def test_ni_parcellate(clust_type):
"""
Test for ni_parcellate
"""
import tempfile
k = 20
base_dir = str(Path(__file__).parent / "examples")
out_dir = f"{base_dir}/outputs/sub-25659/ses-1/func"
tmpdir = tempfile.TemporaryDirectory()
if clust_type != 'ncut':
local_corr = 'allcorr'
else:
local_corr = 'tcorr'
clust_mask = f"{base_dir}/miscellaneous/rMFG_node6mm.nii.gz"
mask = f"{base_dir}/BIDS/sub-25659/ses-1/anat/sub-25659_desc-brain_mask.nii.gz"
func_file = f"{base_dir}/BIDS/sub-25659/ses-1/func/sub-25659_ses-1_task-rest_space-MNI152NLin6Asym_desc-" \
f"smoothAROMAnonaggr_bold_short.nii.gz"
func_img = nib.load(func_file)
nip = clustools.NiParcellate(func_file=func_file,
clust_mask=clust_mask,
k=k,
clust_type=clust_type,
local_corr=local_corr,
outdir=out_dir,
conf=None,
mask=mask)
atlas = nip.create_clean_mask()
nip.create_local_clustering(overwrite=True, r_thresh=0.4)
out_path = f"{str(tmpdir.name)}/parc_tmp.nii.gz"
nib.save(nip.parcellate(func_img), out_path)
assert out_path is not None
assert atlas is not None
def test_ni_parcellate_mult_conn_comps(clust_type):
"""
Test for ni_parcellate with multiple connected components
"""
import tempfile
base_dir = str(Path(__file__).parent / "examples")
out_dir = f"{base_dir}/outputs/sub-0025427/ses-1/func"
tmpdir = tempfile.TemporaryDirectory()
k = 150
if clust_type != 'ncut':
local_corr = 'allcorr'
else:
local_corr = 'tcorr'
clust_mask = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
mask = f"{base_dir}/BIDS/sub-0025427/ses-1/func/sub-0025427_ses-1_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz"
func_file = f"{base_dir}/BIDS/sub-0025427/ses-1/func/sub-0025427_ses-1_task-rest_space-MNI152NLin2009cAsym_desc-smoothAROMAnonaggr_bold_short.nii.gz"
func_img = nib.load(func_file)
nip = clustools.NiParcellate(func_file=func_file,
clust_mask=clust_mask,
k=k,
clust_type=clust_type,
local_corr=local_corr,
outdir=out_dir,
conf=None,
mask=mask)
atlas = nip.create_clean_mask()
nip = clustools.NiParcellate(func_file=func_file,
clust_mask=clust_mask,
k=k,
clust_type=clust_type,
local_corr=local_corr,
outdir=out_dir,
conf=None,
mask=mask)
if not nip.uatlas:
nip.uatlas = f"{tmpdir.name}/clust-{clust_type}_k{str(k)}.nii.gz"
nip._clust_mask_corr_img = nib.load(clust_mask)
nip.create_local_clustering(overwrite=True, r_thresh=0.4)
out_path = f"{str(tmpdir.name)}/parc_tmp.nii.gz"
nib.save(nip.parcellate(func_img), out_path)
assert atlas is not None
assert out_path is not None
def _run_interface(self, runtime):
import gc
from nipype.utils.filemanip import fname_presuffix, copyfile
from pynets.fmri import clustools
from pynets.registration.reg_utils import check_orient_and_dims
clust_list = ['kmeans', 'ward', 'complete', 'average', 'ncut', 'rena']
clust_mask_temp_path = check_orient_and_dims(self.inputs.clust_mask, self.inputs.vox_size,
outdir=runtime.cwd)
if self.inputs.mask:
out_name_mask = fname_presuffix(self.inputs.mask, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.mask, out_name_mask, copy=True, use_hardlink=False)
else:
out_name_mask = None
out_name_func_file = fname_presuffix(self.inputs.func_file, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.func_file, out_name_func_file, copy=True, use_hardlink=False)
if self.inputs.conf:
out_name_conf = fname_presuffix(self.inputs.conf, suffix='_tmp', newpath=runtime.cwd)
copyfile(self.inputs.conf, out_name_conf, copy=True, use_hardlink=False)
else:
out_name_conf = None
nip = clustools.NiParcellate(func_file=out_name_func_file,
clust_mask=clust_mask_temp_path,
k=self.inputs.k,
clust_type=self.inputs.clust_type,
local_corr=self.inputs.local_corr,
conf=out_name_conf,
mask=out_name_mask)
atlas = nip.create_clean_mask()
nip.create_local_clustering(overwrite=True, r_thresh=0.4)
if self.inputs.clust_type in clust_list:
uatlas = nip.parcellate()
else:
raise ValueError('Clustering method not recognized. '
'See: https://nilearn.github.io/modules/generated/nilearn.regions.Parcellations.'
'html#nilearn.regions.Parcellations')
del nip
gc.collect()
self._results['atlas'] = atlas
self._results['uatlas'] = uatlas
self._results['clust_mask'] = self.inputs.clust_mask
self._results['k'] = self.inputs.k
self._results['clust_type'] = self.inputs.clust_type
self._results['clustering'] = True
return runtime
def test_ni_parcellate_mult_conn_comps(clust_type):
"""
Test for ni_parcellate with multiple connected components
"""
import tempfile
base_dir = str(Path(__file__).parent / "examples")
out_dir = f"{base_dir}/outputs/sub-25659/ses-1/func"
tmpdir = tempfile.TemporaryDirectory()
k = 150
if clust_type != 'ncut':
local_corr = 'allcorr'
else:
local_corr = 'tcorr'
clust_mask = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
mask = f"{base_dir}/BIDS/sub-25659/ses-1/anat/sub-25659_desc-brain_" \
f"mask.nii.gz"
func_file = f"{base_dir}/BIDS/sub-25659/ses-1/func/sub-25659_ses-1_task-" \
f"rest_space-MNI152NLin6Asym_desc-" \
f"smoothAROMAnonaggr_bold_short.nii.gz"
conf = f"{base_dir}/BIDS/sub-25659/ses-1/func/" \
f"sub-25659_ses-1_task-rest_desc-confounds_regressors.tsv"
func_img = nib.load(func_file)
nip = clustools.NiParcellate(func_file=func_file,
clust_mask=clust_mask,
k=k,
clust_type=clust_type,
local_corr=local_corr,
outdir=out_dir,
conf=conf,
mask=mask)
atlas = nip.create_clean_mask()
if not nip.uatlas:
nip.uatlas = f"{tmpdir.name}/clust-{clust_type}_k{str(k)}.nii.gz"
nip._clust_mask_corr_img = nib.load(clust_mask)
nip.create_local_clustering(overwrite=True, r_thresh=0.4)
out_path = f"{str(tmpdir.name)}/parc_tmp.nii.gz"
parcellation = clustools.parcellate(
func_img, local_corr, clust_type, nip._local_conn_mat_path,
nip.num_conn_comps, nip._clust_mask_corr_img, nip._standardize,
nip._detrending, nip.k, nip._local_conn, nip.conf, tmpdir.name,
nip._conn_comps)
nib.save(parcellation, out_path)
assert atlas is not None
assert os.path.isfile(out_path)
def test_ni_parcellate(local_corr, clust_type, k):
"""
Test for ni_parcellate
"""
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
clust_mask = base_dir + '/triple_net_ICA_overlap_3_sig_bin.nii.gz'
func_file = dir_path + '/002.nii.gz'
conf = None
mask = None
nip = clustools.NiParcellate(func_file=func_file,
clust_mask=clust_mask,
k=k,
clust_type=clust_type,
local_corr=local_corr,
conf=conf,
mask=mask)
atlas = nip.create_clean_mask()
nip.create_local_clustering(overwrite=True, r_thresh=0.5)
uatlas = nip.parcellate()
assert atlas is not None
assert uatlas is not None
