def bad_cifti():
import nibabel.cifti2.cifti2 as ncif
import nibabel.cifti2.cifti2_axes as ncax
np.random.seed(seed=1)
data = np.random.uniform(size=(1, 50))
ser_ax = ncax.ScalarAxis(name=["Data"])
h = ncif.Cifti2Header.from_axes((ser_ax, ser_ax))
c = ncif.Cifti2Image(data, h)
return c
def cifti():
import nibabel.cifti2.cifti2 as ncif
import nibabel.cifti2.cifti2_axes as ncax
np.random.seed(seed=1)
verts = np.arange(0, 100, 2)
data = np.random.uniform(size=(1, 50))
ser_ax = ncax.ScalarAxis(name=["Data"])
bm_ax = ncax.BrainModelAxis(name="CORTEX_LEFT",
vertex=verts,
affine=np.eye(4),
nvertices={"CORTEX_LEFT": verts.shape[0]})
h = ncif.Cifti2Header.from_axes((ser_ax, bm_ax))
c = ncif.Cifti2Image(data, h)
return c
def check_rewrite(arr, axes, extension='.nii'):
"""
Checks wheter writing the Cifti2 array to disc and reading it back in gives the same object
Parameters
----------
arr : array
N-dimensional array of data
axes : Sequence[cifti2_axes.Axis]
sequence of length N with the meaning of the rows/columns along each dimension
extension : str
custom extension to use
"""
(fd, name) = tempfile.mkstemp(extension)
cifti2.Cifti2Image(arr, header=axes).to_filename(name)
img = nib.load(name)
arr2 = img.get_data()
assert (arr == arr2).all()
for idx in range(len(img.shape)):
assert (axes[idx] == img.header.get_axis(idx))
return img
def save2cifti(file_path,
data,
brain_models,
map_names=None,
volume=None,
label_tables=None):
"""
Save data as a cifti file
If you just want to simply save pure data without extra information,
you can just supply the first three parameters.
NOTE!!!!!!
The result is a Nifti2Image instead of Cifti2Image, when nibabel-2.2.1 is used.
Nibabel-2.3.0 can support for Cifti2Image indeed.
And the header will be regard as Nifti2Header when loading cifti file by nibabel earlier than 2.3.0.
Parameters:
----------
file_path: str
the output filename
data: numpy array
An array with shape (maps, values), each row is a map.
brain_models: sequence of Cifti2BrainModel
Each brain model is a specification of a part of the data.
We can always get them from another cifti file header.
map_names: sequence of str
The sequence's indices correspond to data's row indices and label_tables.
And its elements are maps' names.
volume: Cifti2Volume
The volume contains some information about subcortical voxels,
such as volume dimensions and transformation matrix.
If your data doesn't contain any subcortical voxel, set the parameter as None.
label_tables: sequence of Cifti2LableTable
Cifti2LableTable is a mapper to map label number to Cifti2Label.
Cifti2Lable is a specification of the label, including rgba, label name and label number.
If your data is a label data, it would be useful.
"""
if file_path.endswith('.dlabel.nii'):
assert label_tables is not None
idx_type0 = 'CIFTI_INDEX_TYPE_LABELS'
elif file_path.endswith('.dscalar.nii'):
idx_type0 = 'CIFTI_INDEX_TYPE_SCALARS'
else:
raise TypeError('Unsupported File Format')
if map_names is None:
map_names = [None] * data.shape[0]
else:
assert data.shape[0] == len(
map_names), "Map_names are mismatched with the data"
if label_tables is None:
label_tables = [None] * data.shape[0]
else:
assert data.shape[0] == len(
label_tables), "Label_tables are mismatched with the data"
# CIFTI_INDEX_TYPE_SCALARS always corresponds to Cifti2Image.header.get_index_map(0),
# and this index_map always contains some scalar information, such as named_maps.
# We can get label_table and map_name and metadata from named_map.
mat_idx_map0 = cifti2.Cifti2MatrixIndicesMap([0], idx_type0)
for mn, lbt in zip(map_names, label_tables):
named_map = cifti2.Cifti2NamedMap(mn, label_table=lbt)
mat_idx_map0.append(named_map)
# CIFTI_INDEX_TYPE_BRAIN_MODELS always corresponds to Cifti2Image.header.get_index_map(1),
# and this index_map always contains some brain_structure information, such as brain_models and volume.
mat_idx_map1 = cifti2.Cifti2MatrixIndicesMap(
[1], 'CIFTI_INDEX_TYPE_BRAIN_MODELS')
for bm in brain_models:
mat_idx_map1.append(bm)
if volume is not None:
mat_idx_map1.append(volume)
matrix = cifti2.Cifti2Matrix()
matrix.append(mat_idx_map0)
matrix.append(mat_idx_map1)
header = cifti2.Cifti2Header(matrix)
img = cifti2.Cifti2Image(data, header)
cifti2.save(img, file_path)