def test_plot_conn_mat_nonet_mask():
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
dir_path = base_dir + '/997'
network = None
ID = '997'
thr = 0.95
node_size = 2
smooth = 2
conn_model = 'sps'
atlas_select = 'whole_brain_cluster_labels_PCA200'
mask = None
conn_matrix = np.genfromtxt(
dir_path +
'/whole_brain_cluster_labels_PCA200/997_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')
label_names = pickle.load(labels_file)
start_time = time.time()
plotting.plot_conn_mat_func(conn_matrix, conn_model, atlas_select,
dir_path, ID, network, label_names, mask, thr,
node_size, smooth)
print("%s%s%s" % ('plot_conn_mat_func (Masking version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def plot_all(conn_matrix, conn_model, atlas_select, dir_path, ID, network,
label_names, mask, coords, thr, node_size, edge_threshold, smooth,
prune, uatlas_select):
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.netstats import most_important, prune_disconnected
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
coords = list(coords)
label_names = list(label_names)
dpi_resolution = 500
if '\'b' in atlas_select:
atlas_select = atlas_select.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)
label_names_pre = list(label_names)
if len(pruned_nodes) > 0:
for j in pruned_nodes:
label_names_pre.pop(j)
coords_pre.pop(j)
conn_matrix = nx.to_numpy_array(G)
label_names = label_names_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:
plotting.plot_connectogram(conn_matrix, conn_model, atlas_select,
dir_path, ID, network, label_names)
except:
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'
plotting.plot_conn_mat_func(conn_matrix, conn_model, atlas_select,
dir_path, ID, network, label_names, mask, thr,
node_size, smooth)
# Plot connectome
if mask:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', str(atlas_select), '_', str(conn_model),
'_', str(os.path.basename(mask).split('.')[0]), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"), str(thr),
'_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" % (smooth, 'fwhm_') if float(smooth) > 0 else 'nosm_'),
'func_glass_viz.png')
# Save coords to pickle
coord_path = "%s%s%s%s" % (dir_path, '/coords_',
os.path.basename(mask).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_',
os.path.basename(mask).split('.')[0],
'_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f, protocol=2)
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', str(atlas_select), '_', str(conn_model),
"%s" % ("%s%s%s" % ('_', network, '_') if network else "_"),
str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" % (smooth, 'fwhm_') if float(smooth) > 0 else 'nosm_'),
'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(label_names, 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=0.35, 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_select:
connectome.add_contours(uatlas_select,
filled=False,
alpha=0.3,
colors='black')
else:
node_size_plot = int(node_size)
if len(coords) != conn_matrix.shape[0]:
raise RuntimeWarning(
'WARNING: Number of coordinates does not match conn_matrix dimensions. If you are using disparity filtering, try relaxing the α threshold.'
)
else:
connectome.add_graph(conn_matrix,
coords,
edge_threshold=edge_threshold,
edge_cmap='Blues',
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)
return
def plot_all(conn_matrix, conn_model, atlas_select, dir_path, ID, network,
label_names, mask, coords, thr, node_size, edge_threshold):
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
from pynets.netstats import most_important
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
pruning = True
dpi_resolution = 500
G_pre = nx.from_numpy_matrix(conn_matrix)
if pruning == True:
[G, pruned_nodes, pruned_edges] = most_important(G_pre)
else:
G = G_pre
conn_matrix = nx.to_numpy_array(G)
pruned_nodes.sort(reverse=True)
for j in pruned_nodes:
del label_names[label_names.index(label_names[j])]
del coords[coords.index(coords[j])]
pruned_edges.sort(reverse=True)
for j in pruned_edges:
del label_names[label_names.index(label_names[j])]
del coords[coords.index(coords[j])]
# Plot connectogram
if len(conn_matrix) > 20:
try:
plotting.plot_connectogram(conn_matrix, conn_model, atlas_select,
dir_path, ID, network, label_names)
except RuntimeError:
print('\n\n\nError: Connectogram plotting failed!')
else:
print(
'Error: Cannot plot connectogram for graphs smaller than 20 x 20!')
# Plot adj. matrix based on determined inputs
plotting.plot_conn_mat_func(conn_matrix, conn_model, atlas_select,
dir_path, ID, network, label_names, mask, thr,
node_size)
# Plot connectome
if mask:
if network:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', str(atlas_select), '_',
str(conn_model), '_', str(
os.path.basename(mask).split('.')[0]), '_', str(network),
'_', str(thr), '_', str(node_size), '_func_glass_viz.png')
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', str(atlas_select), '_',
str(conn_model), '_', str(
os.path.basename(mask).split('.')[0]), '_', str(thr), '_',
str(node_size), '_func_glass_viz.png')
# Save coords to pickle
coord_path = "%s%s%s%s" % (dir_path, '/coords_',
os.path.basename(mask).split('.')[0],
'_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
net_parcels_map_nifti = None
# Save labels to pickle
labels_path = "%s%s%s%s" % (dir_path, '/labelnames_',
os.path.basename(mask).split('.')[0],
'_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f, protocol=2)
else:
if network:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_', str(atlas_select), '_',
str(conn_model), '_', str(network), '_', str(thr), '_',
str(node_size), '_func_glass_viz.png')
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', ID, '_',
str(atlas_select), '_', str(conn_model), '_', str(thr), '_',
str(node_size), '_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(label_names, f, protocol=2)
#niplot.plot_connectome(conn_matrix, coords, edge_threshold=edge_threshold, node_size=20, colorbar=True, output_file=out_path_fig)
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)
connectome.add_overlay(ch2better_loc, alpha=0.4, cmap=plt.cm.gray)
[z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
connectome.add_graph(conn_matrix,
coords,
edge_threshold=edge_threshold,
edge_cmap='Greens',
edge_vmax=z_max,
edge_vmin=z_min,
node_size=4)
connectome.savefig(out_path_fig, dpi=dpi_resolution)
#connectome.savefig(out_path_fig, dpi=dpi_resolution, facecolor ='k', edgecolor ='k')
return