def yeo_to_91k(dlabel, medial_wall, reference, out):
"""Convert Yeo-style dlabels (Yeo and Schaefer parcellations) to 91k
grayordinate space
The Yeo lab generates dlabel's inclusive of medial wall vertices and only
for the cortical surfaces. This is different from how typical dlabels are
formatted, which exclude medial wall vertices and include voxels from all
subcortical and cerebellar structures (i.e. the full 91k grayordinate
space). This function corrects Yeo dlabels to proper 91k grayordinates.
Parameters
----------
dlabel : str
A Yeo-style .dlabel.nii atlas
medial_wall : str
HCP medial wall mask (.dlabel.nii)
reference : str
A reference .dlabel.nii file with 91k grayordinates and all brain
models included
out : str
Output 91k grayordinate .dlabel.nii file
"""
dlabel = nib.load(dlabel)
medial_wall = nib.load(medial_wall)
ref = nib.load(reference)
# remove medial wall vertices
array = dlabel.get_fdata()
corrected_array = array[np.logical_not(medial_wall.get_fdata())]
# expand to 91k
grayordinates = np.zeros(ref.shape)
grayordinates[0, :corrected_array.shape[0]] = corrected_array
# make header
labels = dlabel.header.get_axis(index=0).label[0]
label_table = ci.Cifti2LabelTable()
for key, (tag, rgba) in labels.items():
label_table[key] = ci.Cifti2Label(key, tag, *rgba)
maps = [ci.Cifti2NamedMap('labels', ci.Cifti2MetaData({}), label_table)]
label_map = ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension=(0, ),
indices_map_to_data_type='CIFTI_INDEX_TYPE_LABELS',
maps=maps)
model_map = ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension=(1, ),
indices_map_to_data_type='CIFTI_INDEX_TYPE_BRAIN_MODELS',
maps=list(ref.header.get_index_map(1).brain_models))
model_map.volume = ref.header.get_index_map(1).volume
matrix = ci.Cifti2Matrix()
matrix.append(label_map)
matrix.append(model_map)
hdr = ci.Cifti2Header(matrix)
out_dtseries = ci.Cifti2Image(grayordinates, hdr)
out_dtseries.to_filename(out)
return out
def make_imaker(self, arr, header=None, ni_header=None):
for idx, sz in enumerate(arr.shape):
maps = [ci.Cifti2NamedMap(str(value)) for value in range(sz)]
mim = ci.Cifti2MatrixIndicesMap((idx, ),
'CIFTI_INDEX_TYPE_SCALARS',
maps=maps)
header.matrix.append(mim)
return lambda: self.image_maker(arr.copy(), header, ni_header)
def create_scalar_map(applies_to_matrix_dimension):
maps = [
ci.Cifti2NamedMap(name, ci.Cifti2MetaData(meta))
for name, meta in scalars
]
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_SCALARS',
maps=maps)
def create_label_map(applies_to_matrix_dimension):
maps = []
for name, meta, label in labels:
label_table = ci.Cifti2LabelTable()
for key, (tag, rgba) in label.items():
label_table[key] = ci.Cifti2Label(key, tag, *rgba)
maps.append(
ci.Cifti2NamedMap(name, ci.Cifti2MetaData(meta), label_table))
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_LABELS',
maps=maps)
def to_mapping(self, dim):
"""
Converts the hcp_labels to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_SCALARS')
for elem in self.arr:
meta = None if len(elem['meta']) == 0 else elem['meta']
named_map = cifti2.Cifti2NamedMap(elem['name'], cifti2.Cifti2MetaData(meta))
mim.append(named_map)
return mim
def to_mapping(self, dim):
"""
Converts the hcp_labels to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_LABELS')
for elem in self.arr:
label_table = cifti2.Cifti2LabelTable()
for key, value in elem['label'].items():
label_table[key] = (value[0],) + tuple(value[1])
meta = None if len(elem['meta']) == 0 else elem['meta']
named_map = cifti2.Cifti2NamedMap(elem['name'], cifti2.Cifti2MetaData(meta),
label_table)
mim.append(named_map)
return mim
def create_scalar_map(applies_to_matrix_dimension, info):
"""Creates a scalar map form a list of NamedMapInfo"""
maps = [ci.Cifti2NamedMap(i.name, ci.Cifti2MetaData(i.meta)) for i in info]
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
Map.SCALARS,
maps=maps)
