def __init__(self):
super(NiftiTools, self).__init__()
self.afni = AfniWrapper()
def __init__(self):
super(NiftiTools, self).__init__()
self.afni = AfniWrapper()
class NiftiTools(object):
def __init__(self):
super(NiftiTools, self).__init__()
self.afni = AfniWrapper()
def adwarp_to_template_talairach(self, input_path, output_path_prefix, talairach_path, dxyz=1.):
self.afni.adwarp(talairach_path, input_path, output_path_prefix, dxyz=dxyz)
def convert_to_nifti(self, dataset_in, dataset_out):
glob_remove(dataset_out)
subprocess.call(['3dAFNItoNIFTI', '-prefix', dataset_out, dataset_in])
def convert_to_afni(self, nifti_in, dataset_out):
try:
glob_remove(dataset_out+'+orig')
except:
pass
try:
glob_remove(dataset_out+'+tlrc')
except:
pass
subprocess.call(['3dcopy', nifti_in, dataset_out])
def refit(self, functional, anatomical):
subprocess.call(['3drefit', '-apar', anatomical, functional])
def adwarp_to_subject_talairach(self, dataset_in, dataset_out, anatomical, dxyz):
glob_remove(dataset_out)
subprocess.call(['adwarp', '-apar', anatomical, '-dpar', dataset_in,
'-dxyz', str(dxyz), '-prefix', dataset_out])
def create_mask(self, mask_dset_path, output_prefix_path, clfrac=.3):
self.afni.automask(mask_dset_path, output_prefix_path, clfrac=clfrac)
def create_talairach_niftis(self, subject_dirs, functional_name, anatomical_name,
dxyz, talairach_path, output_name, within_subject_warp=True,
to_template_warp=True):
if not output_name.endswith('.nii'):
output_name = output_name+'.nii'
# iterate subject directories:
for s in subject_dirs:
functional = os.path.join(s, functional_name)
anatomical = os.path.join(s, anatomical_name)
# refit the functional to the anatomical:
self.afni.refit_apar(anatomical+'+tlrc', functional+'+orig')
#self.refit(functional, anatomical)
# adwarp the functional to the anatomical
if within_subject_warp:
adwarp_temp = os.path.join(s, 'temp_adwarp_func')
self.afni.adwarp(anatomical+'+tlrc', functional+'+orig', adwarp_temp, dxyz=dxyz)
else:
adwarp_temp = functional
# warp the warped functional to the talairach template:
if to_template_warp:
template_temp = os.path.join(s, 'temp_template_func')
self.afni.adwarp(talairach_path, adwarp_temp+'+tlrc', template_temp, dxyz=dxyz,
force=True)
else:
template_temp = adwarp_temp
# make the nifti files
nifti_path = os.path.join(s, output_name)
try:
glob_remove(nifti_path)
except:
pass
self.convert_to_nifti(template_temp+'+tlrc', nifti_path)
if within_subject_warp:
glob_remove(adwarp_temp)
if to_template_warp:
glob_remove(template_temp)
return [os.path.join(s, output_name) for s in subject_dirs]
def save_nifti(self, data, affine, filepath):
if not filepath.endswith('.nii'):
filepath = filepath+'.nii'
glob_remove(filepath)
nii = nib.Nifti1Image(data, affine)
nii.to_filename(filepath)
def output_nifti_thrumask(self, data, mask, mask_3dshape, trlen, affine, output_filepath):
shaped_matrix = np.zeros((mask_3dshape[0], mask_3dshape[1], mask_3dshape[2],
trlen))
shaped_matrix[np.where(mask)] = data
self.save_nifti(shaped_matrix, affine, output_filepath)
def load_nifti(self, nifti_path):
image = nib.load(nifti_path)
shape = image.get_shape()
idata = image.get_data()
affine = image.get_affine()
return idata, affine, shape
class NiftiTools(object):
def __init__(self):
super(NiftiTools, self).__init__()
self.afni = AfniWrapper()
def adwarp_to_template_talairach(self,
input_path,
output_path_prefix,
talairach_path,
dxyz=1.):
self.afni.adwarp(talairach_path,
input_path,
output_path_prefix,
dxyz=dxyz)
def convert_to_nifti(self, dataset_in, dataset_out):
glob_remove(dataset_out)
subprocess.call(['3dAFNItoNIFTI', '-prefix', dataset_out, dataset_in])
def convert_to_afni(self, nifti_in, dataset_out):
try:
glob_remove(dataset_out + '+orig')
except:
pass
try:
glob_remove(dataset_out + '+tlrc')
except:
pass
subprocess.call(['3dcopy', nifti_in, dataset_out])
def refit(self, functional, anatomical):
subprocess.call(['3drefit', '-apar', anatomical, functional])
def adwarp_to_subject_talairach(self, dataset_in, dataset_out, anatomical,
dxyz):
glob_remove(dataset_out)
subprocess.call([
'adwarp', '-apar', anatomical, '-dpar', dataset_in, '-dxyz',
str(dxyz), '-prefix', dataset_out
])
def create_mask(self, mask_dset_path, output_prefix_path, clfrac=.3):
self.afni.automask(mask_dset_path, output_prefix_path, clfrac=clfrac)
def create_talairach_niftis(self,
subject_dirs,
functional_name,
anatomical_name,
dxyz,
talairach_path,
output_name,
within_subject_warp=True,
to_template_warp=True):
if not output_name.endswith('.nii'):
output_name = output_name + '.nii'
# iterate subject directories:
for s in subject_dirs:
functional = os.path.join(s, functional_name)
anatomical = os.path.join(s, anatomical_name)
# refit the functional to the anatomical:
self.afni.refit_apar(anatomical + '+tlrc', functional + '+orig')
#self.refit(functional, anatomical)
# adwarp the functional to the anatomical
if within_subject_warp:
adwarp_temp = os.path.join(s, 'temp_adwarp_func')
self.afni.adwarp(anatomical + '+tlrc',
functional + '+orig',
adwarp_temp,
dxyz=dxyz)
else:
adwarp_temp = functional
# warp the warped functional to the talairach template:
if to_template_warp:
template_temp = os.path.join(s, 'temp_template_func')
self.afni.adwarp(talairach_path,
adwarp_temp + '+tlrc',
template_temp,
dxyz=dxyz,
force=True)
else:
template_temp = adwarp_temp
# make the nifti files
nifti_path = os.path.join(s, output_name)
try:
glob_remove(nifti_path)
except:
pass
self.convert_to_nifti(template_temp + '+tlrc', nifti_path)
if within_subject_warp:
glob_remove(adwarp_temp)
if to_template_warp:
glob_remove(template_temp)
return [os.path.join(s, output_name) for s in subject_dirs]
def save_nifti(self, data, affine, filepath):
if not filepath.endswith('.nii'):
filepath = filepath + '.nii'
glob_remove(filepath)
nii = nib.Nifti1Image(data, affine)
nii.to_filename(filepath)
def output_nifti_thrumask(self, data, mask, mask_3dshape, trlen, affine,
output_filepath):
shaped_matrix = np.zeros(
(mask_3dshape[0], mask_3dshape[1], mask_3dshape[2], trlen))
shaped_matrix[np.where(mask)] = data
self.save_nifti(shaped_matrix, affine, output_filepath)
def load_nifti(self, nifti_path):
image = nib.load(nifti_path)
shape = image.get_shape()
idata = image.get_data()
affine = image.get_affine()
return idata, affine, shape
