbparcel]
_, u, _ = g.geodesic_kmeans(seeds)
if method == 'ward_and_gkm':
w, _ = g.ward(nbparcel)
_, u, _ = g.geodesic_kmeans(label=w)
# print 'u:'
# print u
lpa = SubDomains(domain, u, 'parcellation')
if verbose:
var_beta = np.array(
[np.var(beta[lpa.label == k], 0).sum() for k in range(lpa.k)])
var_coord = np.array(
[np.var(coord[lpa.label == k], 0).sum() for k in range(lpa.k)])
size = lpa.get_size()
vf = np.dot(var_beta, size) / size.sum()
va = np.dot(var_coord, size) / size.sum()
print nbparcel, "functional variance", vf, "anatomical variance", va
# step3: write the resulting label image
if parcellation_file is not None:
label_image = parcellation_file
# elif write_dir is not None:
# label_image = os.path.join(write_dir, "parcel_%s.nii" % method)
else:
label_image = None
if label_image is not None:
#lpa.to_image(label_image, descrip='Intra-subject parcellation image')
write_texture(u.astype(np.int32), label_image)
if verbose:
print "Wrote the parcellation images as %s" % label_image
return u, label_image
def make_parcellation_surf_from_files(beta_files,
mesh_file,
parcellation_file,
nbparcel,
method,
mu=10.,
verbose=0):
if method not in ['ward', 'gkm', 'ward_and_gkm', 'kmeans']:
raise ValueError('unknown method')
# step 1: load the data ----------------------------
# 1.1 the domain
logger.info('domain from mesh: %s', mesh_file)
domain = domain_from_mesh(mesh_file)
coord = domain.coord
# 1.3 read the functional data
beta = np.array([read_texture(b)[0] for b in beta_files]).T
logger.info('beta: %s', str(beta.shape))
logger.info('mu * coord / np.std(coord): %s',
(mu * coord / np.std(coord)).shape)
feature = np.hstack((beta, mu * coord / np.std(coord)))
if method is not 'kmeans':
g = field_from_coo_matrix_and_data(domain.topology, feature)
if method == 'kmeans':
_, u, _ = kmeans(feature, nbparcel)
if method == 'ward':
u, _ = g.ward(nbparcel)
if method == 'gkm':
seeds = np.argsort(np.random.rand(g.V))[:nbparcel]
_, u, _ = g.geodesic_kmeans(seeds)
if method == 'ward_and_gkm':
w, _ = g.ward(nbparcel)
_, u, _ = g.geodesic_kmeans(label=w)
lpa = SubDomains(domain, u, 'parcellation')
if verbose:
var_beta = np.array(
[np.var(beta[lpa.label == k], 0).sum() for k in range(lpa.k)])
var_coord = np.array(
[np.var(coord[lpa.label == k], 0).sum() for k in range(lpa.k)])
size = lpa.get_size()
vf = np.dot(var_beta, size) / size.sum()
va = np.dot(var_coord, size) / size.sum()
print nbparcel, "functional variance", vf, "anatomical variance", va
# step3: write the resulting label image
if parcellation_file is not None:
label_image = parcellation_file
else:
label_image = None
if label_image is not None:
write_texture(u.astype(np.int32), label_image)
if verbose:
print "Wrote the parcellation images as %s" % label_image
return u, label_image
