def test_plot_conn_mat_nonet_mask(plotting_data):
"""
Test plot_conn_mat_nonet_mask functionality
"""
temp_dir = tempfile.TemporaryDirectory()
dir_path = str(temp_dir.name)
network = None
ID = '002'
thr = 0.95
node_size = 2
smooth = 2
hpass = 0.1
conn_model = 'sps'
extract_strategy = 'mean'
atlas = 'whole_brain_cluster_labels_PCA200'
roi = None
conn_matrix = plotting_data['conn_matrix']
labels = plotting_data['labels']
start_time = time.time()
plot_graphs.plot_conn_mat_func(conn_matrix, conn_model, atlas, dir_path,
ID, network, labels, roi, thr, node_size, smooth, hpass, extract_strategy)
print("%s%s%s" % ('plot_conn_mat_func (Masking version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
temp_dir.cleanup()
def test_plot_conn_mat_nonet_mask():
"""
Test plot_conn_mat_nonet_mask functionality
"""
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
network = None
ID = '002'
thr = 0.95
node_size = 2
smooth = 2
c_boot = 3
hpass = 0.1
conn_model = 'sps'
atlas = 'whole_brain_cluster_labels_PCA200'
roi = None
conn_matrix = np.genfromtxt(
dir_path +
'/whole_brain_cluster_labels_PCA200/002_Default_est_sps_0.94.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)
start_time = time.time()
plot_graphs.plot_conn_mat_func(conn_matrix, conn_model, atlas, dir_path,
ID, network, labels, roi, thr, node_size,
smooth, c_boot, hpass)
print("%s%s%s" % ('plot_conn_mat_func (Masking version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def test_plot_conn_mat_nonet_mask():
"""
Test plot_conn_mat_nonet_mask functionality
"""
import tempfile
base_dir = str(Path(__file__).parent / "examples")
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path)
network = None
ID = '002'
thr = 0.95
node_size = 2
smooth = 2
hpass = 0.1
conn_model = 'sps'
extract_strategy = 'mean'
atlas = 'whole_brain_cluster_labels_PCA200'
roi = None
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)
start_time = time.time()
plot_graphs.plot_conn_mat_func(conn_matrix, conn_model, atlas, dir_path,
ID, network, labels, roi, thr, node_size,
smooth, hpass, extract_strategy)
print("%s%s%s" % ('plot_conn_mat_func (Masking version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def plot_all_func(conn_matrix, conn_model, atlas, dir_path, ID, network, labels, roi, coords, thr,
node_size, edge_threshold, smooth, prune, uatlas, c_boot, norm, binary, hpass):
"""
Plot adjacency matrix, connectogram, and glass brain for functional connectome.
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.
coords : list
List of (x, y, z) tuples corresponding to an a-priori defined set (e.g. a coordinate atlas).
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.
edge_threshold : float
The actual value, between 0 and 1, that the graph was thresholded (can differ from thr if target was not
successfully obtained.
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.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
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 : bool
High-pass filter values (Hz) to apply to node-extracted time-series.
"""
import os
import os.path as op
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
from nilearn import plotting as niplot
import pkg_resources
import networkx as nx
from pynets.core import thresholding
from pynets.plotting import plot_gen, plot_graphs
from pynets.stats.netstats import most_important, prune_disconnected
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
ch2better_loc = pkg_resources.resource_filename("pynets", "templates/ch2better.nii.gz")
coords = list(coords)
labels = list(labels)
if len(coords) > 0:
dpi_resolution = 500
if '\'b' in atlas:
atlas = atlas.decode('utf-8')
if (prune == 1 or prune == 2) and len(coords) == conn_matrix.shape[0]:
G_pre = nx.from_numpy_matrix(np.abs(conn_matrix))
if prune == 1:
[G, pruned_nodes] = prune_disconnected(G_pre)
elif prune == 2:
[G, pruned_nodes] = most_important(G_pre)
else:
G = G_pre
pruned_nodes = []
pruned_nodes.sort(reverse=True)
print('(Display)')
coords_pre = list(coords)
labels_pre = list(labels)
if len(pruned_nodes) > 0:
for j in pruned_nodes:
labels_pre.pop(j)
coords_pre.pop(j)
conn_matrix = nx.to_numpy_array(G)
labels = labels_pre
coords = coords_pre
else:
print('No nodes to prune for plot...')
coords = list(tuple(x) for x in coords)
namer_dir = dir_path + '/figures'
if not os.path.isdir(namer_dir):
os.makedirs(namer_dir, exist_ok=True)
# Plot connectogram
if len(conn_matrix) > 20:
try:
plot_gen.plot_connectogram(conn_matrix, conn_model, atlas, namer_dir, ID, network, labels)
except RuntimeWarning:
print('\n\n\nWarning: Connectogram plotting failed!')
else:
print('Warning: Cannot plot connectogram for graphs smaller than 20 x 20!')
# Plot adj. matrix based on determined inputs
if not node_size or node_size == 'None':
node_size = 'parc'
plot_graphs.plot_conn_mat_func(conn_matrix, conn_model, atlas, namer_dir, ID, network, labels, roi, thr,
node_size, smooth, c_boot, hpass)
# Plot connectome
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (namer_dir, '/', 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, '_glass_viz.png')
if roi:
# Save coords to pickle
coord_path = "%s%s%s%s" % (namer_dir, '/coords_', op.basename(roi).split('.')[0], '_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = "%s%s%s%s" % (namer_dir, '/labelnames_', op.basename(roi).split('.')[0], '_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
else:
# Save coords to pickle
coord_path = "%s%s" % (namer_dir, '/coords_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = "%s%s" % (namer_dir, '/labelnames_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)), [(0, 0, 0)], node_size=0.0001, black_bg=True)
connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
#connectome.add_overlay(ch2better_loc, alpha=0.35, cmap=plt.cm.gray)
conn_matrix = np.array(np.array(thresholding.autofix(conn_matrix)))
[z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
if node_size == 'parc':
node_size_plot = int(6)
else:
node_size_plot = int(node_size)
if len(coords) != conn_matrix.shape[0]:
raise RuntimeWarning('\nWARNING: Number of coordinates does not match conn_matrix dimensions. If you are '
'using disparity filtering, try relaxing the α threshold.')
else:
color_theme = 'Blues'
#color_theme = 'Greens'
#color_theme = 'Reds'
node_color = 'auto'
connectome.add_graph(conn_matrix, coords, edge_threshold=edge_threshold, edge_cmap=color_theme,
edge_vmax=float(z_max), edge_vmin=float(z_min), node_size=node_size_plot,
node_color='auto')
connectome.savefig(out_path_fig, dpi=dpi_resolution)
else:
raise RuntimeError('\nERROR: no coordinates to plot! Are you running plotting outside of pynets\'s internal '
'estimation schemes?')
plt.close('all')
return
def plot_all(conn_matrix, conn_model, atlas, dir_path, ID, network, labels,
roi, coords, thr, node_size, edge_threshold, smooth, prune,
uatlas, c_boot, norm, binary, hpass):
"""
:param conn_matrix:
:param conn_model:
:param atlas:
:param dir_path:
:param ID:
:param network:
:param labels:
:param roi:
:param coords:
:param thr:
:param node_size:
:param edge_threshold:
:param smooth:
:param prune:
:param uatlas:
:param c_boot:
:param norm:
:param binary:
:param hpass:
:return:
"""
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
from nilearn import plotting as niplot
import pkg_resources
import networkx as nx
from pynets import plotting, thresholding
from pynets.plotting import plot_gen, plot_graphs
from pynets.stats.netstats import most_important, prune_disconnected
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
coords = list(coords)
labels = list(labels)
if len(coords) > 0:
dpi_resolution = 500
if '\'b' in atlas:
atlas = atlas.decode('utf-8')
if (prune == 1 or prune == 2) and len(coords) == conn_matrix.shape[0]:
G_pre = nx.from_numpy_matrix(conn_matrix)
if prune == 1:
[G, pruned_nodes] = prune_disconnected(G_pre)
elif prune == 2:
[G, pruned_nodes] = most_important(G_pre)
else:
G = G_pre
pruned_nodes = []
pruned_nodes.sort(reverse=True)
print('(Display)')
coords_pre = list(coords)
labels_pre = list(labels)
if len(pruned_nodes) > 0:
for j in pruned_nodes:
labels_pre.pop(j)
coords_pre.pop(j)
conn_matrix = nx.to_numpy_array(G)
labels = labels_pre
coords = coords_pre
else:
print('No nodes to prune for plot...')
coords = list(tuple(x) for x in coords)
# Plot connectogram
if len(conn_matrix) > 20:
try:
plot_gen.plot_connectogram(conn_matrix, conn_model, atlas,
dir_path, ID, network, labels)
except RuntimeWarning:
print('\n\n\nWarning: Connectogram plotting failed!')
else:
print(
'Warning: Cannot plot connectogram for graphs smaller than 20 x 20!'
)
# Plot adj. matrix based on determined inputs
if not node_size or node_size == 'None':
node_size = 'parc'
plot_graphs.plot_conn_mat_func(conn_matrix, conn_model, atlas,
dir_path, ID, network, labels, roi, thr,
node_size, smooth, c_boot, hpass)
# Plot connectome
if roi:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', atlas, '_', conn_model, '_',
op.basename(roi).split('.')[0], "%s" %
("%s%s%s" %
('_', network, '_') if network else "_"), 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_') if float(smooth) > 0 else ''), "%s" %
("%s%s" % (hpass, 'Hz_') if hpass is not None else ''),
'func_glass_viz.png')
# Save coords to pickle
coord_path = "%s%s%s%s" % (dir_path, '/coords_',
op.basename(roi).split('.')[0],
'_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = "%s%s%s%s" % (dir_path, '/labelnames_',
op.basename(roi).split('.')[0],
'_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', atlas, '_', conn_model, "%s" %
("%s%s%s" %
('_', network, '_') if network else "_"), 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_') if float(smooth) > 0 else ''), "%s" %
("%s%s" % (hpass, 'Hz_') if hpass is not None else ''),
'func_glass_viz.png')
# Save coords to pickle
coord_path = "%s%s" % (dir_path, '/coords_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = "%s%s" % (dir_path, '/labelnames_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
ch2better_loc = pkg_resources.resource_filename(
"pynets", "templates/ch2better.nii.gz")
connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)),
[(0, 0, 0)],
node_size=0.0001,
black_bg=True)
connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
#connectome.add_overlay(ch2better_loc, alpha=0.35, cmap=plt.cm.gray)
conn_matrix = np.array(np.array(thresholding.autofix(conn_matrix)))
[z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
if node_size == 'parc':
node_size_plot = int(2)
if uatlas:
connectome.add_contours(uatlas,
filled=True,
alpha=0.20,
cmap=plt.cm.gist_rainbow)
else:
node_size_plot = int(node_size)
if len(coords) != conn_matrix.shape[0]:
raise RuntimeWarning(
'\nWARNING: Number of coordinates does not match conn_matrix dimensions. If you are '
'using disparity filtering, try relaxing the α threshold.')
else:
color_theme = 'Blues'
#color_theme = 'Greens'
#color_theme = 'Reds'
node_color = 'auto'
connectome.add_graph(conn_matrix,
coords,
edge_threshold=edge_threshold,
edge_cmap=color_theme,
edge_vmax=float(z_max),
edge_vmin=float(z_min),
node_size=node_size_plot,
node_color='auto')
connectome.savefig(out_path_fig, dpi=dpi_resolution)
else:
raise RuntimeError(
'\nERROR: no coordinates to plot! Are you running plotting outside of pynets\'s internal '
'estimation schemes?')
return
def plot_all_func(conn_matrix,
conn_model,
atlas,
dir_path,
ID,
network,
labels,
roi,
coords,
thr,
node_size,
edge_threshold,
smooth,
prune,
uatlas,
norm,
binary,
hpass,
extract_strategy,
edge_color_override=False):
"""
Plot adjacency matrix, connectogram, and glass brain for functional connectome.
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.
coords : list
List of (x, y, z) tuples corresponding to an a-priori defined set (e.g. a coordinate atlas).
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.
edge_threshold : float
The actual value, between 0 and 1, that the graph was thresholded (can differ from thr if target was not
successfully obtained.
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.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
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.
edge_color_override : bool
Switch that enables random sequential colormap selection for edges.
"""
import os
import yaml
import sys
import os.path as op
import random
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
from nilearn import plotting as niplot
import pkg_resources
import networkx as nx
from pynets.plotting import plot_gen, plot_graphs
from pynets.plotting.plot_gen import create_gb_palette
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
ch2better_loc = pkg_resources.resource_filename(
"pynets", "templates/ch2better.nii.gz")
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
'r') as stream:
hardcoded_params = yaml.load(stream)
try:
if edge_color_override is False:
color_theme = hardcoded_params['plotting']['functional'][
'glassbrain']['color_theme'][0]
else:
color_theme = random.choice([
'Purples_d', 'Blues_d', 'Greens_d', 'Oranges_d', 'Reds_d',
'YlOrBr_d', 'YlOrRd_d', 'OrRd_d', 'PuRd_d', 'RdPu_d',
'BuPu_d', 'GnBu_d', 'PuBu_d', 'YlGnBu_d', 'PuBuGn_d',
'BuGn_d', 'YlGn_d'
])
connectogram = hardcoded_params['plotting']['connectogram'][0]
glassbrain = hardcoded_params['plotting']['glassbrain'][0]
adjacency = hardcoded_params['plotting']['adjacency'][0]
dpi_resolution = hardcoded_params['plotting']['dpi'][0]
except KeyError:
print(
'ERROR: Plotting configuration not successfully extracted from runconfig.yaml'
)
sys.exit(0)
stream.close()
if not isinstance(coords, list):
coords = list(tuple(x) for x in coords)
if not isinstance(labels, list):
labels = list(labels)
if len(coords) > 0:
if '\'b' in atlas:
atlas = atlas.decode('utf-8')
namer_dir = dir_path + '/figures'
if not os.path.isdir(namer_dir):
os.makedirs(namer_dir, exist_ok=True)
# Plot connectogram
if connectogram is True:
if len(conn_matrix) > 20:
try:
plot_gen.plot_connectogram(conn_matrix, conn_model, atlas,
namer_dir, ID, network, labels)
except RuntimeWarning:
print('\n\n\nWarning: Connectogram plotting failed!')
else:
print(
'Warning: Cannot plot connectogram for graphs smaller than 20 x 20!'
)
# Plot adj. matrix based on determined inputs
if not node_size or node_size == 'None':
node_size = 'parc'
if adjacency is True:
plot_graphs.plot_conn_mat_func(conn_matrix, conn_model, atlas,
namer_dir, ID, network, labels, roi,
thr, node_size, smooth, hpass,
extract_strategy)
if glassbrain is True:
views = ['x', 'y', 'z']
# Plot connectome
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir, '/', 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, '_glass_viz.png')
connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)),
[(0, 0, 0)],
node_size=0.0001,
black_bg=True)
connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
[
conn_matrix, clust_pal_edges, clust_pal_nodes, node_sizes,
edge_sizes, z_min, z_max, coords, labels
] = create_gb_palette(conn_matrix, color_theme, coords, labels)
if roi:
# Save coords to pickle
coord_path = f"{namer_dir}{'/coords_'}{op.basename(roi).split('.')[0]}{'_plotting.pkl'}"
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = f"{namer_dir}{'/labelnames_'}{op.basename(roi).split('.')[0]}{'_plotting.pkl'}"
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
else:
# Save coords to pickle
coord_path = f"{namer_dir}{'/coords_plotting.pkl'}"
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = f"{namer_dir}{'/labelnames_plotting.pkl'}"
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
connectome.add_graph(conn_matrix,
coords,
edge_cmap=clust_pal_edges,
edge_vmax=float(z_max),
edge_vmin=float(z_min),
node_size=node_sizes,
node_color=clust_pal_nodes,
edge_kwargs={'alpha': 0.45})
for view in views:
mod_lines = []
for line, edge_size in list(
zip(connectome.axes[view].ax.lines, edge_sizes)):
line.set_lw(edge_size)
mod_lines.append(line)
connectome.axes[view].ax.lines = mod_lines
connectome.savefig(out_path_fig, dpi=dpi_resolution)
else:
raise RuntimeError(
'\nERROR: no coordinates to plot! Are you running plotting outside of pynets\'s '
'internal estimation schemes?')
plt.close('all')
return