def create_local_clustering(self, overwrite, r_thresh):
"""
API for performing any of a variety of clustering routines available through NiLearn.
"""
import os.path as op
from scipy.sparse import save_npz, load_npz
from nilearn.regions import connected_regions
from pynets.fmri.clustools import make_local_connectivity_tcorr, make_local_connectivity_scorr
conn_comps = len(connected_regions(self._clust_mask_corr_img, extract_type='connected_components',
min_region_size=1)[1])
if self.clust_type == 'kmeans':
if self.k > conn_comps:
if conn_comps != 1:
raise ValueError('k must be less than or equal to the total number of connected components in '
'the mask in the case of kmeans clustering.')
if self.clust_type == 'complete' or self.clust_type == 'average' or self.clust_type == 'single':
if conn_comps > 1:
raise ValueError('Complete, Average, and Single linkage agglomerative clustering are unstable in the '
'case of multiple connected components.')
if self.clust_type == 'ward':
if self.k < conn_comps:
raise ValueError('k must minimally be greater than the total number of connected components in '
'the mask in the case of agglomerative clustering.')
if self.local_corr == 'tcorr' or self.local_corr == 'scorr':
self._local_conn_mat_path = "%s%s%s%s" % (self.uatlas.split('.nii')[0], '_', self.local_corr,
'_conn.npz')
if (not op.isfile(self._local_conn_mat_path)) or (overwrite is True):
if self.local_corr == 'tcorr':
self._local_conn = make_local_connectivity_tcorr(self._func_img, self._clust_mask_corr_img,
thresh=r_thresh)
elif self.local_corr == 'scorr':
self._local_conn = make_local_connectivity_scorr(self._func_img, self._clust_mask_corr_img,
thresh=r_thresh)
else:
raise ValueError('Local connectivity type not available')
print("%s%s" % ('Saving spatially constrained connectivity structure to: ',
self._local_conn_mat_path))
save_npz(self._local_conn_mat_path, self._local_conn)
elif op.isfile(self._local_conn_mat_path):
self._local_conn = load_npz(self._local_conn_mat_path)
elif self.local_corr == 'allcorr':
self._local_conn = 'auto'
else:
raise ValueError('Local connectivity method not recognized. Only tcorr, scorr, and auto are currently '
'supported')
else:
self._local_conn = 'auto'
return
def individual_tcorr_clustering(func_file,
clust_mask,
ID,
k,
clust_type,
thresh=0.5):
import os
from pynets import utils
from pynets.fmri import clustools
nilearn_clust_list = ['kmeans', 'ward', 'complete', 'average']
mask_name = os.path.basename(clust_mask).split('.nii.gz')[0]
atlas_select = "%s%s%s%s%s" % (mask_name, '_', clust_type, '_k', str(k))
print("%s%s%s%s%s%s%s" %
('\nCreating atlas using ', clust_type, ' at cluster level ', str(k),
' for ', str(atlas_select), '...\n'))
dir_path = utils.do_dir_path(atlas_select, func_file)
uatlas_select = "%s%s%s%s%s%s%s%s" % (dir_path, '/', mask_name, '_',
clust_type, '_k', str(k), '.nii.gz')
if clust_type in nilearn_clust_list:
clustools.nil_parcellate(func_file, clust_mask, k, clust_type, ID,
dir_path, uatlas_select)
elif clust_type == 'ncut':
working_dir = "%s%s%s" % (os.path.dirname(func_file), '/',
atlas_select)
outfile = "%s%s%s%s" % (working_dir, '/rm_tcorr_conn_', str(ID),
'.npy')
outfile_parc = "%s%s%s" % (working_dir, '/rm_tcorr_indiv_cluster_',
str(ID))
binfile = "%s%s%s%s%s%s" % (working_dir, '/rm_tcorr_indiv_cluster_',
str(ID), '_', str(k), '.npy')
clustools.make_local_connectivity_tcorr(func_file, clust_mask, outfile,
thresh)
clustools.binfile_parcellate(outfile, outfile_parc, int(k))
clustools.make_image_from_bin_renum(uatlas_select, binfile, clust_mask)
clustering = True
return uatlas_select, atlas_select, clustering, clust_mask, k, clust_type
def test_make_local_connectivity_tcorr():
"""
Test for make_local_connectivity_tcorr functionality
"""
print("testing make_local_connectivity_tcorr")
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
mask_file = base_dir + '/triple_net_ICA_overlap_3_sig_bin.nii.gz'
image_file = dir_path + '/002.nii.gz'
func_img = nib.load(image_file)
mask_img = nib.load(mask_file)
W = clustools.make_local_connectivity_tcorr(func_img,
mask_img,
thresh=0.50)
assert W is not None
def test_make_local_connectivity_tcorr():
"""
Test for make_local_connectivity_tcorr functionality
"""
print("testing make_local_connectivity_tcorr")
base_dir = str(Path(__file__).parent/"examples")
mask_file = f"{base_dir}/miscellaneous/triple_net_ICA_overlap_3_sig_bin.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)
mask_img = nib.load(mask_file)
W = clustools.make_local_connectivity_tcorr(func_img, mask_img, thresh=0.50)
assert W is not None
out_img = clustools.parcellate_ncut(W, 200, mask_img)
assert isinstance(out_img, nib.Nifti1Image)
def create_local_clustering(self, overwrite, r_thresh, min_region_size=80):
"""
API for performing any of a variety of clustering routines available
through NiLearn.
"""
import os.path as op
from scipy.sparse import save_npz, load_npz
from nilearn.regions import connected_regions
try:
conn_comps = connected_regions(
self._clust_mask_corr_img,
extract_type="connected_components",
min_region_size=min_region_size,
)
self._conn_comps = conn_comps[0]
self.num_conn_comps = len(conn_comps[1])
except BaseException:
raise ValueError("Clustering mask is empty!")
if not self._conn_comps:
if np.sum(np.asarray(self._clust_mask_corr_img.dataobj)) == 0:
raise ValueError("Clustering mask is empty!")
else:
self._conn_comps = self._clust_mask_corr_img
self.num_conn_comps = 1
print(
f"Detected {self.num_conn_comps} connected components in "
f"clustering mask with a mininimum region "
f"size of {min_region_size}")
if (
self.clust_type == "complete"
or self.clust_type == "average"
or self.clust_type == "single"
):
if self.num_conn_comps > 1:
raise ValueError(
"Clustering method unstable with spatial constrainsts "
"applied to multiple connected components.")
if (
self.clust_type == "ward" and self.num_conn_comps > 1
) or self.clust_type == "ncut":
if self.k < self.num_conn_comps:
raise ValueError(
"k must minimally be greater than the total number of "
"connected components in "
"the mask in the case of agglomerative clustering.")
if self.local_corr == "tcorr" or self.local_corr == "scorr":
self._local_conn_mat_path = (
f"{self.uatlas.split('.nii')[0]}_"
f"{self.local_corr}_conn.npz"
)
if (not op.isfile(self._local_conn_mat_path)) or (
overwrite is True):
from pynets.fmri.clustools import (
make_local_connectivity_tcorr,
make_local_connectivity_scorr,
)
if self.local_corr == "tcorr":
self._local_conn = make_local_connectivity_tcorr(
self._func_img, self._clust_mask_corr_img,
thresh=r_thresh)
elif self.local_corr == "scorr":
self._local_conn = make_local_connectivity_scorr(
self._func_img, self._clust_mask_corr_img,
thresh=r_thresh)
else:
raise ValueError(
"Local connectivity type not available")
print(
f"Saving spatially constrained connectivity structure"
f" to: {self._local_conn_mat_path}"
)
save_npz(self._local_conn_mat_path, self._local_conn)
elif op.isfile(self._local_conn_mat_path):
self._local_conn = load_npz(self._local_conn_mat_path)
elif self.local_corr == "allcorr":
if self.clust_type == "ncut":
raise ValueError(
"Must select either `tcorr` or `scorr` local "
"connectivity option if you are using "
"`ncut` clustering method")
self._local_conn = "auto"
else:
raise ValueError(
"Local connectivity method not recognized. Only tcorr,"
" scorr, and auto are currently "
"supported")
else:
self._local_conn = "auto"
return
def create_local_clustering(self, overwrite, r_thresh, min_region_size=80):
"""
API for performing any of a variety of clustering routines available through NiLearn.
"""
import os.path as op
from scipy.sparse import save_npz, load_npz
from nilearn.regions import connected_regions
try:
conn_comps = connected_regions(self._clust_mask_corr_img,
extract_type='connected_components',
min_region_size=min_region_size)
self._conn_comps = conn_comps[0]
self.num_conn_comps = len(conn_comps[1])
except:
raise ValueError('Clustering mask is empty!')
if not self._conn_comps:
if np.sum(np.asarray(self._clust_mask_corr_img.dataobj)) == 0:
raise ValueError('Clustering mask is empty!')
else:
self._conn_comps = self._clust_mask_corr_img
self.num_conn_comps = 1
print(
f"Detected {self.num_conn_comps} connected components in clustering mask with a mininimum region "
f"size of {min_region_size}")
if self.clust_type == 'complete' or self.clust_type == 'average' or self.clust_type == 'single':
if self.num_conn_comps > 1:
raise ValueError(
'Clustering method unstable with spatial constrainsts applied to multiple '
'connected components.')
if (self.clust_type == 'ward'
and self.num_conn_comps > 1) or self.clust_type == 'ncut':
if self.k < self.num_conn_comps:
raise ValueError(
'k must minimally be greater than the total number of connected components in '
'the mask in the case of agglomerative clustering.')
if self.local_corr == 'tcorr' or self.local_corr == 'scorr':
self._local_conn_mat_path = f"{self.uatlas.split('.nii')[0]}_{self.local_corr}_conn.npz"
if (not op.isfile(
self._local_conn_mat_path)) or (overwrite is True):
from pynets.fmri.clustools import make_local_connectivity_tcorr, make_local_connectivity_scorr
if self.local_corr == 'tcorr':
self._local_conn = make_local_connectivity_tcorr(
self._func_img,
self._clust_mask_corr_img,
thresh=r_thresh)
elif self.local_corr == 'scorr':
self._local_conn = make_local_connectivity_scorr(
self._func_img,
self._clust_mask_corr_img,
thresh=r_thresh)
else:
raise ValueError(
'Local connectivity type not available')
print(
f"Saving spatially constrained connectivity structure to: {self._local_conn_mat_path}"
)
save_npz(self._local_conn_mat_path, self._local_conn)
elif op.isfile(self._local_conn_mat_path):
self._local_conn = load_npz(self._local_conn_mat_path)
elif self.local_corr == 'allcorr':
if self.clust_type == 'ncut':
raise ValueError(
'Must select either `tcorr` or `scorr` local connectivity option if you are using '
'`ncut` clustering method')
self._local_conn = 'auto'
else:
raise ValueError(
'Local connectivity method not recognized. Only tcorr, scorr, and auto are currently '
'supported')
else:
self._local_conn = 'auto'
return
