def test_nodemaker_tools_masking_coords_WB():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
dir_path = base_dir + '/997'
mask = dir_path + '/pDMN_3_bin.nii.gz'
atlas_select = 'coords_dosenbach_2010'
error = 2
start_time = time.time()
[WB_coords, _, _,
WB_label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
print("%s%s%s" % (
'fetch_nilearn_atlas_coords (Masking whole-brain coords version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[WB_coords_masked,
WB_label_names_masked] = nodemaker.coord_masker(mask, WB_coords,
WB_label_names, error)
print("%s%s%s" %
('coord_masker (Masking whole-brain coords version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert WB_coords is not None
assert WB_coords is not None
assert WB_coords_masked is not None
assert WB_label_names is not None
assert WB_label_names_masked is not None
def test_nodemaker_tools_nilearn_coords_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'
network = 'Default'
#can't find original atlas_select
atlas_select = 'coords_dosenbach_2010'
#atlas_select = dir_path + '/coords_dosenbach_2010.nii.gz'
parc = False
parcel_list = None
start_time = time.time()
[coords, _, _,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
print("%s%s%s" % ('fetch_nilearn_atlas_coords --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_coords, _, 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'))
assert coords is not None
assert label_names is not None
assert net_coords is not None
assert net_label_names is not None
assert network is not None
def test_nodemaker_tools_nilearn_coords_RSN():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
func_file = dir_path + '/002.nii.gz'
network = 'Default'
atlas = 'coords_dosenbach_2010'
parc = False
parcel_list = None
start_time = time.time()
[coords, _, _, labels] = nodemaker.fetch_nilearn_atlas_coords(atlas)
print("%s%s%s" % ('fetch_nilearn_atlas_coords --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_coords, _, 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'))
assert coords is not None
assert labels is not None
assert net_coords is not None
assert net_labels is not None
assert network is not None
def test_nodemaker_tools_masking_coords_WB():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
roi = base_dir + '/pDMN_3_bin.nii.gz'
atlas = 'coords_dosenbach_2010'
error = 2
start_time = time.time()
[WB_coords, _, _, WB_labels] = nodemaker.fetch_nilearn_atlas_coords(atlas)
print("%s%s%s" % (
'fetch_nilearn_atlas_coords (Masking whole-brain coords version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[WB_coords_masked,
WB_labels_masked] = nodemaker.coords_masker(roi, WB_coords, WB_labels,
error)
print("%s%s%s" %
('coords_masker (Masking whole-brain coords version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert WB_coords is not None
assert WB_coords is not None
assert WB_coords_masked is not None
assert WB_labels is not None
assert WB_labels_masked is not None
def test_nodemaker_tools_masking_coords_RSN():
##Set example inputs##
base_dir = str(Path(__file__).parent / "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'
mask = dir_path + '/pDMN_3_bin.nii.gz'
atlas_select = 'coords_dosenbach_2010'
network = 'Default'
parc = False
parcel_list = None
[coords, atlas_select, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
[net_coords, net_parcel_list, net_label_names,
network] = nodemaker.get_node_membership(network, func_file, coords,
label_names, parc, parcel_list)
[net_coords_masked,
net_label_names_masked] = nodemaker.coord_masker(mask, net_coords,
net_label_names)
assert coords is not None
assert net_coords is not None
assert net_coords_masked is not None
assert net_label_names is not None
assert net_label_names_masked is not None
assert network is not None
def test_nodemaker_tools_nilearn_coords_WB():
##Set example inputs##
atlas_select = 'coords_dosenbach_2010'
[WB_coords, atlas_select, networks_list,
WB_label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
assert WB_coords is not None
assert WB_label_names is not None
def RSN_fetch_nodes_and_labels(atlas_select, parlistfile, ref_txt, parc,
func_file):
from pynets import utils, nodemaker
import pandas as pd
##Test if atlas_select is a nilearn atlas. If so, fetch coords, labels, and/or networks.
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
if atlas_select in nilearn_atlases:
[label_names, networks_list,
parlistfile] = utils.nilearn_atlas_helper(atlas_select)
##Get coordinates and/or parcels from atlas
if parlistfile is None and parc == False:
print(
'Fetching coordinates and labels from nilearn coordinate-based atlases'
)
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
parcel_list = None
par_max = None
else:
##Fetch user-specified atlas coords
[coords, atlas_select, par_max,
parcel_list] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
networks_list = None
##Labels prep
try:
label_names
except:
if ref_txt is not None and os.path.exists(ref_txt):
atlas_select = os.path.basename(ref_txt).split('.txt')[0]
dict_df = pd.read_csv(ref_txt,
sep=" ",
header=None,
names=["Index", "Region"])
label_names = dict_df['Region'].tolist()
else:
label_names = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
if label_names is None:
label_names = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
try:
atlas_name
except:
atlas_name = atlas_select
dir_path = utils.do_dir_path(atlas_select, func_file)
return (label_names, coords, atlas_name, networks_list, parcel_list,
par_max, parlistfile, dir_path)
def test_nodemaker_tools_nilearn_coords_WB():
# Set example inputs
atlas_select = 'coords_dosenbach_2010'
start_time = time.time()
[WB_coords, _, _, WB_label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
print("%s%s%s" % ('fetch_nilearn_atlas_coords (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
def WB_fetch_nodes_and_labels(atlas_select, parlistfile, ref_txt, parc, func_file):
from pynets import utils, nodemaker
import pandas as pd
from pathlib import Path
##Test if atlas_select is a nilearn atlas. If so, fetch coords, labels, and/or networks.
nilearn_parc_atlases=['atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009']
nilearn_coord_atlases=['harvard_oxford', 'msdl', 'coords_power_2011', 'smith_2009', 'basc_multiscale_2015', 'allen_2011', 'coords_dosenbach_2010']
if atlas_select in nilearn_parc_atlases:
[label_names, networks_list, parlistfile] = utils.nilearn_atlas_helper(atlas_select)
##Get coordinates and/or parcels from atlas
if parlistfile is None and parc == False and atlas_select in nilearn_coord_atlases:
print('Fetching coordinates and labels from nilearn coordinate-based atlases')
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list, label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
parcel_list = None
par_max = None
else:
try:
##Fetch user-specified atlas coords
[coords, atlas_select, par_max, parcel_list] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
networks_list = None
##Describe user atlas coords
print('\n' + str(atlas_select) + ' comes with {0} '.format(par_max) + 'parcels' + '\n')
except:
raise ValueError('\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')
##Labels prep
try:
label_names
except:
if ref_txt is not None and os.path.exists(ref_txt):
atlas_select = os.path.basename(ref_txt).split('.txt')[0]
dict_df = pd.read_csv(ref_txt, sep=" ", header=None, names=["Index", "Region"])
label_names = dict_df['Region'].tolist()
else:
try:
atlas_ref_txt = atlas_select + '.txt'
ref_txt = Path(__file__)/'atlases'/atlas_ref_txt
dict_df = pd.read_csv(ref_txt, sep=" ", header=None, names=["Index", "Region"])
label_names = dict_df['Region'].tolist()
except:
label_names = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
if label_names is None:
label_names = np.arange(len(coords) + 1)[np.arange(len(coords) + 1) != 0].tolist()
try:
atlas_name
except:
atlas_name = atlas_select
dir_path = utils.do_dir_path(atlas_select, func_file)
return(label_names, coords, atlas_name, networks_list, parcel_list, par_max, parlistfile, dir_path)
def test_nodemaker_tools():
##Set example inputs##
NETWORK = 'DMN'
mask = Path(__file__).parent / "examples" / "997" / "pDMN_3_bin.nii.gz"
parlistfile = Path(
__file__
).parent / "examples" / "whole_brain_cluster_labels_PCA100.nii.gz"
atlas_select = 'coords_power_2011'
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
if atlas_name == 'Power 2011 atlas':
network_coords_ref = NETWORK + '_coords.csv'
atlas_coords = pkgutil.get_data("pynets",
"rsnrefs/" + network_coords_ref)
df = pd.read_csv(io.BytesIO(atlas_coords)).ix[:, 0:4]
i = 1
net_coords = []
ix_labels = []
for i in range(len(df)):
x = int(df.ix[i, 1])
y = int(df.ix[i, 2])
z = int(df.ix[i, 3])
net_coords.append((x, y, z))
ix_labels.append(i)
i = i + 1
label_names = ix_labels
if label_names != ix_labels:
try:
label_names = label_names.tolist()
except:
pass
label_names = [label_names[i] for i in ix_labels]
##Test 1
[net_coords,
label_names_out] = nodemaker.coord_masker(str(mask), net_coords,
label_names)
##Test 2
[coords, atlas_name,
par_max] = nodemaker.get_names_and_coords_of_parcels(str(parlistfile))
##Test 3
#out_path = nodemaker.gen_network_parcels(str(parlistfile), NETWORK, labels_names)
assert net_coords is not None
assert label_names_out is not None
assert mask is not None
assert coords is not None
assert atlas_name is not None
assert par_max is not None
def test_nodemaker_tools_masking_coords_WB():
##Set example inputs##
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/997'
mask = dir_path + '/pDMN_3_bin.nii.gz'
atlas_select = 'coords_dosenbach_2010'
[WB_coords, atlas_select, networks_list,
WB_label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
[WB_coords_masked,
WB_label_names_masked] = nodemaker.coord_masker(mask, WB_coords,
WB_label_names)
assert WB_coords is not None
assert WB_coords is not None
assert WB_coords_masked is not None
assert WB_label_names is not None
assert WB_label_names_masked is not None
def test_nodemaker_tools_masking_coords_RSN():
# Set example inputs
base_dir = str(Path(__file__).parent / "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'
mask = dir_path + '/pDMN_3_bin.nii.gz'
atlas_select = 'coords_dosenbach_2010'
network = 'Default'
parc = False
parcel_list = None
error = 2
start_time = time.time()
[coords, _, _,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
print("%s%s%s" %
('fetch_nilearn_atlas_coords (Masking RSN version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_coords, _, net_label_names,
network] = nodemaker.get_node_membership(network, func_file, coords,
label_names, parc, parcel_list)
print("%s%s%s" %
('get_node_membership (Masking RSN version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
start_time = time.time()
[net_coords_masked,
net_label_names_masked] = nodemaker.coord_masker(mask, net_coords,
net_label_names, error)
print("%s%s%s" % ('coord_masker (Masking RSN version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
assert coords is not None
assert net_coords is not None
assert net_coords_masked is not None
assert net_label_names is not None
assert net_label_names_masked is not None
assert network is not None
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 network_connectome(input_file, ID, atlas_select, NETWORK, node_size, mask,
thr, parlistfile, all_nets, conn_model, dens_thresh,
conf, adapt_thresh, plot_switch, bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Test if atlas_select is a nilearn atlas
if atlas_select in nilearn_atlases:
atlas = getattr(datasets, 'fetch_%s' % atlas_select)()
try:
parlistfile = atlas.maps
try:
label_names = atlas.labels
except:
label_names = None
try:
networks_list = atlas.networks
except:
networks_list = None
except RuntimeError:
print('Error, atlas fetching failed.')
sys.exit()
if parlistfile == None and atlas_select not in nilearn_atlases:
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
if atlas_name == 'Power 2011 atlas':
##Reference RSN list
import pkgutil
import io
network_coords_ref = NETWORK + '_coords.csv'
atlas_coords = pkgutil.get_data("pynets",
"rsnrefs/" + network_coords_ref)
df = pd.read_csv(io.BytesIO(atlas_coords)).ix[:, 0:4]
i = 1
net_coords = []
ix_labels = []
for i in range(len(df)):
#print("ROI Reference #: " + str(i))
x = int(df.ix[i, 1])
y = int(df.ix[i, 2])
z = int(df.ix[i, 3])
#print("X:" + str(x) + " Y:" + str(y) + " Z:" + str(z))
net_coords.append((x, y, z))
ix_labels.append(i)
i = i + 1
#print(net_coords)
label_names = ix_labels
elif atlas_name == 'Dosenbach 2010 atlas':
coords = list(tuple(x) for x in coords)
##Get coord membership dictionary
[membership, membership_plotting
] = nodemaker.get_mem_dict(func_file, coords, networks_list)
##Convert to membership dataframe
mem_df = membership.to_frame().reset_index()
nets_avail = list(set(list(mem_df['index'])))
##Get network name equivalents
if NETWORK == 'DMN':
NETWORK = 'default'
elif NETWORK == 'FPTC':
NETWORK = 'fronto-parietal'
elif NETWORK == 'CON':
NETWORK = 'cingulo-opercular'
elif NETWORK not in nets_avail:
print('Error: ' + NETWORK + ' not available with this atlas!')
sys.exit()
##Get coords for network-of-interest
mem_df.loc[mem_df['index'] == NETWORK]
net_coords = mem_df.loc[mem_df['index'] == NETWORK][[0]].values[:,
0]
net_coords = list(tuple(x) for x in net_coords)
ix_labels = mem_df.loc[mem_df['index'] == NETWORK].index.values
####Add code for any special RSN reference lists for the nilearn atlases here#####
##If labels_names are not indices and NETWORK is specified, sub-list label names
if label_names != ix_labels:
try:
label_names = label_names.tolist()
except:
pass
label_names = [label_names[i] for i in ix_labels]
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##If masking, remove those coords that fall outside of the mask
if mask != None:
[net_coords,
label_names] = nodemaker.coord_masker(mask, net_coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_' + NETWORK + '_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(net_coords, f)
labels_path = dir_path + '/labelnames_' + NETWORK + '_' + str(
thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, net_coords, node_size)
else:
##Fetch user-specified atlas coords
[coords_all, atlas_name,
par_max] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
coords = list(tuple(x) for x in coords_all)
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_name
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Convert to membership dataframe
mem_df = membership.to_frame().reset_index()
##Get coords for network-of-interest
mem_df.loc[mem_df['index'] == NETWORK]
net_coords = mem_df.loc[mem_df['index'] == NETWORK][[0]].values[:, 0]
net_coords = list(tuple(x) for x in net_coords)
ix_labels = mem_df.loc[mem_df['index'] == NETWORK].index.values
try:
label_names = [label_names[i] for i in ix_labels]
except:
label_names = ix_labels
if mask != None:
[net_coords,
label_names] = nodemaker.coord_masker(mask, net_coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_' + NETWORK + '_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(net_coords, f)
labels_path = dir_path + '/labelnames_' + NETWORK + '_' + str(
thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, net_coords, node_size)
##Generate network parcels image (through refinement, this could be used
##in place of the 3 lines above)
#net_parcels_img_path = gen_network_parcels(parlistfile, NETWORK, labels)
#parcellation = nib.load(net_parcels_img_path)
#parcel_masker = input_data.NiftiLabelsMasker(labels_img=parcellation, background_label=0, memory='nilearn_cache', memory_level=5, standardize=True)
#ts_within_parcels = parcel_masker.fit_transform(func_file)
#net_ts = ts_within_parcels
##Grow ROIs
masker = input_data.NiftiSpheresMasker(seeds=net_coords,
radius=float(node_size),
allow_overlap=True,
memory_level=5,
memory='nilearn_cache',
verbose=2,
standardize=True)
ts_within_spheres = masker.fit_transform(func_file, confounds=conf)
net_ts = ts_within_spheres
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_' + NETWORK + '_net_ts.txt'
np.savetxt(out_path_ts, net_ts)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_spheres,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_name, dir_path,
ID, NETWORK, label_names, mask)
##Plot network time-series
plotting.plot_timeseries(net_ts, NETWORK, ID, dir_path, atlas_name,
label_names)
##Plot connectome viz for specific Yeo networks
title = "Connectivity Projected on the " + NETWORK
out_path_fig = dir_path + '/' + ID + '_' + NETWORK + '_connectome_plot.png'
niplot.plot_connectome(conn_matrix,
net_coords,
edge_threshold=edge_threshold,
title=title,
display_mode='lyrz',
output_file=out_path_fig)
return est_path, thr
def wb_connectome_with_nl_atlas_coords(input_file, ID, atlas_select, NETWORK,
node_size, mask, thr, all_nets,
conn_model, dens_thresh, conf,
adapt_thresh, plot_switch,
bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary if all_nets option triggered
if all_nets != False:
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Mask coordinates
if mask is not None:
[coords, label_names] = nodemaker.coord_masker(mask, coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_wb_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(coords, f)
labels_path = dir_path + '/labelnames_wb_' + str(thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, coords, node_size)
##Extract within-spheres time-series from funct file
spheres_masker = input_data.NiftiSpheresMasker(seeds=coords,
radius=float(node_size),
memory='nilearn_cache',
memory_level=5,
verbose=2,
standardize=True)
ts_within_spheres = spheres_masker.fit_transform(func_file, confounds=conf)
print('\n' +
'Time series has {0} samples'.format(ts_within_spheres.shape[0]) +
'\n')
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_whole_brain_ts_within_spheres.txt'
np.savetxt(out_path_ts, ts_within_spheres)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_spheres,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_name, dir_path,
ID, NETWORK, label_names, mask)
##Plot connectome viz for all Yeo networks
if all_nets != False:
plotting.plot_membership(membership_plotting, conn_matrix,
conn_model, coords, edge_threshold,
atlas_name, dir_path)
else:
out_path_fig = dir_path + '/' + ID + '_' + atlas_name + '_connectome_viz.png'
niplot.plot_connectome(conn_matrix,
coords,
title=atlas_name,
edge_threshold=edge_threshold,
node_size=20,
colorbar=True,
output_file=out_path_fig)
return est_path, thr
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
