def connectivity_data():
"""Fixture for connectivity tests."""
base_dir = os.path.abspath(pkg_resources.resource_filename(
"pynets", "../data/examples"))
func_file = f"{base_dir}/BIDS/sub-25659/ses-1/func/" \
f"sub-25659_ses-1_task-rest_space-T1w_desc-preproc_bold.nii.gz"
mask_file = f"{base_dir}/BIDS/sub-25659/ses-1/func/" \
f"sub-25659_ses-1_task-rest_space-T1w_desc-preproc_" \
f"bold_mask.nii.gz"
parcellation = pkg_resources.resource_filename(
"pynets", "templates/atlases/DesikanKlein2012.nii.gz"
)
masker = NiftiLabelsMasker(
labels_img=nib.load(parcellation), background_label=0,
resampling_target="labels", dtype="auto",
mask_img=nib.load(mask_file), standardize=True)
time_series = masker.fit_transform(func_file)
conn_measure = ConnectivityMeasure(
kind="correlation")
conn_matrix = conn_measure.fit_transform([time_series])[0]
[coords, _, _, label_intensities] = \
get_names_and_coords_of_parcels(parcellation)
labels = ['ROI_' + str(idx) for idx, val in enumerate(label_intensities)]
yield {'time_series': time_series, 'conn_matrix': conn_matrix,
'labels': labels, 'coords': coords, 'indices': label_intensities}
def test_nodemaker_tools_parlistfile_WB():
"""
Test nodemaker_tools_parlistfile_WB functionality
"""
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
parlistfile = f"{base_dir}/miscellaneous/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_labels = 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_labels 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 test_nodemaker_tools_masking_parlistfile_RSN():
"""
Test nodemaker_tools_masking_parlistfile_RSN functionality
"""
# 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 make_node_dict_from_parcellation(parcellation, dir_path, vox_size='2mm'):
from pynets.core.nodemaker import get_names_and_coords_of_parcels, \
parcel_naming
from pynets.core.utils import save_coords_and_labels_to_json
coords, _, _, label_intensities = \
get_names_and_coords_of_parcels(parcellation)
labels = parcel_naming(coords, vox_size)
node_file = save_coords_and_labels_to_json(coords, labels,
dir_path, network='regen',
indices=label_intensities)
return node_file
def test_drop_coords_labels_from_restricted_parcellation():
import tempfile
from nipype.utils.filemanip import copyfile
base_dir = str(Path(__file__).parent / "examples")
parlistfile = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
[coords, _, _, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parlistfile)
labels = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
labs = list(zip(labels, label_intensities))
[parcellation_okay, cleaned_coords, cleaned_labels] = \
nodemaker.drop_coords_labels_from_restricted_parcellation(parlistfile,
coords,
labs)
parcellation_okay_img = nib.load(parcellation_okay)
intensities_ok = list(
np.unique(np.asarray(parcellation_okay_img.dataobj).astype("int"))[1:])
assert len(cleaned_coords) == len(cleaned_labels) == len(intensities_ok)
parlist_img = nib.load(parlistfile)
parlist_img_data = parlist_img.get_fdata()
parlist_img_data[np.where(parlist_img_data == 10)] = 0
par_tmp = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
nib.save(nib.Nifti1Image(parlist_img_data, affine=parlist_img.affine),
par_tmp.name)
[parcellation_okay, cleaned_coords, cleaned_labels] = \
nodemaker.drop_coords_labels_from_restricted_parcellation(parlistfile,
coords,
labs)
parcellation_okay_img = nib.load(parcellation_okay)
intensities_ok = list(
np.unique(np.asarray(parcellation_okay_img.dataobj).astype("int"))[1:])
assert len(cleaned_coords) == len(cleaned_labels) == len(intensities_ok)
bad_coords = np.delete(coords, 30, axis=0)
del labs[-30]
par_tmp2 = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz').name
copyfile(parlistfile, par_tmp2, copy=True, use_hardlink=False)
[parcellation_mod, cleaned_coords, cleaned_labels] = \
nodemaker.drop_coords_labels_from_restricted_parcellation(par_tmp2,
bad_coords,
labs)
parcellation_mod_img = nib.load(parcellation_mod)
intensities_ok = list(
np.unique(np.asarray(parcellation_mod_img.dataobj).astype("int"))[1:])
assert len(cleaned_coords) == len(cleaned_labels) == len(intensities_ok)
def test_nodemaker_tools_parlistfile_RSN():
"""
Test nodemaker_tools_parlistfile_RSN functionality
"""
import pkg_resources
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
template = pkg_resources.resource_filename(
"pynets", f"templates/MNI152_T1_brain_2mm.nii.gz")
dir_path = f"{base_dir}/BIDS/sub-25659/ses-1/func"
parlistfile = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
network = 'Default'
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, template, 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_parcels_map_nifti,
parcel_list_exp] = nodemaker.create_parcel_atlas(net_parcel_list)
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,
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 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_parlistfile_RSN():
"""
Test nodemaker_tools_parlistfile_RSN functionality
"""
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
func_file = f"{base_dir}/BIDS/sub-0025427/ses-1/func/sub-0025427_ses-1_task-rest_space-MNI152NLin2009cAsym_desc-smoothAROMAnonaggr_bold.nii.gz"
dir_path = f"{base_dir}/BIDS/sub-0025427/ses-1/func"
parlistfile = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
network = 'Default'
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_parcels_map_nifti,
parcel_list_exp] = nodemaker.create_parcel_atlas(net_parcel_list)
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,
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 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_parlistfile_WB():
"""
Test nodemaker_tools_parlistfile_WB functionality
"""
# Set example inputs
parlistfile = pkg_resources.resource_filename(
"pynets", "templates/atlases/whole_brain_cluster_labels_PCA200.nii.gz")
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path, exist_ok=True)
shutil.copy2(parlistfile, f"{dir_path}/{os.path.basename(parlistfile)}")
parlistfile = f"{dir_path}/{os.path.basename(parlistfile)}"
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_labels = np.arange(len(WB_coords) +
1)[np.arange(len(WB_coords) + 1) != 0].tolist()
start_time = time.time()
WB_parcel_list = nodemaker.three_to_four_parcellation(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_labels 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 test_streams2graph(fa_wei, dsn):
from dipy.core.gradients import gradient_table
from pynets.registration import register
from pynets.core import nodemaker
from dipy.io import save_pickle
import os
base_dir = str(Path(__file__).parent / "examples")
dwi_file = f"{base_dir}/003/test_out/003/dwi/sub-003_dwi_reor-RAS_res-2mm.nii.gz"
conn_model = 'csd'
min_length = 10
error_margin = 2
directget = 'prob'
track_type = 'particle'
target_samples = 500
overlap_thr = 1
min_span_tree = True
prune = 3
norm = 6
binary = False
roi = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
network = 'Default'
ID = '003'
parc = True
disp_filt = False
node_size = None
dens_thresh = False
atlas = 'whole_brain_cluster_labels_PCA200'
uatlas = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
t1_aligned_mni = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
atlas_dwi = f"{base_dir}/003/dmri/whole_brain_cluster_labels_PCA200_dwi_track.nii.gz"
streams = f"{base_dir}/miscellaneous/003_streamlines_est-csd_nodetype-parc_samples-1000streams_tt-particle_" \
f"dg-prob_ml-10.trk"
B0_mask = f"{base_dir}/003/anat/mean_B0_bet_mask_tmp.nii.gz"
dir_path = f"{base_dir}/003/dmri"
bvals = f"{dir_path}/sub-003_dwi.bval"
bvecs = f"{base_dir}/003/test_out/003/dwi/bvecs_reor.bvec"
gtab_file = f"{base_dir}/gtab.pkl"
gtab = gradient_table(bvals, bvecs)
gtab.b0_threshold = 50
gtab_bvals = gtab.bvals.copy()
b0_thr_ixs = np.where(gtab_bvals < gtab.b0_threshold)[0]
gtab_bvals[b0_thr_ixs] = 0
gtab.b0s_mask = gtab_bvals == 0
save_pickle(gtab_file, gtab)
fa_path = tens_mod_fa_est(gtab_file, dwi_file, B0_mask)[0]
coords = nodemaker.get_names_and_coords_of_parcels(uatlas)[0]
labels = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
# if dsn is True:
# os.makedirs(f"{dir_path}/dmri_reg/DSN", exist_ok=True)
# (streams_mni, dir_path, track_type, target_samples, conn_model, network, node_size, dens_thresh, ID, roi,
# min_span_tree, disp_filt, parc, prune, atlas, uatlas, labels, coords, norm, binary, atlas_mni, directget,
# warped_fa, min_length, error_margin) = register.direct_streamline_norm(streams, fa_path, fa_path, dir_path,
# track_type, target_samples, conn_model,
# network, node_size, dens_thresh, ID,
# roi, min_span_tree, disp_filt, parc,
# prune, atlas, atlas_dwi, uatlas,
# labels, coords, norm, binary, uatlas,
# dir_path, [0.1, 0.2], [40, 30],
# directget, min_length, t1_aligned_mni,
# error_margin)
#
# conn_matrix = streams2graph(atlas_mni, streams_mni, overlap_thr, dir_path, track_type, target_samples,
# conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
# disp_filt, parc, prune, atlas, uatlas, labels, coords, norm, binary,
# directget, warped_fa, error_margin, min_length, fa_wei)[2]
# else:
# conn_matrix = streams2graph(atlas_dwi, streams, overlap_thr, dir_path, track_type, target_samples,
# conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
# disp_filt, parc, prune, atlas, atlas_dwi, labels, coords, norm, binary,
# directget, fa_path, error_margin, min_length, fa_wei)[2]
conn_matrix = streams2graph(
atlas_dwi, streams, overlap_thr, dir_path, track_type, target_samples,
conn_model, network, node_size, dens_thresh, ID, roi, min_span_tree,
disp_filt, parc, prune, atlas, atlas_dwi, labels, coords, norm, binary,
directget, fa_path, error_margin, min_length, fa_wei)[2]
assert conn_matrix is not None
def test_nodemaker_tools_masking_parlistfile_WB():
"""
Test nodemaker_tools_masking_parlistfile_WB functionality
"""
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
dir_path = f"{base_dir}/BIDS/sub-0025427/ses-1/func"
parlistfile = f"{base_dir}/miscellaneous/whole_brain_cluster_labels_PCA200.nii.gz"
atlas = 'whole_brain_cluster_labels_PCA200'
roi = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
ID = '002'
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_labels = 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_labels,
dir_path, ID, perc_overlap)
print("%s%s%s" %
('parcel_masker (Masking whole-brain version) --> finished: ',
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: ',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
[WB_net_parcels_map_nifti_unmasked, WB_coords_unmasked, _, _, _,
dir_path] = nodemaker.node_gen(WB_coords, WB_parcel_list, WB_labels,
dir_path, ID, parc, atlas, parlistfile)
print("%s%s%s" % ('node_gen (Masking whole-brain version) --> finished: ',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
[
WB_net_parcels_map_nifti_masked, WB_coords_masked, WB_labels_masked, _,
_, _
] = nodemaker.node_gen_masking(roi, WB_coords, WB_parcel_list, WB_labels,
dir_path, ID, parc, atlas, parlistfile)
print("%s%s%s" %
('node_gen_masking (Masking whole-brain version) --> finished: ',
np.round(time.time() - start_time, 1), 's'))
assert WB_coords is not None
assert WB_labels is not None
assert WB_parcel_list is not None
assert WB_coords_masked is not None
assert WB_labels_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_parlistfile_RSN():
"""
Test nodemaker_tools_parlistfile_RSN functionality
"""
# Set example inputs
template = pkg_resources.resource_filename(
"pynets", f"templates/standard/MNI152_T1_brain_2mm.nii.gz")
dir_path = str(tempfile.TemporaryDirectory().name)
parlistfile = pkg_resources.resource_filename(
"pynets", "templates/atlases/whole_brain_cluster_labels_PCA200.nii.gz")
os.makedirs(dir_path, exist_ok=True)
shutil.copy2(parlistfile, f"{dir_path}/{os.path.basename(parlistfile)}")
parlistfile = f"{dir_path}/{os.path.basename(parlistfile)}"
subnet = "Default"
parc = True
start_time = time.time()
coords = nodemaker.get_names_and_coords_of_parcels(parlistfile)[0]
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()
parcels_4d_img = nodemaker.three_to_four_parcellation(parlistfile)
[net_coords, net_parcels_4d_img, net_labels, subnet] = \
nodemaker.get_node_membership(subnet, template, coords, labels,
parc, parcels_4d_img
)
print("%s%s%s" % (
"get_node_membership --> 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_parcels_4d_img)
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, subnet, net_labels,
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_parcels_4d_img 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 subnet is not None
def test_drop_coords_labels_from_restricted_parcellation():
from nipype.utils.filemanip import copyfile
parlistfile = pkg_resources.resource_filename(
"pynets", "templates/atlases/whole_brain_cluster_labels_PCA200.nii.gz")
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path, exist_ok=True)
shutil.copy2(parlistfile, f"{dir_path}/{os.path.basename(parlistfile)}")
parlistfile = f"{dir_path}/{os.path.basename(parlistfile)}"
[coords, _, _, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parlistfile
)
labels = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
labs = list(zip(labels, label_intensities))
[
parcellation_okay,
cleaned_coords,
cleaned_labels,
] = nodemaker.drop_coords_labels_from_restricted_parcellation(
parlistfile, coords, labs)
parcellation_okay_img = nib.load(parcellation_okay)
intensities_ok = list(
np.unique(np.asarray(parcellation_okay_img.dataobj).astype("int"))[1:])
assert len(cleaned_coords) == len(cleaned_labels) == len(intensities_ok)
parlist_img = nib.load(parlistfile)
parlist_img_data = parlist_img.get_fdata()
parlist_img_data[np.where(parlist_img_data == 10)] = 0
par_tmp = tempfile.NamedTemporaryFile(mode="w+", suffix=".nii.gz")
nib.save(nib.Nifti1Image(parlist_img_data, affine=parlist_img.affine),
par_tmp.name)
[
parcellation_okay,
cleaned_coords,
cleaned_labels,
] = nodemaker.drop_coords_labels_from_restricted_parcellation(
parlistfile, coords, labs)
parcellation_okay_img = nib.load(parcellation_okay)
intensities_ok = list(
np.unique(np.asarray(parcellation_okay_img.dataobj).astype("int"))[1:])
assert len(cleaned_coords) == len(cleaned_labels) == len(intensities_ok)
bad_coords = np.delete(coords, 30, axis=0)
del labs[-30]
par_tmp2 = tempfile.NamedTemporaryFile(mode="w+", suffix=".nii.gz").name
copyfile(parlistfile, par_tmp2, copy=True, use_hardlink=False)
[
parcellation_mod,
cleaned_coords,
cleaned_labels,
] = nodemaker.drop_coords_labels_from_restricted_parcellation(
par_tmp2, bad_coords, labs)
parcellation_mod_img = nib.load(parcellation_mod)
intensities_ok = list(
np.unique(np.asarray(parcellation_mod_img.dataobj).astype("int"))[1:])
assert len(cleaned_coords) == len(cleaned_labels) == len(intensities_ok)
def test_nodemaker_tools_masking_parlistfile_WB():
"""
Test nodemaker_tools_masking_parlistfile_WB functionality
"""
# Set example inputs
parlistfile = pkg_resources.resource_filename(
"pynets", "templates/atlases/whole_brain_cluster_labels_PCA200.nii.gz")
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path, exist_ok=True)
shutil.copy2(parlistfile, f"{dir_path}/{os.path.basename(parlistfile)}")
parlistfile = f"{dir_path}/{os.path.basename(parlistfile)}"
atlas = "whole_brain_cluster_labels_PCA200"
roi = tempfile.NamedTemporaryFile(mode="w+", suffix=".nii.gz").name
data_gen.generate_mni_space_img()[1].to_filename(roi)
ID = "002"
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_labels = np.arange(len(WB_coords) +
1)[np.arange(len(WB_coords) + 1) != 0].tolist()
start_time = time.time()
WB_parcel_list = nodemaker.three_to_four_parcellation(parlistfile)
start_time = time.time()
[
WB_net_parcels_map_nifti_unmasked,
WB_coords_unmasked,
_,
_,
_,
dir_path,
] = nodemaker.node_gen(WB_coords, WB_parcel_list, WB_labels, dir_path, ID,
parc, atlas, parlistfile)
print("%s%s%s" % (
"node_gen (Masking whole-brain version) --> finished: ",
np.round(time.time() - start_time, 1),
"s",
))
start_time = time.time()
WB_parcel_list = nodemaker.three_to_four_parcellation(parlistfile)
[WB_parcels_map_nifti,
parcel_list_exp] = nodemaker.create_parcel_atlas(WB_parcel_list)
print("%s%s%s" % (
"create_parcel_atlas (Masking whole-brain version) --> finished: ",
np.round(time.time() - start_time, 1),
"s",
))
start_time = time.time()
WB_parcel_list = nodemaker.three_to_four_parcellation(parlistfile)
[
WB_net_parcels_map_nifti_masked,
WB_coords_masked,
WB_labels_masked,
_,
_,
_,
] = nodemaker.node_gen_masking(
roi,
WB_coords,
WB_parcel_list,
WB_labels,
dir_path,
ID,
parc,
atlas,
parlistfile,
vox_size="2mm",
)
WB_parcel_list = nodemaker.three_to_four_parcellation(parlistfile)
WB_parcel_list_masked = nodemaker.parcel_masker(
roi,
WB_coords,
WB_parcel_list,
WB_labels,
dir_path,
ID,
perc_overlap,
vox_size="2mm",
)[2]
print("%s%s%s" % (
"parcel_masker (Masking whole-brain version) --> "
"finished: ",
np.round(time.time() - start_time, 1),
"s",
))
print("%s%s%s" % (
"node_gen_masking (Masking whole-brain version) --> "
"finished: ",
np.round(time.time() - start_time, 1),
"s",
))
assert WB_coords is not None
assert WB_labels is not None
assert WB_parcel_list is not None
assert WB_coords_masked is not None
assert WB_labels_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():
"""
Test nodemaker_tools_masking_parlistfile_RSN functionality
"""
# Set example inputs
template = pkg_resources.resource_filename(
"pynets", f"templates/standard/MNI152_T1_brain_2mm.nii.gz")
tmp = tempfile.TemporaryDirectory()
dir_path = str(tmp.name)
os.makedirs(dir_path, exist_ok=True)
parlistfile = pkg_resources.resource_filename(
"pynets", "templates/atlases/whole_brain_cluster_labels_PCA200.nii.gz")
shutil.copy2(parlistfile, f"{dir_path}/{os.path.basename(parlistfile)}")
parlistfile = f"{dir_path}/{os.path.basename(parlistfile)}"
roi = tempfile.NamedTemporaryFile(mode="w+", suffix=".nii.gz").name
data_gen.generate_mni_space_img()[1].to_filename(roi)
subnet = "Default"
ID = "002"
perc_overlap = 0.75
parc = True
start_time = time.time()
coords = nodemaker.get_names_and_coords_of_parcels(parlistfile)[0]
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()
parcels_4d_img = nodemaker.three_to_four_parcellation(parlistfile)
[net_coords, net_parcels_4d_img, net_labels, subnet] = \
nodemaker.get_node_membership(
subnet, template, coords, labels, parc, parcels_4d_img
)
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_parcels_4d_img_masked,
] = nodemaker.parcel_masker(
roi,
net_coords,
net_parcels_4d_img,
net_labels,
dir_path,
ID,
perc_overlap,
vox_size="2mm",
)
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_parcels_4d_img_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, subnet,
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_parcels_4d_img is not None
assert net_coords_masked is not None
assert net_labels_masked is not None
assert net_parcels_4d_img_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 subnet is not None
tmp.cleanup()
def _run_interface(self, runtime):
from pynets.core import utils, nodemaker
from nipype.utils.filemanip import fname_presuffix, copyfile
from nilearn.image import concat_imgs
import pandas as pd
import time
import textwrap
from pathlib import Path
import os.path as op
import glob
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"]
local_atlases = [
op.basename(i).split(".nii")[0]
for i in glob.glob(f"{str(Path(base_path).parent.parent)}"
f"/templates/atlases/*.nii.gz")
if "_4d" not in i
]
if self.inputs.parcellation is None and self.inputs.atlas in \
nilearn_parc_atlases:
[labels, networks_list, parcellation
] = nodemaker.nilearn_atlas_helper(self.inputs.atlas,
self.inputs.parc)
if parcellation:
if not isinstance(parcellation, str):
nib.save(
parcellation, f"{runtime.cwd}"
f"{self.inputs.atlas}{'.nii.gz'}")
parcellation = f"{runtime.cwd}" \
f"{self.inputs.atlas}{'.nii.gz'}"
if self.inputs.clustering is False:
[parcellation,
labels] = \
nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation, label_names=labels)
[coords, atlas, par_max, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parcellation)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
else:
raise FileNotFoundError(
f"\nAtlas file for {self.inputs.atlas} not found!")
atlas = self.inputs.atlas
elif (self.inputs.parcellation is None and self.inputs.parc is False
and self.inputs.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(self.inputs.atlas)
parcels_4d = None
par_max = None
atlas = self.inputs.atlas
parcellation = None
label_intensities = None
elif (self.inputs.parcellation is None and self.inputs.parc is False
and self.inputs.atlas in nilearn_prob_atlases):
import matplotlib
matplotlib.use("agg")
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, parcellation
] = nodemaker.nilearn_atlas_helper(self.inputs.atlas,
self.inputs.parc)
coords = find_probabilistic_atlas_cut_coords(maps_img=parcellation)
if parcellation:
if not isinstance(parcellation, str):
nib.save(
parcellation, f"{runtime.cwd}"
f"{self.inputs.atlas}{'.nii.gz'}")
parcellation = f"{runtime.cwd}" \
f"{self.inputs.atlas}{'.nii.gz'}"
if self.inputs.clustering is False:
[parcellation,
labels] = \
nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation, label_names=labels)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
else:
raise FileNotFoundError(
f"\nAtlas file for {self.inputs.atlas} not found!")
par_max = None
atlas = self.inputs.atlas
label_intensities = None
elif self.inputs.parcellation is None and self.inputs.atlas in \
local_atlases:
parcellation_pre = (
f"{str(Path(base_path).parent.parent)}/templates/atlases/"
f"{self.inputs.atlas}.nii.gz")
parcellation = fname_presuffix(parcellation_pre,
newpath=runtime.cwd)
copyfile(parcellation_pre,
parcellation,
copy=True,
use_hardlink=False)
try:
par_img = nib.load(parcellation)
except indexed_gzip.ZranError as e:
print(
e, "\nCannot load subnetwork reference image. "
"Do you have git-lfs installed?")
try:
if self.inputs.clustering is False:
[parcellation, _] = \
nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation)
# Fetch user-specified atlas coords
[coords, _, par_max, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parcellation)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
# Describe user atlas coords
print(f"\n{self.inputs.atlas} comes with {par_max} parcels\n")
except ValueError as e:
print(
e, "Either you have specified the name of an atlas that "
"does not exist in the nilearn or local repository or "
"you have not supplied a 3d atlas parcellation image!")
labels = None
networks_list = None
atlas = self.inputs.atlas
elif self.inputs.parcellation:
if self.inputs.clustering is True:
while True:
if op.isfile(self.inputs.parcellation):
break
else:
print("Waiting for atlas file...")
time.sleep(5)
try:
parcellation_tmp_path = fname_presuffix(
self.inputs.parcellation, newpath=runtime.cwd)
copyfile(self.inputs.parcellation,
parcellation_tmp_path,
copy=True,
use_hardlink=False)
# Fetch user-specified atlas coords
if self.inputs.clustering is False:
[parcellation,
_] = nodemaker.enforce_hem_distinct_consecutive_labels(
parcellation_tmp_path)
else:
parcellation = parcellation_tmp_path
[coords, atlas, par_max, label_intensities] = \
nodemaker.get_names_and_coords_of_parcels(parcellation)
if self.inputs.parc is True:
parcels_4d_img = nodemaker.three_to_four_parcellation(
parcellation)
else:
parcels_4d_img = None
atlas = utils.prune_suffices(atlas)
# Describe user atlas coords
print(f"\n{atlas} comes with {par_max} parcels\n")
except ValueError as e:
print(
e, "Either you have specified the name of an atlas that "
"does not exist in the nilearn or local repository or "
"you have not supplied a 3d atlas parcellation image!")
labels = None
networks_list = None
else:
raise ValueError(
"Either you have specified the name of an atlas that does"
" not exist in the nilearn or local repository or you have"
" not supplied a 3d atlas parcellation image!")
# Labels prep
if atlas and not labels:
if (self.inputs.ref_txt is not None) and (op.exists(
self.inputs.ref_txt)):
labels = pd.read_csv(self.inputs.ref_txt,
sep=" ",
header=None,
names=["Index",
"Region"])["Region"].tolist()
else:
if atlas in local_atlases:
ref_txt = (
f"{str(Path(base_path).parent.parent)}/templates/"
f"labels/"
f"{atlas}.txt")
else:
ref_txt = self.inputs.ref_txt
if ref_txt is not None:
try:
labels = pd.read_csv(ref_txt,
sep=" ",
header=None,
names=["Index", "Region"
])["Region"].tolist()
except BaseException:
if self.inputs.use_parcel_naming is True:
try:
labels = nodemaker.parcel_naming(
coords, self.inputs.vox_size)
except BaseException:
print("AAL reference labeling failed!")
labels = np.arange(len(coords) + 1)[
np.arange(len(coords) + 1) != 0].tolist()
else:
print("Using generic index labels...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
if self.inputs.use_parcel_naming is True:
try:
labels = nodemaker.parcel_naming(
coords, self.inputs.vox_size)
except BaseException:
print("AAL reference labeling failed!")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print("Using generic index labels...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
dir_path = utils.do_dir_path(atlas, self.inputs.outdir)
if len(coords) != len(labels):
labels = [
i for i in labels if (i != 'Unknown' and i != 'Background')
]
if len(coords) != len(labels):
print("Length of coordinates is not equal to length of "
"label names...")
if self.inputs.use_parcel_naming is True:
try:
print("Attempting consensus parcel naming instead...")
labels = nodemaker.parcel_naming(
coords, self.inputs.vox_size)
except BaseException:
print("Reverting to integer labels instead...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
else:
print("Reverting to integer labels instead...")
labels = np.arange(len(coords) +
1)[np.arange(len(coords) +
1) != 0].tolist()
print(f"Coordinates:\n{coords}")
print(f"Labels:\n"
f"{textwrap.shorten(str(labels), width=1000, placeholder='...')}"
f"")
assert len(coords) == len(labels)
if label_intensities is not None:
self._results["labels"] = list(zip(labels, label_intensities))
else:
self._results["labels"] = labels
self._results["coords"] = coords
self._results["atlas"] = atlas
self._results["networks_list"] = networks_list
# TODO: Optimize this with 4d array concatenation and .npyz
out_path = f"{runtime.cwd}/parcels_4d.nii.gz"
nib.save(parcels_4d_img, out_path)
self._results["parcels_4d"] = out_path
self._results["par_max"] = par_max
self._results["parcellation"] = parcellation
self._results["dir_path"] = dir_path
return runtime
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 : 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.core 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)) and (use_AAL_naming is False):
labels = pd.read_csv(ref_txt, sep=" ", header=None, names=["Index", "Region"])['Region'].tolist()
else:
try:
ref_txt = "%s%s%s%s" % (str(Path(base_path).parent), '/labelcharts/', atlas, '.txt')
if op.exists(ref_txt) and (use_AAL_naming is False):
try:
labels = pd.read_csv(ref_txt,
sep="\t", header=None, names=["Index", "Region"])['Region'].tolist()
except:
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 index 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 index 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))
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, networks_list, parcel_list, par_max, uatlas, dir_path
