def test_create_est_path_func():
"""
Test create_est_path_diff functionality
"""
base_dir = str(Path(__file__).parent/"examples")
dir_path = base_dir + '/002/fmri'
network = 'Default'
ID = '002'
models = ['corr', 'cov', 'sps', 'partcorr']
roi = None
node_size = 6
smooth = 6
c_boot = 1000
hpass = 100
parc = True
# Cross test various connectivity models, thresholds, and parc true/false.
for conn_model in models:
for val in range(1, 10):
thr = round(val*0.1, 1)
for thr_type in ['prop', 'abs', 'dens', 'mst', 'disp']:
for parc in [True, False]:
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi, dir_path, node_size,
smooth, c_boot,
thr_type, hpass, parc)
assert est_path is not None
def test_create_csv_path():
"""
Test create_csv_path functionality
"""
import tempfile
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path)
# fmri case
network = 'Default'
ID = '002'
conn_model = 'corr'
roi = None
node_size = 6
smooth = 6
hpass = 100
parc = True
thr = 0.75
thr_type = 'prop'
extract_strategy = 'mean'
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi,
dir_path, node_size, smooth,
thr_type, hpass, parc,
extract_strategy)
out_path = utils.create_csv_path(dir_path, est_path)
assert out_path is not None
def test_create_est_path_func(node_size, hpass, smooth, parc):
"""
Test create_est_path_diff functionality
"""
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
network = 'Default'
ID = '002'
conn_model = 'corr'
roi = None
c_boot = 1000
thr_type = 'prop'
thr = 0.75
# Delete pre-existing graphs dir
graph_dir = dir_path + '/graphs'
if os.path.exists(graph_dir):
shutil.move(graph_dir, graph_dir + '_tmp')
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi,
dir_path, node_size, smooth, c_boot,
thr_type, hpass, parc)
assert est_path is not None
# Restore pre-existing graph dir
if os.path.exists(graph_dir + '_tmp'):
shutil.rmtree(graph_dir)
shutil.move(graph_dir + '_tmp', graph_dir)
def test_create_csv_path():
"""
Test create_csv_path functionality
"""
import tempfile
tmp = tempfile.TemporaryDirectory()
dir_path = str(tmp.name)
os.makedirs(dir_path, exist_ok=True)
# fmri case
subnet = 'Default'
ID = '002'
conn_model = 'corr'
roi = None
node_size = 6
smooth = 6
hpass = 100
parc = True
thr = 0.75
thr_type = 'prop'
signal = 'mean'
est_path = utils.create_est_path_func(ID, subnet, conn_model, thr, roi,
dir_path, node_size, smooth,
thr_type, hpass, parc, signal)
out_path = utils.create_csv_path(dir_path, est_path)
assert out_path is not None
tmp.cleanup()
def test_create_csv_path():
"""
Test create_csv_path functionality
"""
base_dir = str(Path(__file__).parent/"examples")
# fmri case
dir_path = base_dir + '/002/fmri'
network = 'Default'
ID = '002'
models = ['corr', 'cov', 'sps', 'partcorr']
roi = None
node_size = 6
smooth = 6
c_boot = 100
hpass = 100
parc = True
directget = 'prob'
max_length = 200
# Cross test various connectivity models, thresholds, and parc true/false.
for conn_model in models:
for val in range(1, 10):
thr = round(val*0.1, 1)
for thr_type in ['prop', 'abs', 'dens', 'mst', 'disp']:
for parc in [True, False]:
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi, dir_path, node_size,
smooth, c_boot,
thr_type, hpass, parc)
out_path = utils.create_csv_path(dir_path, est_path)
assert out_path is not None
dir_path = base_dir + '/002/dmri'
network = 'Default'
ID = '002'
models = ['corr', 'cov', 'sps', 'partcorr']
roi = None
node_size = 6
for conn_model in models:
for val in range(1, 10):
thr = round(val*0.1, 1)
for thr_type in ['prop', 'abs', 'dens', 'mst', 'disp']:
for target_samples in range(0, 100, 1000):
for track_type in ['local', 'particle']:
for parc in [True, False]:
est_path = utils.create_est_path_diff(ID, network, conn_model, thr, roi,
dir_path, node_size, target_samples,
track_type, thr_type, parc, directget, max_length)
out_path = utils.create_csv_path(dir_path, est_path)
assert out_path is not None
def test_create_csv_path():
"""
Test create_csv_path functionality
"""
base_dir = str(Path(__file__).parent / "examples")
# fmri case
dir_path = base_dir + '/002/fmri'
network = 'Default'
ID = '002'
conn_model = 'corr'
roi = None
node_size = 6
smooth = 6
c_boot = 100
hpass = 100
parc = True
thr = 0.75
thr_type = 'prop'
# Delete pre-existing graphs dir
graph_dir = dir_path + '/graphs'
if os.path.exists(graph_dir):
shutil.move(graph_dir, graph_dir + '_tmp')
# Delete pre-existing netmetrics dir
netmetrics_dir = str(Path(dir_path).parent) + '/netmetrics'
if os.path.exists(netmetrics_dir):
shutil.move(netmetrics_dir, netmetrics_dir + '_tmp')
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi,
dir_path, node_size, smooth, c_boot,
thr_type, hpass, parc)
out_path = utils.create_csv_path(dir_path, est_path)
assert out_path is not None
# Restore pre-existing graph dir
if os.path.exists(graph_dir + '_tmp'):
shutil.rmtree(graph_dir)
shutil.move(graph_dir + '_tmp', graph_dir)
if os.path.exists(netmetrics_dir + '_tmp'):
shutil.rmtree(netmetrics_dir)
shutil.move(netmetrics_dir + '_tmp', netmetrics_dir)
def test_create_est_path_func(node_size, hpass, smooth, parc):
"""
Test create_est_path_diff functionality
"""
import tempfile
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path)
network = 'Default'
ID = '002'
conn_model = 'corr'
roi = None
thr_type = 'prop'
thr = 0.75
extract_strategy = 'mean'
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi,
dir_path, node_size, smooth,
thr_type, hpass, parc,
extract_strategy)
assert est_path is not None
def test_create_est_path_func(node_size, hpass, smooth, parc):
"""
Test create_est_path_diff functionality
"""
import tempfile
tmp = tempfile.TemporaryDirectory()
dir_path = str(tmp.name)
os.makedirs(dir_path, exist_ok=True)
subnet = 'Default'
ID = '002'
conn_model = 'corr'
roi = None
thr_type = 'prop'
thr = 0.75
signal = 'mean'
est_path = utils.create_est_path_func(ID, subnet, conn_model, thr, roi,
dir_path, node_size, smooth,
thr_type, hpass, parc, signal)
assert est_path is not None
tmp.cleanup()
def test_pass_meta_outs(embed_multimodal_multiplex):
"""
Test pass_meta_outs functionality
Note: omni argument may be failing due to functions in netmotifs or due to
an unexpected input. Marked for xfail and should be updated after tests for
netmotifs are created.
"""
base_dir = str(Path(__file__).parent / "examples")
dmri_path = base_dir + '/002/dmri'
func_path = base_dir + '/002/fmri'
conn_model_func = 'corr'
conn_model_struct = 'csa'
network_func = 'Default'
network_struct = 'Default'
thr_func = 0.6
thr_struct = 0.8
prune_func = True
prune_struct = False
ID_func = '002'
ID_struct = '002'
roi_func = func_path + '/pDMN_3_bin_mask.nii.gz'
roi_struct = dmri_path + '/pDMN_3_bin_mask.nii.gz' # this doesn't exists
norm_func = 1
norm_struct = 2
binary_func = False
binary_struct = True
embed = embed_multimodal_multiplex[0]
multimodal = embed_multimodal_multiplex[1]
multiplex = embed_multimodal_multiplex[2]
node_size = 6
smooth = 6
c_boot = 1000
thr_type = 'prop'
hpass = 100
parc = True
directget = 'prob'
max_length = 200
thr_type = 'prop'
target_samples = 10
track_type = 'local'
conn_matrix_diff = np.random.rand(10, 10)
conn_matrix_func = np.random.rand(10, 10)
est_path_func = utils.create_est_path_func(ID_func, network_func,
conn_model_func, thr_func,
roi_func, func_path, node_size,
smooth, c_boot, thr_type, hpass,
parc)
est_path_diff = utils.create_est_path_diff(ID_struct, network_struct,
conn_model_struct, thr_struct,
roi_struct, dmri_path,
node_size, target_samples,
track_type, thr_type, parc,
directget, max_length)
utils.save_mat(conn_matrix_diff, est_path_diff)
utils.save_mat(conn_matrix_func, est_path_func)
[
conn_model_iterlist, est_path_iterlist, network_iterlist, thr_iterlist,
prune_iterlist, ID_iterlist, roi_iterlist, norm_iterlist,
binary_iterlist
] = utils.pass_meta_ins_multi(conn_model_func, est_path_func, network_func,
thr_func, prune_func, ID_func, roi_func,
norm_func, binary_func, conn_model_struct,
est_path_diff, network_struct, thr_struct,
prune_struct, ID_struct, roi_struct,
norm_struct, binary_struct)
[
conn_model_iterlist_out, est_path_iterlist_out, network_iterlist_out,
thr_iterlist_out, prune_iterlist_out, ID_iterlist_out,
roi_iterlist_out, norm_iterlist_out, binary_iterlist_out
] = utils.pass_meta_outs(conn_model_iterlist, est_path_iterlist,
network_iterlist, thr_iterlist, prune_iterlist,
ID_iterlist, roi_iterlist, norm_iterlist,
binary_iterlist, embed, multimodal, multiplex)
assert conn_model_iterlist_out is not None
assert est_path_iterlist_out is not None
assert network_iterlist_out is not None
assert thr_iterlist_out is not None
assert prune_iterlist_out is not None
assert ID_iterlist_out is not None
assert roi_iterlist_out is not None
assert norm_iterlist_out is not None
assert binary_iterlist_out is not None
def thresh_func(dens_thresh, thr, conn_matrix, conn_model, network, ID,
dir_path, roi, node_size, min_span_tree, smooth, disp_filt,
parc, prune, atlas, uatlas, labels, coords, c_boot, norm,
binary, hpass):
"""
Threshold a functional connectivity matrix using any of a variety of methods.
Parameters
----------
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis for thresholding.
thr : float
A value, between 0 and 1, to threshold the graph using any variety of methods
triggered through other options.
conn_matrix : array
Adjacency matrix stored as an m x n array of nodes and edges.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for covariance, sps for precision covariance,
partcorr for partial correlation). sps type is used by default.
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.
ID : str
A subject id or other unique identifier.
dir_path : str
Path to directory containing subject derivative data for given run.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
Returns
-------
conn_matrix_thr : array
Weighted, thresholded, NxN matrix.
edge_threshold : str
The string percentage representation of thr.
est_path : str
File path to the thresholded graph, conn_matrix_thr, saved as a numpy array in .npy format.
thr : float
The value, between 0 and 1, used to threshold the graph using any variety of methods
triggered through other options.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
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.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for covariance, sps for precision covariance,
partcorr for partial correlation). sps type is used by default.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
ID : str
A subject id or other unique identifier.
dir_path : str
Path to directory containing subject derivative data for given run.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
"""
import gc
from pynets.core import utils, thresholding
thr_perc = 100 - np.abs(100 * float(thr))
if parc is True:
node_size = 'parc'
if np.count_nonzero(conn_matrix) == 0:
raise ValueError('ERROR: Raw connectivity matrix contains only zeros.')
# Save unthresholded
utils.save_mat(
conn_matrix,
utils.create_raw_path_func(ID, network, conn_model, roi, dir_path,
node_size, smooth, c_boot, hpass, parc))
[thr_type, edge_threshold, conn_matrix_thr, coords,
labels] = thresholding.perform_thresholding(conn_matrix, coords, labels,
thr, thr_perc, min_span_tree,
dens_thresh, disp_filt)
if not nx.is_connected(nx.from_numpy_matrix(conn_matrix_thr)):
print('Warning: Fragmented graph')
# Save thresholded mat
est_path = utils.create_est_path_func(ID, network, conn_model, thr, roi,
dir_path, node_size, smooth, c_boot,
thr_type, hpass, parc)
utils.save_mat(conn_matrix_thr, est_path)
gc.collect()
return (conn_matrix_thr, edge_threshold, est_path, thr, node_size, network,
conn_model, roi, smooth, prune, ID, dir_path, atlas, uatlas,
labels, coords, c_boot, norm, binary, hpass)
def thresh_func(
dens_thresh,
thr,
conn_matrix,
conn_model,
network,
ID,
dir_path,
roi,
node_size,
min_span_tree,
smooth,
disp_filt,
parc,
prune,
atlas,
uatlas,
labels,
coords,
norm,
binary,
hpass,
extract_strategy,
check_consistency=True,
):
"""
Threshold a functional connectivity matrix using any of a variety of
methods.
Parameters
----------
dens_thresh : bool
Indicates whether a target graph density is to be used as the basis
for thresholding.
thr : float
A value, between 0 and 1, to threshold the graph using any variety of
methods triggered through other options.
conn_matrix : array
Adjacency matrix stored as an m x n array of nodes and edges.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for
covariance, sps for precision covariance, partcorr for partial
correlation). sps type is used by default.
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.
ID : str
A subject id or other unique identifier.
dir_path : str
Path to directory containing subject derivative data for given run.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
node_size : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's.
min_span_tree : bool
Indicates whether local thresholding from the Minimum Spanning Tree
should be used.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting
signal from ROI's.
disp_filt : bool
Indicates whether local thresholding using a disparity filter and
'backbone network' should be used.
parc : bool
Indicates whether to use parcels instead of coordinates as ROI nodes.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
extract_strategy : str
The name of a valid function used to reduce the time-series region
extraction.
Returns
-------
conn_matrix_thr : array
Weighted, thresholded, NxN matrix.
edge_threshold : str
The string percentage representation of thr.
est_path : str
File path to the thresholded graph, conn_matrix_thr, saved as a numpy
array in .npy format.
thr : float
The value, between 0 and 1, used to threshold the graph using any
variety of methods triggered through other options.
node_size : int
Spherical centroid node size in the case that coordinate-based
centroids are used as ROI's.
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.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for
covariance, sps for precision covariance, partcorr for partial
correlation). sps type is used by default.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting
signal from ROI's.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
ID : str
A subject id or other unique identifier.
dir_path : str
Path to directory containing subject derivative data for given run.
atlas : str
Name of atlas parcellation used.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
labels : list
List of string labels corresponding to ROI nodes.
coords : list
List of (x, y, z) tuples corresponding to a coordinate atlas used or
which represent the center-of-mass of each parcellation node.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
hpass : float
High-pass filter values (Hz) to apply to node-extracted time-series.
extract_strategy : str
The name of a valid function used to reduce the time-series region
extraction.
References
----------
.. [1] van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010).
Comparing brain networks of different size and connectivity
density using graph theory. PLoS ONE.
https://doi.org/10.1371/journal.pone.0013701
"""
import gc
from pynets.core import utils, thresholding
if np.count_nonzero(conn_matrix) == 0:
print(UserWarning("Raw connectivity matrix contains only" " zeros."))
[thr_type, edge_threshold, conn_matrix_thr] = \
thresholding.perform_thresholding(
conn_matrix, thr, min_span_tree, dens_thresh, disp_filt)
if not nx.is_connected(nx.from_numpy_matrix(conn_matrix_thr)):
print("Warning: Fragmented graph")
# Save thresholded mat
est_path = utils.create_est_path_func(
ID,
network,
conn_model,
thr,
roi,
dir_path,
node_size,
smooth,
thr_type,
hpass,
parc,
extract_strategy,
)
utils.save_mat(conn_matrix_thr, est_path)
gc.collect()
if check_consistency is True:
assert len(coords) == len(labels) == conn_matrix_thr.shape[0]
if network is not None:
atlas_name = f"{atlas}_{network}_stage-post_thr"
else:
atlas_name = f"{atlas}_stage-post_thr"
utils.save_coords_and_labels_to_json(coords,
labels,
dir_path,
atlas_name,
indices=None)
return (
edge_threshold,
est_path,
thr,
node_size,
network,
conn_model,
roi,
smooth,
prune,
ID,
dir_path,
atlas,
uatlas,
labels,
coords,
norm,
binary,
hpass,
extract_strategy,
)
