def test_PickAtlas_outputs():
output_map = dict(mask_file=dict(), )
outputs = PickAtlas.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_PickAtlas_outputs():
output_map = dict(mask_file=dict())
outputs = PickAtlas.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_PickAtlas_inputs():
input_map = dict(
atlas=dict(mandatory=True),
dilation_size=dict(usedefault=True),
hemi=dict(usedefault=True),
ignore_exception=dict(nohash=True, usedefault=True),
labels=dict(mandatory=True),
output_file=dict(),
)
inputs = PickAtlas.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_PickAtlas_inputs():
input_map = dict(
atlas=dict(mandatory=True, ),
dilation_size=dict(usedefault=True, ),
hemi=dict(usedefault=True, ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
labels=dict(mandatory=True, ),
output_file=dict(),
)
inputs = PickAtlas.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def get_atlas_rois(atlas, roi_idx, hemisphere, res=None, path=None):
"""
Extract ROIs from a given atlas.
Parameters
----------
atlas : str
Atlas dataset to be downloaded through nilearn's dataset_fetch_atlas functionality.
roi_idx: list
List of int of the ROI(s) you want to extract from the atlas. If not sure, use get_atlas_info.
hemisphere: list
List of str, that is hemispheres of the ROI(s) you want to extract. Can be ['left'], ['right'] or ['left', 'right'].
res: str
Specific version of atlas to be downloaded. Only necessary for Harvard-Oxford and Talairach.
Please check nilearns respective documentation at
https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_atlas_harvard_oxford.html or
https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_atlas_talairach.html
path: str
Path to where the extracted ROI(s) will be saved to. If None, ROI(s) will be saved in the current
working directory.
Returns
-------
list_rois: list
A list of the extracted ROIs.
Examples
--------
>>> get_atlas_rois('aal', [1, 2, 3], ['left', 'right'], path='/home/urial/Desktop')
list_rois
"""
if atlas == 'aal':
atl_ds = datasets.fetch_atlas_aal()
elif atlas == 'harvard_oxford':
if res is None:
print(
'Please provide the specific version of the Harvard-Oxford atlas you would like to use.'
)
else:
atl_ds = datasets.fetch_atlas_harvard_oxford(res)
elif atlas == 'destriuex':
atl_ds = datasets.fetch_atlas_destrieux_2009()
elif atlas == 'msdl':
atl_ds = datasets.fetch_atlas_msdl()
elif atlas == 'talairach':
if res is None:
print(
'Please provide the level of the Talairach atlas you would like to use.'
)
else:
atl_ds = datasets.fetch_atlas_talairach(level_name=res)
elif atlas == 'pauli_2017':
atl_ds = datasets.fetch_atlas_pauli_2017()
if roi_idx is None:
print('Please provide the indices of the ROIs you want to extract.')
elif hemisphere is None:
print(
'Please provide the hemisphere(s) from which you want to extract ROIs.'
)
for label in roi_idx:
for hemi in hemisphere:
roi_ex = Node(PickAtlas(), name='roi_ex')
roi_ex.inputs.atlas = atl_ds.maps
roi_ex.inputs.labels = label
roi_ex.inputs.hemi = hemi
if path is None:
roi_ex.inputs.output_file = '%s_%s_%s.nii.gz' % (
atlas, str(label), hemi)
roi_ex.run()
list_rois = glob('%s_*.nii.gz' % atlas)
elif path:
roi_ex.inputs.output_file = opj(
path, '%s_%s_%s.nii.gz' % (atlas, str(label), hemi))
roi_ex.run()
list_rois = glob(opj(path, '%s_*.nii.gz' % atlas))
print('The following ROIs were extracted: ')
print('\n'.join(map(str, list_rois)))
return list_rois
