def test_nodemaker_tools_parlistfile_WB():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'
start_time = time.time()
[WB_coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
print("%s%s%s" % (
'get_names_and_coords_of_parcels (User-atlas whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
WB_label_names = np.arange(len(WB_coords) +
1)[np.arange(len(WB_coords) + 1) != 0].tolist()
start_time = time.time()
WB_parcel_list = nodemaker.gen_img_list(parlistfile)
[WB_parcels_map_nifti,
parcel_list_exp] = nodemaker.create_parcel_atlas(WB_parcel_list)
print(
"%s%s%s" %
('create_parcel_atlas (User-atlas whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert WB_coords is not None
assert WB_label_names is not None
assert WB_parcel_list is not None
assert WB_parcels_map_nifti is not None
assert parcel_list_exp is not None
def prep_nodes(node_size, parc, parcel_list, net_parcels_map_nifti, network,
dir_path, mask, atlas_select):
import shutil
from pynets import diffconnectometry, nodemaker
if parc is True:
node_size = 'parc'
if network:
seeds_dir = "%s%s%s%s%s%s%s" % (dir_path, '/seeds_', network, '_',
atlas_select, '_', str(node_size))
else:
seeds_dir = "%s%s%s%s%s" % (dir_path, '/seeds_', atlas_select, '_',
str(node_size))
if os.path.exists(seeds_dir) is True:
shutil.rmtree(seeds_dir)
if not os.path.exists(seeds_dir):
os.mkdir(seeds_dir)
if parc is True:
# If masking was performed, get reduced list
if mask or network:
parcel_list = nodemaker.gen_img_list(net_parcels_map_nifti)
diffconnectometry.save_parcel_vols(parcel_list, seeds_dir)
seeds_list = get_seeds_list(seeds_dir)
return parcel_list, seeds_dir, node_size, seeds_list
def test_nodemaker_tools_masking_parlistfile_RSN():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
dir_path = base_dir + '/997'
func_file = dir_path + '/sub-997_ses-01_task-REST_run-01_bold_space-MNI152NLin2009cAsym_preproc_masked.nii.gz'
parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'
mask = dir_path + '/pDMN_3_bin.nii.gz'
network = 'Default'
ID = '997'
perc_overlap = 0.10
parc = True
start_time = time.time()
[coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
print("%s%s%s" % ('get_names_and_coords_of_parcels --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
label_names = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
start_time = time.time()
parcel_list = nodemaker.gen_img_list(parlistfile)
[net_coords, net_parcel_list, net_label_names,
network] = nodemaker.get_node_membership(network, func_file, coords,
label_names, parc, parcel_list)
print("%s%s%s" % ('get_node_membership --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_coords_masked, net_label_names_masked, net_parcel_list_masked
] = nodemaker.parcel_masker(mask, net_coords, net_parcel_list,
net_label_names, dir_path, ID, perc_overlap)
print("%s%s%s" % ('parcel_masker --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_parcels_map_nifti,
parcel_list_exp] = nodemaker.create_parcel_atlas(net_parcel_list_masked)
print("%s%s%s" % ('create_parcel_atlas --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
out_path = nodemaker.gen_network_parcels(parlistfile, network,
net_label_names_masked, dir_path)
print("%s%s%s" % ('gen_network_parcels --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert coords is not None
assert net_coords is not None
assert net_label_names is not None
assert net_parcel_list is not None
assert net_coords_masked is not None
assert net_label_names_masked is not None
assert net_parcel_list_masked is not None
assert out_path is not None
assert net_parcels_map_nifti is not None
assert parcel_list_exp is not None
assert network is not None
def test_nodemaker_tools_masking_parlistfile_WB():
# Set example inputs
base_dir = str(Path(__file__).parent/"examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
dir_path = base_dir + '/997'
parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'
atlas_select = 'whole_brain_cluster_labels_PCA200'
roi = dir_path + '/pDMN_3_bin.nii.gz'
mask = None
ID = '997'
parc = True
perc_overlap = 0.10
start_time = time.time()
[WB_coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
print("%s%s%s" % ('get_names_and_coords_of_parcels (Masking whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
WB_label_names = np.arange(len(WB_coords) + 1)[np.arange(len(WB_coords) + 1) != 0].tolist()
start_time = time.time()
WB_parcel_list = nodemaker.gen_img_list(parlistfile)
[_, _, WB_parcel_list_masked] = nodemaker.parcel_masker(roi, WB_coords, WB_parcel_list, WB_label_names, dir_path,
ID, mask, perc_overlap)
print("%s%s%s" % ('parcel_masker (Masking whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[WB_parcels_map_nifti, parcel_list_exp] = nodemaker.create_parcel_atlas(WB_parcel_list_masked)
print("%s%s%s" % ('create_parcel_atlas (Masking whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[WB_net_parcels_map_nifti_unmasked, WB_coords_unmasked, _,
WB_atlas_select, WB_uatlas_select] = nodemaker.node_gen(WB_coords, WB_parcel_list, WB_label_names, dir_path,
ID, parc, atlas_select, parlistfile)
print("%s%s%s" % ('node_gen (Masking whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[WB_net_parcels_map_nifti_masked, WB_coords_masked, WB_label_names_masked,
WB_atlas_select, WB_uatlas_select] = nodemaker.node_gen_masking(roi, WB_coords, WB_parcel_list, WB_label_names,
dir_path, ID, parc, atlas_select, parlistfile,
mask)
print("%s%s%s" % ('node_gen_masking (Masking whole-brain version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert WB_coords is not None
assert WB_label_names is not None
assert WB_parcel_list is not None
assert WB_coords_masked is not None
assert WB_label_names_masked is not None
assert WB_parcel_list_masked is not None
assert WB_parcels_map_nifti is not None
assert parcel_list_exp is not None
assert WB_net_parcels_map_nifti_unmasked is not None
assert WB_coords_unmasked is not None
assert WB_net_parcels_map_nifti_masked is not None
assert WB_coords_masked is not None
def test_nodemaker_tools_masking_parlistfile_RSN():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
func_file = dir_path + '/002.nii.gz'
parlistfile = base_dir + '/whole_brain_cluster_labels_PCA200.nii.gz'
roi = base_dir + '/pDMN_3_bin.nii.gz'
network = 'Default'
ID = '002'
perc_overlap = 0.10
parc = True
start_time = time.time()
[coords, _, _] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
print("%s%s%s" % ('get_names_and_coords_of_parcels --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
labels = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
start_time = time.time()
parcel_list = nodemaker.gen_img_list(parlistfile)
[net_coords, net_parcel_list, net_labels,
network] = nodemaker.get_node_membership(network, func_file, coords,
labels, parc, parcel_list)
print("%s%s%s" % ('get_node_membership --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_coords_masked, net_labels_masked, net_parcel_list_masked
] = nodemaker.parcel_masker(roi, net_coords, net_parcel_list, net_labels,
dir_path, ID, perc_overlap)
print("%s%s%s" % ('parcel_masker --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_parcels_map_nifti,
parcel_list_exp] = nodemaker.create_parcel_atlas(net_parcel_list_masked)
print("%s%s%s" % ('create_parcel_atlas --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
out_path = nodemaker.gen_network_parcels(parlistfile, network,
net_labels_masked, dir_path)
print("%s%s%s" % ('gen_network_parcels --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert coords is not None
assert net_coords is not None
assert net_labels is not None
assert net_parcel_list is not None
assert net_coords_masked is not None
assert net_labels_masked is not None
assert net_parcel_list_masked is not None
assert out_path is not None
assert net_parcels_map_nifti is not None
assert parcel_list_exp is not None
assert network is not None
def gen_network_parcels(uatlas_select, network, labels, dir_path):
from nilearn.image import concat_imgs
from pynets import nodemaker
img_list = nodemaker.gen_img_list(uatlas_select)
print("%s%s%s" % ('\nExtracting parcels associated with ', network,
' network locations...\n'))
net_parcels = [i for j, i in enumerate(img_list) if j in labels]
bna_4D = concat_imgs(net_parcels).get_data()
index_vec = np.array(range(len(net_parcels))) + 1
net_parcels_sum = np.sum(index_vec * bna_4D, axis=3)
net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum, affine=np.eye(4))
out_path = "%s%s%s%s" % (dir_path, '/', network, '_parcels.nii.gz')
nib.save(net_parcels_map_nifti, out_path)
return out_path
def gen_network_parcels(uatlas, network, labels, dir_path):
"""
Return a modified verion of an atlas parcellation label, where labels have been filtered baseed on their spatial
affinity for a specified RSN definition.
Parameters
----------
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
dir_path : str
Path to directory containing subject derivative data for given run.
Returns
-------
out_path : str
File path to a new, RSN-filtered atlas parcellation Nifti1Image.
"""
from nilearn.image import concat_imgs
from pynets import nodemaker
import os.path as op
if not op.isfile(uatlas):
raise ValueError(
'\nERROR: User-specified atlas input not found! Check that the file(s) specified with the -ua '
'flag exist(s)')
img_list = nodemaker.gen_img_list(uatlas)
print("%s%s%s" % ('\nExtracting parcels associated with ', network,
' network locations...\n'))
net_parcels = [i for j, i in enumerate(img_list) if j in labels]
bna_4D = concat_imgs(net_parcels).get_fdata()
index_vec = np.array(range(len(net_parcels))) + 1
net_parcels_sum = np.sum(index_vec * bna_4D, axis=3)
net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum, affine=np.eye(4))
out_path = "%s%s%s%s" % (dir_path, '/', network, '_parcels.nii.gz')
nib.save(net_parcels_map_nifti, out_path)
net_parcels_map_nifti.uncache()
return out_path
def gen_network_parcels(uatlas_select, network, labels, dir_path):
from nilearn.image import concat_imgs
from pynets import nodemaker
import os.path as op
if not op.isfile(uatlas_select):
raise ValueError(
'\nERROR: User-specified atlas input not found! Check that the file(s) specified with the -ua '
'flag exist(s)')
img_list = nodemaker.gen_img_list(uatlas_select)
print("%s%s%s" % ('\nExtracting parcels associated with ', network,
' network locations...\n'))
net_parcels = [i for j, i in enumerate(img_list) if j in labels]
bna_4D = concat_imgs(net_parcels).get_fdata()
index_vec = np.array(range(len(net_parcels))) + 1
net_parcels_sum = np.sum(index_vec * bna_4D, axis=3)
net_parcels_map_nifti = nib.Nifti1Image(net_parcels_sum, affine=np.eye(4))
out_path = "%s%s%s%s" % (dir_path, '/', network, '_parcels.nii.gz')
nib.save(net_parcels_map_nifti, out_path)
net_parcels_map_nifti.uncache()
return out_path
def fetch_nodes_and_labels(atlas_select,
uatlas_select,
ref_txt,
parc,
in_file,
use_AAL_naming,
clustering=False):
from pynets import utils, nodemaker
import pandas as pd
import time
from pathlib import Path
import os.path as op
base_path = utils.get_file()
# Test if atlas_select is a nilearn atlas. If so, fetch coords, labels, and/or networks.
nilearn_parc_atlases = [
'atlas_harvard_oxford', 'atlas_aal', 'atlas_destrieux_2009',
'atlas_talairach_gyrus', 'atlas_talairach_ba', 'atlas_talairach_lobe'
]
nilearn_coords_atlases = ['coords_power_2011', 'coords_dosenbach_2010']
nilearn_prob_atlases = ['atlas_msdl', 'atlas_pauli_2017']
if uatlas_select is None and atlas_select in nilearn_parc_atlases:
[label_names, networks_list,
uatlas_select] = nodemaker.nilearn_atlas_helper(atlas_select, parc)
if uatlas_select:
if not isinstance(uatlas_select, str):
nib.save(uatlas_select,
"%s%s%s" % ('/tmp/', atlas_select, '.nii.gz'))
uatlas_select = "%s%s%s" % ('/tmp/', atlas_select, '.nii.gz')
[coords, _, par_max
] = nodemaker.get_names_and_coords_of_parcels(uatlas_select)
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas_select)
else:
parcel_list = None
else:
raise ValueError(
"%s%s%s" %
('\nERROR: Atlas file for ', atlas_select, ' not found!'))
elif uatlas_select is None and parc is False and atlas_select in nilearn_coords_atlases:
print(
'Fetching coords and labels from nilearn coordsinate-based atlas library...'
)
# Fetch nilearn atlas coords
[coords, _, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
parcel_list = None
par_max = None
elif uatlas_select is None and parc is False and atlas_select in nilearn_prob_atlases:
from nilearn.plotting import find_probabilistic_atlas_cut_coords
print(
'Fetching coords and labels from nilearn probabilistic atlas library...'
)
# Fetch nilearn atlas coords
[label_names, networks_list,
uatlas_select] = nodemaker.nilearn_atlas_helper(atlas_select, parc)
coords = find_probabilistic_atlas_cut_coords(maps_img=uatlas_select)
if uatlas_select:
if not isinstance(uatlas_select, str):
nib.save(uatlas_select,
"%s%s%s" % ('/tmp/', atlas_select, '.nii.gz'))
uatlas_select = "%s%s%s" % ('/tmp/', atlas_select, '.nii.gz')
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas_select)
else:
parcel_list = None
else:
raise ValueError(
"%s%s%s" %
('\nERROR: Atlas file for ', atlas_select, ' not found!'))
par_max = None
elif uatlas_select:
if clustering is True:
while True:
if op.isfile(uatlas_select):
break
else:
print('Waiting for atlas file...')
time.sleep(15)
atlas_select = uatlas_select.split('/')[-1].split('.')[0]
try:
# Fetch user-specified atlas coords
[coords, atlas_select, par_max
] = nodemaker.get_names_and_coords_of_parcels(uatlas_select)
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas_select)
else:
parcel_list = None
# Describe user atlas coords
print("%s%s%s%s" %
('\n', atlas_select, ' comes with {0} '.format(par_max),
'parcels\n'))
except ValueError:
print(
'\n\nError: Either you have specified the name of a nilearn atlas that does not exist or you have not '
'supplied a 3d atlas parcellation image!\n\n')
parcel_list = None
par_max = None
coords = None
label_names = None
networks_list = None
else:
networks_list = None
label_names = None
parcel_list = None
par_max = None
coords = None
# Labels prep
if atlas_select:
if label_names:
pass
else:
if ref_txt is not None and op.exists(ref_txt):
dict_df = pd.read_csv(ref_txt,
sep=" ",
header=None,
names=["Index", "Region"])
label_names = dict_df['Region'].tolist()
else:
try:
ref_txt = "%s%s%s%s" % (str(
Path(base_path).parent), '/labelcharts/', atlas_select,
'.txt')
if op.exists(ref_txt):
try:
dict_df = pd.read_csv(ref_txt,
sep="\t",
header=None,
names=["Index", "Region"])
label_names = dict_df['Region'].tolist()
#print(label_names)
except:
print(
"WARNING: label names from label reference file failed to populate or are invalid. "
"Attempting AAL naming...")
try:
label_names = nodemaker.AAL_naming(coords)
# print(label_names)
except:
print('AAL reference labeling failed!')
label_names = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
if use_AAL_naming is True:
try:
label_names = nodemaker.AAL_naming(coords)
# print(label_names)
except:
print('AAL reference labeling failed!')
label_names = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
print('Using generic numbering labels...')
label_names = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
except:
print(
"Label reference file not found. Attempting AAL naming..."
)
if use_AAL_naming is True:
try:
label_names = nodemaker.AAL_naming(coords)
#print(label_names)
except:
print('AAL reference labeling failed!')
label_names = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
print('Using generic numbering labels...')
label_names = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print(
'WARNING: No labels available since atlas name is not specified!')
print("%s%s" % ('Labels:\n', label_names))
atlas_name = atlas_select
dir_path = utils.do_dir_path(atlas_select, in_file)
return label_names, coords, atlas_name, networks_list, parcel_list, par_max, uatlas_select, dir_path
def fetch_nodes_and_labels(atlas,
uatlas,
ref_txt,
parc,
in_file,
use_AAL_naming,
clustering=False):
"""
General API for fetching, identifying, and defining atlas nodes based on coordinates and/or labels.
Parameters
----------
atlas : str
Name of a Nilearn-hosted coordinate or parcellation/label-based atlas supported for fetching.
See Nilearn's datasets.atlas module for more detailed reference.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
ref_txt : str
Path to an atlas reference .txt file that maps labels to intensities corresponding to uatlas.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
in_file : str
File path to Nifti1Image object whose affine will provide sampling reference for fetching.
use_AAL_naming : bool
Indicates whether to perform Automated-Anatomical Labeling of each coordinate from a list of a voxel
coordinates.
clustering : bool
Indicates whether clustering was performed. Default is False.
Returns
-------
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples in mm-space corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
atlas_name : str
Name of atlas parcellation (can differ slightly from fetch API string).
networks_list : list
List of RSN's and their associated cooordinates, if predefined uniquely for a given atlas.
parcel_list : list
List of 3D boolean numpy arrays or binarized Nifti1Images corresponding to ROI masks.
par_max : int
The maximum label intensity in the parcellation image.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
dir_path : str
Path to directory containing subject derivative data for given run.
"""
from pynets import utils, nodemaker
import pandas as pd
import time
from pathlib import Path
import os.path as op
base_path = utils.get_file()
# Test if atlas is a nilearn atlas. If so, fetch coords, labels, and/or networks.
nilearn_parc_atlases = [
'atlas_harvard_oxford', 'atlas_aal', 'atlas_destrieux_2009',
'atlas_talairach_gyrus', 'atlas_talairach_ba', 'atlas_talairach_lobe'
]
nilearn_coords_atlases = ['coords_power_2011', 'coords_dosenbach_2010']
nilearn_prob_atlases = ['atlas_msdl', 'atlas_pauli_2017']
if uatlas is None and atlas in nilearn_parc_atlases:
[labels, networks_list,
uatlas] = nodemaker.nilearn_atlas_helper(atlas, parc)
if uatlas:
if not isinstance(uatlas, str):
nib.save(uatlas, "%s%s%s" % ('/tmp/', atlas, '.nii.gz'))
uatlas = "%s%s%s" % ('/tmp/', atlas, '.nii.gz')
[coords, _,
par_max] = nodemaker.get_names_and_coords_of_parcels(uatlas)
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas)
else:
parcel_list = None
else:
raise ValueError(
"%s%s%s" % ('\nERROR: Atlas file for ', atlas, ' not found!'))
elif uatlas is None and parc is False and atlas in nilearn_coords_atlases:
print(
'Fetching coords and labels from nilearn coordinate-based atlas library...'
)
# Fetch nilearn atlas coords
[coords, _, networks_list,
labels] = nodemaker.fetch_nilearn_atlas_coords(atlas)
parcel_list = None
par_max = None
elif uatlas is None and parc is False and atlas in nilearn_prob_atlases:
from nilearn.plotting import find_probabilistic_atlas_cut_coords
print(
'Fetching coords and labels from nilearn probabilistic atlas library...'
)
# Fetch nilearn atlas coords
[labels, networks_list,
uatlas] = nodemaker.nilearn_atlas_helper(atlas, parc)
coords = find_probabilistic_atlas_cut_coords(maps_img=uatlas)
if uatlas:
if not isinstance(uatlas, str):
nib.save(uatlas, "%s%s%s" % ('/tmp/', atlas, '.nii.gz'))
uatlas = "%s%s%s" % ('/tmp/', atlas, '.nii.gz')
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas)
else:
parcel_list = None
else:
raise ValueError(
"%s%s%s" % ('\nERROR: Atlas file for ', atlas, ' not found!'))
par_max = None
elif uatlas:
if clustering is True:
while True:
if op.isfile(uatlas):
break
else:
print('Waiting for atlas file...')
time.sleep(15)
atlas = uatlas.split('/')[-1].split('.')[0]
try:
# Fetch user-specified atlas coords
[coords, atlas,
par_max] = nodemaker.get_names_and_coords_of_parcels(uatlas)
if parc is True:
parcel_list = nodemaker.gen_img_list(uatlas)
else:
parcel_list = None
# Describe user atlas coords
print(
"%s%s%s%s" %
('\n', atlas, ' comes with {0} '.format(par_max), 'parcels\n'))
except ValueError:
print(
'\n\nError: Either you have specified the name of a nilearn atlas that does not exist or '
'you have not supplied a 3d atlas parcellation image!\n\n')
parcel_list = None
par_max = None
coords = None
labels = None
networks_list = None
else:
networks_list = None
labels = None
parcel_list = None
par_max = None
coords = None
# Labels prep
if atlas:
if labels:
pass
else:
if ref_txt is not None and op.exists(ref_txt):
dict_df = pd.read_csv(ref_txt,
sep=" ",
header=None,
names=["Index", "Region"])
labels = dict_df['Region'].tolist()
else:
try:
ref_txt = "%s%s%s%s" % (str(Path(base_path).parent),
'/labelcharts/', atlas, '.txt')
if op.exists(ref_txt):
try:
dict_df = pd.read_csv(ref_txt,
sep="\t",
header=None,
names=["Index", "Region"])
labels = dict_df['Region'].tolist()
except:
print(
"WARNING: label names from label reference file failed to populate or are invalid. "
"Attempting AAL naming...")
try:
labels = nodemaker.AAL_naming(coords)
except:
print('AAL reference labeling failed!')
labels = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
if use_AAL_naming is True:
try:
labels = nodemaker.AAL_naming(coords)
except:
print('AAL reference labeling failed!')
labels = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
print('Using generic numbering labels...')
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
except:
print(
"Label reference file not found. Attempting AAL naming..."
)
if use_AAL_naming is True:
try:
labels = nodemaker.AAL_naming(coords)
except:
print('AAL reference labeling failed!')
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print('Using generic numbering labels...')
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print(
'WARNING: No labels available since atlas name is not specified!')
print("%s%s" % ('Labels:\n', labels))
atlas_name = atlas
dir_path = utils.do_dir_path(atlas, in_file)
if len(coords) != len(labels):
labels = len(coords) * [np.nan]
if len(coords) != len(labels):
raise ValueError(
'ERROR: length of coordinates is not equal to length of label names'
)
return labels, coords, atlas_name, networks_list, parcel_list, par_max, uatlas, dir_path