def main(args):
''' Reads both meshes, finds associations between nodes from both meshes
and writes :
- a distance map between each pair of associated nodes
- a difference map between texture values for each pair of nodes
If n is provided, stops the process after n random nodes'''
import thickness as t
import random
m1 = t.Mesh(args.m1)
m2 = t.Mesh(args.m2)
t1 = gio.read(args.t1).darrays[0].data
t2 = gio.read(args.t2).darrays[0].data
diff = [-1] * len(m1.vertex)
dist = [-1] * len(m1.vertex)
if args.n:
for i in xrange(int(args.n)):
if i%1000==0: print i, '/', int(args.n)
r = random.randint(0, len(m1.vertex))
diff[r], dist[r] = compare_node(r, m1, m2, t1, t2)
else:
for r in xrange(len(m1.vertex)):
if r%1000==0: print r, '/', len(m1.vertex)
diff[r], dist[r] = compare_node(r, m1, m2, t1, t2)
gda = gifti.GiftiDataArray.from_array(np.array(diff), intent=1001)
g = gifti.GiftiImage(darrays=[gda])
gio.write(g, args.difffp)
gda = gifti.GiftiDataArray.from_array(np.array(dist), intent=1001)
g = gifti.GiftiImage(darrays=[gda])
gio.write(g, args.distfp)
def test_read_deprecated():
with clear_and_catch_warnings() as w:
warnings.simplefilter('always', DeprecationWarning)
from nibabel.gifti.giftiio import read, write
img = read(DATA_FILE1)
assert_equal(len(w), 1)
with InTemporaryDirectory():
write(img, 'test.gii')
assert_equal(len(w), 2)
def test_read_deprecated():
with clear_and_catch_warnings() as w:
warnings.simplefilter('always', DeprecationWarning)
from nibabel.gifti.giftiio import read, write
img = read(DATA_FILE1)
assert_equal(len(w), 1)
with InTemporaryDirectory():
write(img, 'test.gii')
assert_equal(len(w), 2)
def convert(inputfp, outputfp):
print 'converting %s to %s'%(inputfp, outputfp)
plydata = PlyData.read(inputfp)
vertex = np.array([each for each in plydata.elements[0].data.tolist()], dtype=np.float32)
faces = plydata.elements[1].data.tolist()
faces = np.array([each[0] for each in faces], dtype=np.int32)
gv = gifti.GiftiDataArray.from_array(vertex, intent=1008)
gf = gifti.GiftiDataArray.from_array(faces, intent=1009)
g = gifti.GiftiImage()
g.add_gifti_data_array(gv)
g.add_gifti_data_array(gf)
gio.write(g, outputfp)
def test_gifti_dataset(fn, format_, include_nodes):
expected_ds = _get_test_dataset(include_nodes)
expected_ds_sa = expected_ds.copy(deep=True)
expected_ds_sa.sa['chunks'] = [4, 3, 2, 1, 3, 2]
expected_ds_sa.sa['targets'] = ['t%d' % i for i in xrange(6)]
# build GIFTI file from scratch
gifti_string = _build_gifti_string(format_, include_nodes)
with open(fn, 'w') as f:
f.write(gifti_string)
# reading GIFTI file
ds = gifti_dataset(fn)
assert_datasets_almost_equal(ds, expected_ds)
# test GiftiImage input
img = nb_giftiio.read(fn)
ds2 = gifti_dataset(img)
assert_datasets_almost_equal(ds2, expected_ds)
# test using Nibabel's output from write
nb_giftiio.write(img, fn)
ds3 = gifti_dataset(fn)
assert_datasets_almost_equal(ds3, expected_ds)
# test targets and chunks arguments
ds3_sa = gifti_dataset(fn, targets=expected_ds_sa.targets,
chunks=expected_ds_sa.chunks)
assert_datasets_almost_equal(ds3_sa, expected_ds_sa)
# test map2gifti
img2 = map2gifti(ds)
ds4 = gifti_dataset(img2)
assert_datasets_almost_equal(ds4, expected_ds)
map2gifti(ds, fn, encoding=format_)
ds5 = gifti_dataset(fn)
assert_datasets_almost_equal(ds5, expected_ds)
# test map2gifti with array input; nodes are not stored
map2gifti(ds.samples, fn)
ds6 = gifti_dataset(fn)
if include_nodes:
assert_raises(AssertionError, assert_datasets_almost_equal,
ds6, expected_ds)
else:
assert_datasets_almost_equal(ds6, expected_ds)
assert_raises(TypeError, gifti_dataset, ds3_sa)
assert_raises(TypeError, map2gifti, img, fn)
def test_gifti_dataset(fn, format_, include_nodes):
expected_ds = _get_test_dataset(include_nodes)
expected_ds_sa = expected_ds.copy(deep=True)
expected_ds_sa.sa['chunks'] = [4, 3, 2, 1, 3, 2]
expected_ds_sa.sa['targets'] = ['t%d' % i for i in xrange(6)]
# build GIFTI file from scratch
gifti_string = _build_gifti_string(format_, include_nodes)
with open(fn, 'w') as f:
f.write(gifti_string)
# reading GIFTI file
ds = gifti_dataset(fn)
assert_datasets_almost_equal(ds, expected_ds)
# test GiftiImage input
img = nb_giftiio.read(fn)
ds2 = gifti_dataset(img)
assert_datasets_almost_equal(ds2, expected_ds)
# test using Nibabel's output from write
nb_giftiio.write(img, fn)
ds3 = gifti_dataset(fn)
assert_datasets_almost_equal(ds3, expected_ds)
# test targets and chunks arguments
ds3_sa = gifti_dataset(fn, targets=expected_ds_sa.targets,
chunks=expected_ds_sa.chunks)
assert_datasets_almost_equal(ds3_sa, expected_ds_sa)
# test map2gifti
img2 = map2gifti(ds)
ds4 = gifti_dataset(img2)
assert_datasets_almost_equal(ds4, expected_ds)
# test float64 and int64, which must be converted to float32 and int32
fa = dict()
if include_nodes:
fa['node_indices'] = ds.fa.node_indices.astype(np.int64)
ds_float64 = Dataset(samples=ds.samples.astype(np.float64), fa=fa)
ds_float64_again = gifti_dataset(map2gifti(ds_float64))
assert_equal(ds_float64_again.samples.dtype, np.float32)
if include_nodes:
assert_equal(ds_float64_again.fa.node_indices.dtype, np.int32)
# test contents of GIFTI image
assert (isinstance(img2, nb_gifti.GiftiImage))
nsamples = ds.samples.shape[0]
if include_nodes:
node_arr = img2.darrays[0]
assert_equal(node_arr.intent,
intent_codes.code['NIFTI_INTENT_NODE_INDEX'])
assert_equal(node_arr.coordsys, None)
assert_equal(node_arr.data.dtype, np.int32)
assert_equal(node_arr.datatype, data_type_codes['int32'])
first_data_array_pos = 1
narrays = nsamples + 1
else:
first_data_array_pos = 0
narrays = nsamples
assert_equal(len(img.darrays), narrays)
for i in xrange(nsamples):
arr = img2.darrays[i + first_data_array_pos]
# check intent code
illegal_intents = ['NIFTI_INTENT_NODE_INDEX',
'NIFTI_INTENT_GENMATRIX',
'NIFTI_INTENT_POINTSET',
'NIFTI_INTENT_TRIANGLE']
assert (arr.intent not in [intent_codes.code[s]
for s in illegal_intents])
# although the GIFTI standard is not very clear about whether
# arrays with other intent than NODE_INDEX can have a
# GiftiCoordSystem, FreeSurfer's mris_convert
# does not seem to like its presence. Thus we make sure that
# it's not there.
assert_equal(arr.coordsys, None)
assert_equal(arr.data.dtype, np.float32)
assert_equal(arr.datatype, data_type_codes['float32'])
# another test for map2gifti, setting the encoding explicitly
map2gifti(ds, fn, encoding=format_)
ds5 = gifti_dataset(fn)
assert_datasets_almost_equal(ds5, expected_ds)
# test map2gifti with array input; nodes are not stored
map2gifti(ds.samples, fn)
ds6 = gifti_dataset(fn)
if include_nodes:
assert_raises(AssertionError, assert_datasets_almost_equal,
ds6, expected_ds)
else:
assert_datasets_almost_equal(ds6, expected_ds)
assert_raises(TypeError, gifti_dataset, ds3_sa)
assert_raises(TypeError, map2gifti, img, fn)
if pits_data_R.size == 0:
pits_data_R = gio.read(file_pits_R).darrays[0].data
else:
pits_data_R += gio.read(file_pits_R).darrays[0].data
if os.path.isfile(file_pits_L):
count_L += 1
if pits_data_L.size == 0:
pits_data_L = gio.read(file_pits_L).darrays[0].data
else:
pits_data_L += gio.read(file_pits_L).darrays[0].data
pits_data_R_temp = pits_data_R / count_R
pits_data_L_temp = pits_data_L / count_L
total_density = (pits_data_R_temp + pits_data_L_temp) / 2
"""g_R = gio.read(file_pits_R)
g_L = gio.read(file_pits_L)
g_R.darrays[0].data = pits_data_R_temp
g_L.darrays[0].data = pits_data_L_temp
gio.write(g_R, OUTPUT+filename_R_gii)
gio.write(g_L, OUTPUT+filename_L_gii)"""
g_L_R = gio.read(file_pits_L)
g_L_R.darrays[0].data = total_density
gio.write(g_L_R, OUTPUT + filename_L_R_gii)
"""
database = 'databaseBV'
cohort = 'hcp'
OUTPUT = '/media/yl247234/SAMSUNG/'+cohort+'/'+database+'/pits_density/'
path = '/media/yl247234/SAMSUNG/'+cohort+'/'+database+'/'+cohort+'/'
temp_file_s_ids='/home/yl247234/s_ids_lists/s_ids.json'
def map2gifti(ds, filename=None, encoding='GIFTI_ENCODING_B64GZ'):
"""Maps data(sets) into a GiftiImage, and optionally saves it to disc.
Parameters
----------
ds : AttrDataset or numpy.ndarray
The data to be mapepd
filename : basestring or None, optional
Filename to which the GiftiImage is stored
encoding : "ASCII" or "Base64Binary" or "GZipBase64Binary", optional
Encoding format of data
Returns
-------
img : GiftiImage
dataset contents represented in GiftiImage
"""
darrays = []
if isinstance(ds, np.ndarray):
samples = ds
elif isinstance(ds, AttrDataset):
samples = ds.samples
_warn_if_fmri_dataset(ds)
else:
raise TypeError('first argument must be AttrDataset or numpy.ndarray')
[nsamples, nfeatures] = samples.shape
def _get_attribute_value(ds, attr_name, keys_):
if isinstance(ds, np.ndarray):
# no attributes
return None
attr_collection = ds.__dict__.get(attr_name)
if isinstance(keys_, basestring):
keys_ = (keys_,)
for key in keys_:
if key in attr_collection:
return attr_collection[key].value
return None
def _build_array(data, intent, encoding=encoding):
return gifti.GiftiDataArray.from_array(data, intent,
encoding=encoding)
node_indices_labels = ('node_indices', 'center_ids', 'ids', 'roi_ids')
node_indices = _get_attribute_value(ds, 'fa', node_indices_labels)
if node_indices is not None:
darray = _build_array(node_indices, 'NIFTI_INTENT_NODE_INDEX')
darrays.append(darray)
intents = _get_attribute_value(ds, 'sa', 'intents')
for i, sample in enumerate(samples):
intent = 'NIFTI_INTENT_NONE' if intents is None else intents[i]
darray = _build_array(sample, intent)
darrays.append(darray)
image = gifti.GiftiImage(darrays=darrays)
if filename is not None:
giftiio.write(image, filename)
return image
def map2gifti(ds, filename=None, encoding='GIFTI_ENCODING_B64GZ'):
"""Maps data(sets) into a GiftiImage, and optionally saves it to disc.
Parameters
----------
ds : AttrDataset or numpy.ndarray
The data to be mapepd
filename : basestring or None, optional
Filename to which the GiftiImage is stored
encoding : "ASCII" or "Base64Binary" or "GZipBase64Binary", optional
Encoding format of data
Returns
-------
img : GiftiImage
dataset contents represented in GiftiImage
"""
darrays = []
if isinstance(ds, np.ndarray):
samples = ds
elif isinstance(ds, AttrDataset):
samples = ds.samples
_warn_if_fmri_dataset(ds)
else:
raise TypeError('first argument must be AttrDataset or numpy.ndarray')
[nsamples, nfeatures] = samples.shape
def _get_attribute_value(ds, attr_name, keys_):
if isinstance(ds, np.ndarray):
# no attributes
return None
attr_collection = ds.__dict__.get(attr_name)
if isinstance(keys_, basestring):
keys_ = (keys_, )
for key in keys_:
if key in attr_collection:
return attr_collection[key].value
return None
def _build_array(data, intent, encoding=encoding):
return gifti.GiftiDataArray.from_array(data, intent, encoding=encoding)
node_indices_labels = ('node_indices', 'center_ids', 'ids', 'roi_ids')
node_indices = _get_attribute_value(ds, 'fa', node_indices_labels)
if node_indices is not None:
darray = _build_array(node_indices, 'NIFTI_INTENT_NODE_INDEX')
darrays.append(darray)
intents = _get_attribute_value(ds, 'sa', 'intents')
for i, sample in enumerate(samples):
intent = 'NIFTI_INTENT_NONE' if intents is None else intents[i]
darray = _build_array(sample, intent)
darrays.append(darray)
image = gifti.GiftiImage(darrays=darrays)
if filename is not None:
giftiio.write(image, filename)
return image
def map2gifti(ds, filename=None, encoding='GIFTI_ENCODING_B64GZ',
surface=None):
"""Maps data(sets) into a GiftiImage, and optionally saves it to disc.
Parameters
----------
ds : AttrDataset or numpy.ndarray
The data to be mapepd
filename : basestring or None, optional
Filename to which the GiftiImage is stored
encoding : "ASCII" or "Base64Binary" or "GZipBase64Binary", optional
Encoding format of data
surface : mvpa2.surf.nibabel.surf.Surface or str, optional
Optional anatomical Surface object, or filename of anatomical surface
file, to be stored together with the data. This should allow
FreeSurfer's mris_convert to read files written by this function
Returns
-------
img : GiftiImage
dataset contents represented in GiftiImage
"""
darrays = []
if isinstance(ds, np.ndarray):
samples = ds
elif isinstance(ds, AttrDataset):
samples = ds.samples
_warn_if_fmri_dataset(ds)
else:
raise TypeError('first argument must be AttrDataset or numpy.ndarray')
[nsamples, nfeatures] = samples.shape
def _get_attribute_value(ds, attr_name, keys_):
if isinstance(ds, np.ndarray):
# no attributes
return None
attr_collection = ds.__dict__.get(attr_name)
if isinstance(keys_, basestring):
keys_ = (keys_,)
for key in keys_:
if key in attr_collection:
return attr_collection[key].value
return None
def _build_array(data, intent, encoding=encoding):
is_integer = intent == 'NIFTI_INTENT_NODE_INDEX'
dtype = np.int32 if is_integer else np.float32
arr = gifti.GiftiDataArray.from_array(data.astype(dtype), intent,
encoding=encoding)
# Setting the coordsys argument the constructor would set the matrix
# to the 4x4 identity matrix, which is not desired. Instead the
# coordsys is explicitly set to None afterwards
arr.coordsys = None
return arr
node_indices_labels = ('node_indices', 'center_ids', 'ids', 'roi_ids')
node_indices = _get_attribute_value(ds, 'fa', node_indices_labels)
if node_indices is not None:
darray = _build_array(node_indices, 'NIFTI_INTENT_NODE_INDEX')
darrays.append(darray)
intents = _get_attribute_value(ds, 'sa', 'intents')
for i, sample in enumerate(samples):
intent = 'NIFTI_INTENT_NONE' if intents is None else intents[i]
darray = _build_array(sample, intent)
darrays.append(darray)
# if there is a surface, add it
if surface is not None:
surface_object = surf_from_any(surface, )
anat_image = anat_surf_to_gifti_image(surface_object, add_indices=False)
for darray in anat_image.darrays:
darrays.append(darray)
image = gifti.GiftiImage(darrays=darrays)
if filename is not None:
giftiio.write(image, filename)
return image
if os.path.isfile(pits_R):
count_R += 1
if pits_data_R.size == 0:
pits_data_R = gio.read(pits_R).darrays[0].data
else:
pits_data_R += gio.read(pits_R).darrays[0].data
else:
print k
print s_id
if os.path.isfile(pits_L):
count_L += 1
if pits_data_L.size == 0:
pits_data_L = gio.read(pits_L).darrays[0].data
else:
pits_data_L += gio.read(pits_L).darrays[0].data
pits_data_R_temp = pits_data_R / count_R
pits_data_L_temp = pits_data_L / count_L
total_density = (pits_data_R_temp + pits_data_L_temp) / 2
g_L_R = gio.read(pits_L)
g_L_R.darrays[0].data = total_density
gio.write(g_L_R, fname_LR)
g_R = gio.read(pits_L)
g_L = gio.read(pits_L)
g_R.darrays[0].data = pits_data_R_temp
g_L.darrays[0].data = pits_data_L_temp
gio.write(g_R, fname_R)
gio.write(g_L, fname_L)
for subject in os.listdir(path_c):
path_s = path_c+subject+'/'
if os.path.isdir(path_s):
file_pits_R = path_s + 't1mri/BL/default_analysis/segmentation/mesh/surface_analysis/'+subject+'_Rwhite_pits_smoothed_on_atlas.gii'
file_pits_L = path_s + 't1mri/BL/default_analysis/segmentation/mesh/surface_analysis/'+subject+'_Lwhite_pits_smoothed_on_atlas.gii'
if os.path.isfile(file_pits_R):
count_R +=1
if pits_data_R.size == 0:
pits_data_R = gio.read(file_pits_R).darrays[0].data
else:
pits_data_R += gio.read(file_pits_R).darrays[0].data
if os.path.isfile(file_pits_L):
count_L +=1
if pits_data_L.size == 0:
pits_data_L = gio.read(file_pits_L).darrays[0].data
else:
pits_data_L += gio.read(file_pits_L).darrays[0].data
pits_data_R = pits_data_R/count_R
pits_data_L = pits_data_L/count_L
g_R = gio.read(file_pits_R)
g_L = gio.read(file_pits_L)
g_R.darrays[0].data = pits_data_R
g_L.darrays[0].data = pits_data_L
np.savetxt(OUTPUT+filename_R, pits_data_R)
np.savetxt(OUTPUT+filename_L, pits_data_L)
gio.write(g_R, OUTPUT+filename_R_gii)
gio.write(g_L, OUTPUT+filename_L_gii)
def test_gifti_dataset(fn, format_, include_nodes):
expected_ds = _get_test_dataset(include_nodes)
expected_ds_sa = expected_ds.copy(deep=True)
expected_ds_sa.sa['chunks'] = [4, 3, 2, 1, 3, 2]
expected_ds_sa.sa['targets'] = ['t%d' % i for i in xrange(6)]
# build GIFTI file from scratch
gifti_string = _build_gifti_string(format_, include_nodes)
with open(fn, 'w') as f:
f.write(gifti_string)
# reading GIFTI file
ds = gifti_dataset(fn)
assert_datasets_almost_equal(ds, expected_ds)
# test GiftiImage input
img = nb_giftiio.read(fn)
ds2 = gifti_dataset(img)
assert_datasets_almost_equal(ds2, expected_ds)
# test using Nibabel's output from write
nb_giftiio.write(img, fn)
ds3 = gifti_dataset(fn)
assert_datasets_almost_equal(ds3, expected_ds)
# test targets and chunks arguments
ds3_sa = gifti_dataset(fn, targets=expected_ds_sa.targets,
chunks=expected_ds_sa.chunks)
assert_datasets_almost_equal(ds3_sa, expected_ds_sa)
# test map2gifti
img2 = map2gifti(ds)
ds4 = gifti_dataset(img2)
assert_datasets_almost_equal(ds4, expected_ds)
# test float64 and int64, which must be converted to float32 and int32
fa = dict()
if include_nodes:
fa['node_indices'] = ds.fa.node_indices.astype(np.int64)
ds_float64 = Dataset(samples=ds.samples.astype(np.float64), fa=fa)
ds_float64_again = gifti_dataset(map2gifti(ds_float64))
assert_equal(ds_float64_again.samples.dtype, np.float32)
if include_nodes:
assert_equal(ds_float64_again.fa.node_indices.dtype, np.int32)
# test contents of GIFTI image
assert (isinstance(img2, nb_gifti.GiftiImage))
nsamples = ds.samples.shape[0]
if include_nodes:
node_arr = img2.darrays[0]
assert_equal(node_arr.intent,
intent_codes.code['NIFTI_INTENT_NODE_INDEX'])
assert_equal(node_arr.coordsys, None)
assert_equal(node_arr.data.dtype, np.int32)
assert_equal(node_arr.datatype, data_type_codes['int32'])
first_data_array_pos = 1
narrays = nsamples + 1
else:
first_data_array_pos = 0
narrays = nsamples
assert_equal(len(img.darrays), narrays)
for i in xrange(nsamples):
arr = img2.darrays[i + first_data_array_pos]
# check intent code
illegal_intents = ['NIFTI_INTENT_NODE_INDEX',
'NIFTI_INTENT_GENMATRIX',
'NIFTI_INTENT_POINTSET',
'NIFTI_INTENT_TRIANGLE']
assert (arr.intent not in [intent_codes.code[s]
for s in illegal_intents])
# although the GIFTI standard is not very clear about whether
# arrays with other intent than NODE_INDEX can have a
# GiftiCoordSystem, FreeSurfer's mris_convert
# does not seem to like its presence. Thus we make sure that
# it's not there.
assert_equal(arr.coordsys, None)
assert_equal(arr.data.dtype, np.float32)
assert_equal(arr.datatype, data_type_codes['float32'])
# another test for map2gifti, setting the encoding explicitly
map2gifti(ds, fn, encoding=format_)
ds5 = gifti_dataset(fn)
assert_datasets_almost_equal(ds5, expected_ds)
# test map2gifti with array input; nodes are not stored
map2gifti(ds.samples, fn)
ds6 = gifti_dataset(fn)
if include_nodes:
assert_raises(AssertionError, assert_datasets_almost_equal,
ds6, expected_ds)
else:
assert_datasets_almost_equal(ds6, expected_ds)
assert_raises(TypeError, gifti_dataset, ds3_sa)
assert_raises(TypeError, map2gifti, img, fn)
def map2gifti(ds,
filename=None,
encoding='GIFTI_ENCODING_B64GZ',
surface=None):
"""Maps data(sets) into a GiftiImage, and optionally saves it to disc.
Parameters
----------
ds : AttrDataset or numpy.ndarray
The data to be mapepd
filename : basestring or None, optional
Filename to which the GiftiImage is stored
encoding : "ASCII" or "Base64Binary" or "GZipBase64Binary", optional
Encoding format of data
surface : mvpa2.surf.nibabel.surf.Surface or str, optional
Optional anatomical Surface object, or filename of anatomical surface
file, to be stored together with the data. This should allow
FreeSurfer's mris_convert to read files written by this function
Returns
-------
img : GiftiImage
dataset contents represented in GiftiImage
"""
darrays = []
if isinstance(ds, np.ndarray):
samples = ds
elif isinstance(ds, AttrDataset):
samples = ds.samples
_warn_if_fmri_dataset(ds)
else:
raise TypeError('first argument must be AttrDataset or numpy.ndarray')
[nsamples, nfeatures] = samples.shape
def _get_attribute_value(ds, attr_name, keys_):
if isinstance(ds, np.ndarray):
# no attributes
return None
attr_collection = ds.__dict__.get(attr_name)
if isinstance(keys_, basestring):
keys_ = (keys_, )
for key in keys_:
if key in attr_collection:
return attr_collection[key].value
return None
def _build_array(data, intent, encoding=encoding):
is_integer = intent == 'NIFTI_INTENT_NODE_INDEX'
dtype = np.int32 if is_integer else np.float32
arr = gifti.GiftiDataArray.from_array(data.astype(dtype),
intent,
encoding=encoding)
# Setting the coordsys argument the constructor would set the matrix
# to the 4x4 identity matrix, which is not desired. Instead the
# coordsys is explicitly set to None afterwards
arr.coordsys = None
return arr
node_indices_labels = ('node_indices', 'center_ids', 'ids', 'roi_ids')
node_indices = _get_attribute_value(ds, 'fa', node_indices_labels)
if node_indices is not None:
darray = _build_array(node_indices, 'NIFTI_INTENT_NODE_INDEX')
darrays.append(darray)
intents = _get_attribute_value(ds, 'sa', 'intents')
for i, sample in enumerate(samples):
intent = 'NIFTI_INTENT_NONE' if intents is None else intents[i]
darray = _build_array(sample, intent)
darrays.append(darray)
# if there is a surface, add it
if surface is not None:
surface_object = surf_from_any(surface, )
anat_image = anat_surf_to_gifti_image(surface_object,
add_indices=False)
for darray in anat_image.darrays:
darrays.append(darray)
image = gifti.GiftiImage(darrays=darrays)
if filename is not None:
giftiio.write(image, filename)
return image
