def plot_conn_mat_struct(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names, roi, thr, node_size, smooth,
c_boot):
from pynets.plotting import plot_graphs
if roi:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
str(op.basename(roi).split('.')[0]), '_struct_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" %
('_', network, '_') if network else "_"), 'struct_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
plot_graphs.plot_conn_mat(conn_matrix, label_names, out_path_fig)
return
def plot_conn_mat_func(conn_matrix, conn_model, atlas, dir_path, ID, network, labels, roi, thr, node_size, smooth, c_boot, hpass):
"""
:param conn_matrix:
:param conn_model:
:param atlas:
:param dir_path:
:param ID:
:param network:
:param labels:
:param roi:
:param thr:
:param node_size:
:param smooth:
:param c_boot:
:param hpass:
:return:
"""
import networkx as nx
import os.path as op
import community
from pynets.plotting import plot_graphs
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (dir_path, '/', ID, '_', atlas,
'%s' % ("%s%s%s" % ('_', network, '_') if network else "_"),
'%s' % (op.basename(roi).split('.')[0] + '_' if roi is not None else ''),
'func_adj_mat_', conn_model, '_', thr, '_', node_size,
'%s' % ("mm_" if node_size != 'parc' else "_"),
'%s' % ("%s%s" % (int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'),
'%s' % ("%s%s" % (smooth, 'fwhm.png') if float(smooth) > 0 else ''),
'%s' % ("%s%s" % (hpass, 'Hz.png') if hpass is not None else '.png'))
plot_graphs.plot_conn_mat(conn_matrix, labels, out_path_fig)
# Plot community adj. matrix
G = nx.from_numpy_matrix(conn_matrix)
try:
node_comm_aff_mat = community.best_partition(G)
print("%s%s%s" % ('Found ', str(len(np.unique(node_comm_aff_mat))), ' communities...'))
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (dir_path, '/', ID, '_', atlas,
'%s' % ("%s%s%s" % ('_', network, '_') if network else "_"),
'%s' % (op.basename(roi).split('.')[0] + '_' if roi is not None else ''),
'func_adj_mat_comm_', conn_model, '_', thr, '_',
node_size, '%s' % ("mm_" if node_size != 'parc' else "_"),
'%s' % ("%s%s" % (int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'),
'%s' % ("%s%s" % (smooth, 'fwhm.png') if float(smooth) > 0 else ''),
'%s' % ("%s%s" % (hpass, 'Hz.png') if hpass is not None else '.png'))
plot_graphs.plot_community_conn_mat(conn_matrix, labels, out_path_fig_comm, node_comm_aff_mat)
except:
print('\nWARNING: Louvain community detection failed. Cannot plot community matrix...')
return
def test_plot_conn_mat_rois_gt_100():
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
conn_matrix = np.genfromtxt(
dir_path +
'/whole_brain_cluster_labels_PCA200/002_Default_est_sps_0.95.txt')
labels_file_path = dir_path + '/whole_brain_cluster_labels_PCA200/Default_func_labelnames_wb.pkl'
labels_file = open(labels_file_path, 'rb')
labels = pickle.load(labels_file)
out_path_fig = '/tmp/fig'
start_time = time.time()
plot_graphs.plot_conn_mat(conn_matrix, labels, out_path_fig)
print("%s%s%s" % ('plot_timeseries --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def test_plot_conn_mat_rois_gt_100(plotting_data):
"""
Test plot_conn_mat_rois_gt_100 functionality
"""
temp_dir = tempfile.TemporaryDirectory()
dir_path = str(temp_dir.name)
conn_matrix = plotting_data['conn_matrix']
labels = plotting_data['labels']
start_time = time.time()
plot_graphs.plot_conn_mat(conn_matrix, labels, dir_path, cmap='Blues')
print("%s%s%s" % ('plot_timeseries --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))
temp_dir.cleanup()
def test_plot_conn_mat_rois_gt_100():
"""
Test plot_conn_mat_rois_gt_100 functionality
"""
base_dir = str(Path(__file__).parent / "examples")
conn_matrix = np.genfromtxt(
f"{base_dir}/miscellaneous/002_rsn-Default_nodetype-parc_est-sps_thrtype-PROP_thr-0.94.txt"
)
labels_file_path = f"{base_dir}/miscellaneous/Default_func_labelnames_wb.pkl"
labels_file = open(labels_file_path, 'rb')
labels = pickle.load(labels_file)
out_path_fig = '/tmp/fig'
start_time = time.time()
plot_graphs.plot_conn_mat(conn_matrix, labels, out_path_fig, cmap='Blues')
print("%s%s%s" % ('plot_timeseries --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def plot_conn_mat_struct(conn_matrix, conn_model, atlas, dir_path, ID, network,
labels, roi, thr, node_size, target_samples,
track_type, directget, max_length):
"""
API for selecting among various structural connectivity matrix plotting approaches.
Parameters
----------
conn_matrix : array
NxN matrix.
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.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
thr : float
A value, between 0 and 1, 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.
target_samples : int
Total number of streamline samples specified to generate streams.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
max_length : int
Maximum fiber length threshold in mm to restrict tracking.
"""
from pynets.plotting import plot_graphs
import networkx as nx
import os.path as op
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_modality-dwi_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" % ('samples-', int(target_samples), 'streams_')
if float(target_samples) > 0 else '_'), 'tt-', track_type, '_dg-',
directget, '_ml-', max_length, '_thr-', thr, '_adj_mat.png')
plot_graphs.plot_conn_mat(conn_matrix, labels, out_path_fig)
# Plot community adj. matrix
try:
from pynets.stats.netstats import community_resolution_selection
G = nx.from_numpy_matrix(np.abs(conn_matrix))
_, node_comm_aff_mat, resolution, num_comms = community_resolution_selection(
G)
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_modality-dwi_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" % ('samples-', int(target_samples), 'streams_')
if float(target_samples) > 0 else '_'), 'tt-', track_type, '_dg-',
directget, '_ml-', max_length, '_thr-', thr, '_adj_mat_comm.png')
plot_graphs.plot_community_conn_mat(conn_matrix, labels,
out_path_fig_comm,
node_comm_aff_mat)
except:
print(
'\nWARNING: Louvain community detection failed. Cannot plot community matrix...'
)
return
def plot_conn_mat_func(conn_matrix, conn_model, atlas, dir_path, ID, network,
labels, roi, thr, node_size, smooth, c_boot, hpass):
"""
API for selecting among various functional connectivity matrix plotting approaches.
Parameters
----------
conn_matrix : array
NxN matrix.
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.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
thr : float
A value, between 0 and 1, 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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
c_boot : int
Number of bootstraps if user specified circular-block bootstrapped resampling of the node-extracted time-series.
hpass : bool
High-pass filter values (Hz) to apply to node-extracted time-series.
"""
import networkx as nx
import os.path as op
from pynets.plotting import plot_graphs
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_modality-func_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" %
('boot-', int(c_boot), 'iter_') if float(c_boot) > 0 else ''), "%s" %
("%s%s%s" %
('smooth-', smooth, 'fwhm_') if float(smooth) > 0 else ''), "%s" %
("%s%s%s" % ('hpass-', hpass, 'Hz_') if hpass is not None else ''),
'_thr-', thr, '_adj_mat.png')
plot_graphs.plot_conn_mat(conn_matrix, labels, out_path_fig)
# Plot community adj. matrix
try:
from pynets.stats.netstats import community_resolution_selection
G = nx.from_numpy_matrix(np.abs(conn_matrix))
_, node_comm_aff_mat, resolution, num_comms = community_resolution_selection(
G)
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_modality-func_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" %
('boot-', int(c_boot), 'iter_') if float(c_boot) > 0 else ''),
"%s" %
("%s%s%s" %
('smooth-', smooth, 'fwhm_') if float(smooth) > 0 else ''), "%s" %
("%s%s%s" % ('hpass-', hpass, 'Hz_') if hpass is not None else ''),
'_thr-', thr, '_adj_mat_comm.png')
plot_graphs.plot_community_conn_mat(conn_matrix, labels,
out_path_fig_comm,
node_comm_aff_mat)
except:
print(
'\nWARNING: Louvain community detection failed. Cannot plot community matrix...'
)
return
def plot_conn_mat_struct(conn_matrix, conn_model, atlas, dir_path, ID, network,
labels, roi, thr, node_size, target_samples,
track_type, directget, min_length, error_margin):
"""
API for selecting among various structural connectivity matrix plotting
approaches.
Parameters
----------
conn_matrix : array
NxN matrix.
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.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming
(e.g. 'Default') used to filter nodes in the study of brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
thr : float
A value, between 0 and 1, 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.
target_samples : int
Total number of streamline samples specified to generate streams.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
directget : str
The statistical approach to tracking. Options are:
det (deterministic), closest (clos), boot (bootstrapped), and prob
(probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
"""
import matplotlib.pyplot as plt
from pynets.core.utils import load_runconfig
import sys
from pynets.plotting import plot_graphs
import networkx as nx
import os.path as op
out_path_fig = \
"%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % \
(dir_path,
"/adjacency_",
ID,
"_modality-dwi_",
"%s" % ("%s%s%s" % ("rsn-",
network,
"_") if network is not None else ""),
"%s" % ("%s%s%s" % ("roi-",
op.basename(roi).split(".")[0],
"_") if roi is not None else ""),
"model-",
conn_model,
"_",
"%s" % ("%s%s%s" % ("nodetype-spheres-",
node_size,
"mm_") if (
(node_size != "parc") and (
node_size is not None)) else "nodetype-parc_"),
"%s" % ("%s%s%s" % ("samples-",
int(target_samples),
"streams_") if float(target_samples) > 0
else "_"),
"tracktype-",
track_type,
"_directget-",
directget,
"_minlength-",
min_length,
"_tol-",
error_margin,
"_thr-",
thr,
".png",
)
hardcoded_params = load_runconfig()
try:
cmap_name = hardcoded_params["plotting"]["structural"]["adjacency"][
"color_theme"][0]
except KeyError as e:
print(
e, "Plotting configuration not successfully extracted from"
" runconfig.yaml")
plot_graphs.plot_conn_mat(conn_matrix,
labels,
out_path_fig,
cmap=plt.get_cmap(cmap_name))
# Plot community adj. matrix
try:
from pynets.stats.netstats import community_resolution_selection
G = nx.from_numpy_matrix(np.abs(conn_matrix))
_, node_comm_aff_mat, resolution, num_comms = \
community_resolution_selection(G)
out_path_fig_comm = \
"%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" \
% (dir_path,
"/adjacency-communities_",
ID,
"_modality-dwi_",
"%s" % ("%s%s%s" % ("rsn-",
network,
"_") if network is not None else ""),
"%s" % ("%s%s%s" % ("roi-",
op.basename(roi).split(".")[0],
"_") if roi is not None else ""),
"model-",
conn_model,
"_",
"%s" % ("%s%s%s" % ("nodetype-spheres-",
node_size,
"mm_") if (
(node_size != "parc") and (
node_size is not None)) else "nodetype-parc_"),
"%s" % ("%s%s%s" % ("samples-",
int(target_samples),
"streams_") if float(target_samples) > 0
else "_"),
"tracktype-",
track_type,
"_directget-",
directget,
"_minlength-",
min_length,
"_tol-",
error_margin,
"_thr-",
thr,
".png",
)
plot_graphs.plot_community_conn_mat(
conn_matrix,
labels,
out_path_fig_comm,
node_comm_aff_mat,
cmap=plt.get_cmap(cmap_name),
)
except BaseException:
print("\nWARNING: Louvain community detection failed. Cannot plot"
" community matrix...")
return
def plot_conn_mat_func(
conn_matrix,
conn_model,
atlas,
dir_path,
ID,
network,
labels,
roi,
thr,
node_size,
smooth,
hpass,
extract_strategy,
):
"""
API for selecting among various functional connectivity matrix plotting
approaches.
Parameters
----------
conn_matrix : array
NxN matrix.
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.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g.
'Default') used to filter nodes in the study of brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
thr : float
A value, between 0 and 1, 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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting
signal from ROI's.
hpass : bool
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.
"""
import matplotlib.pyplot as plt
from pynets.core.utils import load_runconfig
import sys
import networkx as nx
import os.path as op
from pynets.plotting import plot_graphs
out_path_fig = \
"%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % \
(dir_path,
"/adjacency_",
ID,
"_modality-func_",
"%s" % ("%s%s%s" % ("rsn-",
network,
"_") if network is not None else ""),
"%s" % ("%s%s%s" % ("roi-",
op.basename(roi).split(".")[0],
"_") if roi is not None else ""),
"model-",
conn_model,
"_",
"%s" % ("%s%s%s" % ("nodetype-spheres-",
node_size,
"mm_") if (
(node_size != "parc") and (
node_size is not None)) else "nodetype-parc_"),
"%s" % ("%s%s%s" % ("smooth-",
smooth,
"fwhm_") if float(smooth) > 0 else ""),
"%s" % ("%s%s%s" % ("hpass-",
hpass,
"Hz_") if hpass is not None else ""),
"%s" % ("%s%s%s" % ("extract-",
extract_strategy,
"") if extract_strategy is not None else ""),
"_thr-",
thr,
".png",
)
hardcoded_params = load_runconfig()
try:
cmap_name = hardcoded_params["plotting"]["functional"]["adjacency"][
"color_theme"][0]
except KeyError as e:
print(
e, "Plotting configuration not successfully extracted from"
" runconfig.yaml")
plot_graphs.plot_conn_mat(conn_matrix,
labels,
out_path_fig,
cmap=plt.get_cmap(cmap_name))
# Plot community adj. matrix
try:
from pynets.stats.netstats import community_resolution_selection
G = nx.from_numpy_matrix(np.abs(conn_matrix))
_, node_comm_aff_mat, resolution, num_comms = \
community_resolution_selection(G)
out_path_fig_comm = \
"%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % \
(dir_path,
"/adjacency-communities_",
ID,
"_modality-func_",
"%s" % ("%s%s%s" % ("rsn-",
network,
"_") if network is not None else ""),
"%s" % ("%s%s%s" % ("roi-",
op.basename(roi).split(".")[0],
"_") if roi is not None else ""),
"model-",
conn_model,
"_",
"%s" % ("%s%s%s" % ("nodetype-spheres-",
node_size,
"mm_") if (
(node_size != "parc") and (
node_size is not None)) else "nodetype-parc_"),
"%s" % ("%s%s%s" % ("smooth-",
smooth,
"fwhm_") if float(smooth) > 0 else ""),
"%s" % ("%s%s%s" % ("hpass-",
hpass,
"Hz_") if hpass is not None else ""),
"%s" % ("%s%s%s" % ("extract-",
extract_strategy,
"") if extract_strategy is not None else ""),
"_thr-",
thr,
".png",
)
plot_graphs.plot_community_conn_mat(
conn_matrix,
labels,
out_path_fig_comm,
node_comm_aff_mat,
cmap=plt.get_cmap(cmap_name),
)
except BaseException:
print("\nWARNING: Louvain community detection failed. Cannot plot "
"community matrix...")
return
def plot_conn_mat_func(conn_matrix, conn_model, atlas, dir_path, ID, network,
labels, roi, thr, node_size, smooth, hpass,
extract_strategy):
"""
API for selecting among various functional connectivity matrix plotting approaches.
Parameters
----------
conn_matrix : array
NxN matrix.
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.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
thr : float
A value, between 0 and 1, 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.
smooth : int
Smoothing width (mm fwhm) to apply to time-series when extracting signal from ROI's.
hpass : bool
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.
"""
import matplotlib.pyplot as plt
import pkg_resources
import yaml
import sys
import networkx as nx
import os.path as op
from pynets.plotting import plot_graphs
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_modality-func_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" %
('smooth-', smooth, 'fwhm_') if float(smooth) > 0 else ''), "%s" %
("%s%s%s" %
('hpass-', hpass, 'Hz_') if hpass is not None else ''), "%s" %
("%s%s%s" % ('extract-', extract_strategy, '_') if extract_strategy
is not None else ''), '_thr-', thr, '_adj_mat.png')
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
'r') as stream:
hardcoded_params = yaml.load(stream)
try:
cmap_name = hardcoded_params['plotting']['functional'][
'adjacency']['color_theme'][0]
except KeyError:
print(
'ERROR: Plotting configuration not successfully extracted from runconfig.yaml'
)
sys.exit(0)
stream.close()
plot_graphs.plot_conn_mat(conn_matrix,
labels,
out_path_fig,
cmap=plt.get_cmap(cmap_name))
# Plot community adj. matrix
try:
from pynets.stats.netstats import community_resolution_selection
G = nx.from_numpy_matrix(np.abs(conn_matrix))
_, node_comm_aff_mat, resolution, num_comms = community_resolution_selection(
G)
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_modality-func_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" %
('smooth-', smooth, 'fwhm_') if float(smooth) > 0 else ''), "%s" %
("%s%s%s" %
('hpass-', hpass, 'Hz_') if hpass is not None else ''), "%s" %
("%s%s%s" % ('extract-', extract_strategy, '_') if extract_strategy
is not None else ''), '_thr-', thr, '_adj_mat_comm.png')
plot_graphs.plot_community_conn_mat(conn_matrix,
labels,
out_path_fig_comm,
node_comm_aff_mat,
cmap=plt.get_cmap(cmap_name))
except:
print(
'\nWARNING: Louvain community detection failed. Cannot plot community matrix...'
)
return
def plot_conn_mat_func(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names, roi, thr, node_size, smooth,
c_boot):
import networkx as nx
import os.path as op
from pynets.plotting import plot_graphs
from pynets.stats.netstats import modularity_louvain_und_sign
if roi:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
str(op.basename(roi).split('.')[0]), '_func_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
str(op.basename(roi).split('.')[0]), '_func_adj_mat_communities_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" %
('_', network, '_') if network else "_"), 'func_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
'func_adj_mat_communities_', str(conn_model), '_', str(thr), '_',
str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
plot_graphs.plot_conn_mat(conn_matrix, label_names, out_path_fig)
# Plot community adj. matrix
gamma = nx.density(nx.from_numpy_array(conn_matrix))
try:
if network or len(conn_matrix) < 100:
[node_comm_aff_mat,
q] = modularity_louvain_und_sign(conn_matrix,
gamma=float(gamma * 0.001))
else:
[node_comm_aff_mat,
q] = modularity_louvain_und_sign(conn_matrix,
gamma=float(gamma * 0.01))
print("%s%s%s%s%s" % ('Found ', str(len(np.unique(node_comm_aff_mat))),
' communities using γ=', str(gamma), '...'))
plot_graphs.plot_community_conn_mat(conn_matrix, label_names,
out_path_fig_comm,
node_comm_aff_mat)
except:
print(
'\nWARNING: Louvain community detection failed. Cannot plot community matrix...'
)
return
