def save(self, output_dir):
"""
Save paradigm to output_dir/paradigm.csv,
BOLD to output_dir/bold.nii, mask to output_dir/mask.nii
#TODO: handle multi-session
Return: tuple of file names in this order: (paradigm, bold, mask)
"""
paradigm_file = op.join(output_dir,'paradigm.csv')
self.paradigm.save_csv(paradigm_file)
if self.data_type == 'volume':
# unflatten bold
bold_vol = expand_array_in_mask(self.bold, self.roiMask, 1)
bold_vol = np.rollaxis(bold_vol, 0, 4)
bold_file = op.join(output_dir, 'bold.nii')
write_volume(bold_vol, bold_file, self.meta_obj)
mask_file = op.join(output_dir, 'mask.nii')
write_volume(self.roiMask, mask_file, self.meta_obj)
elif self.data_type == 'surface': #TODO surface
bold_file = op.join(output_dir, 'bold.gii')
write_texture(self.bold_vol, bold_file, self.meta_obj)
pass
return paradigm_file, bold_file, mask_file
def project_fmri_from_kernels(input_mesh, kernels_file, fmri_data_file,
output_tex, bin_threshold=None, ):
pyhrf.verbose(2,'Project data onto mesh using kernels ...')
if 0:
print 'Projecting ...'
print 'func data:', fmri_data_file
print 'Mesh file:', input_mesh
print 'Save as:', output_tex
pyhrf.verbose(2,'Call AimsFunctionProjection -op 1 ...')
data_files = []
output_texs = []
p_ids = None
if bin_threshold is not None:
d,h = read_volume(fmri_data_file)
if np.allclose(d.astype(int), d):
tmp_dir = pyhrf.get_tmp_path()
p_ids = np.unique(d)
pyhrf.verbose(2, 'bin threshold: %f' %bin_threshold)
pyhrf.verbose(2, 'pids(n=%d): %d...%d' \
%(len(p_ids),min(p_ids),max(p_ids)))
for i,p_id in enumerate(p_ids):
if p_id != 0:
new_p = np.zeros_like(d)
new_p[np.where(d==p_id)] = i + 1 #0 is background
ifn = op.join(tmp_dir,'pmask_%d.nii'%p_id)
write_volume(new_p, ifn, h)
data_files.append(ifn)
ofn = op.join(tmp_dir,'ptex_%d.gii'%p_id)
output_texs.append(ofn)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
else:
data_files.append(fmri_data_file)
output_texs.append(output_tex)
pyhrf.verbose(3, 'input data files: %s' %str(data_files))
pyhrf.verbose(3, 'output data files: %s' %str(output_texs))
for data_file, o_tex in zip(data_files, output_texs):
projection = [
'AimsFunctionProjection',
'-op', '1',
'-d', kernels_file,
'-d1', data_file,
'-m', input_mesh,
'-o', o_tex
]
cmd = ' '.join(map(str,projection))
pyhrf.verbose(3, 'cmd: %s' %cmd)
os.system(cmd)
if bin_threshold is not None:
pyhrf.verbose(2, 'Binary threshold of texture at %f' %bin_threshold)
o_tex = output_texs[0]
data,data_gii = read_texture(o_tex)
data = (data>bin_threshold).astype(np.int32)
print 'data:', data.dtype
if p_ids is not None:
for pid, o_tex in zip(p_ids[1:], output_texs[1:]):
pdata,pdata_gii = read_texture(o_tex)
data += (pdata>bin_threshold).astype(np.int32) * pid
#assert (np.unique(data) == p_ids).all()
write_texture(data, output_tex, intent='NIFTI_INTENT_LABEL')
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
