for ax in maxes:
for n in cg.nodes():
cg.node[n][ax] = -cg.node[n][ax]
if ii < len(args.translate):
cell_pos[fname] = [int(x) for x in args.translate[ii].split()]
else:
cell_pos[fname] = [0, 0, 0]
if ii < len(args.rotate):
rot = args.rotate[ii].split()
for jj in range(0, len(rot), 2):
rot[jj + 1] = float(rot[jj + 1])
cell_rot[fname] = zip(rot[0::2], rot[1::2])
else:
cell_rot[fname] = []
if args.branches:
lnodes, edges = zip(*ng.branch_points(cg))
label_nodes[fname] = list(lnodes)
if args.leaves:
label_nodes[fname] += [
node for node in cg.nodes() if cg.degree(node) == 1
]
if len(args.sid) > 0:
sid_node_map = ng.get_stype_node_map(cg)
for sid in args.sid:
label_nodes[fname] += sid_node_map[sid]
show_neurons(cell_graphs,
lines=args.lines,
label_nodes_dict=label_nodes,
labels_dict={},
pos_dict=cell_pos,
rot_dict=cell_rot,
if len(args.rotate) > ii:
xrot, yrot, zrot = args.rotate[ii]
tmatrix = make_tmatrix(xshift, yshift, zshift, xrot, yrot, zrot)
print('Transformation matrix for', fname)
print(tmatrix)
rigid_transform_graph(cellgraph, tmatrix)
if ii > 0:
maxnode = max(combined_cellgraph.nodes())
for node in cellgraph.nodes():
cellgraph.node[node]['p'] += maxnode
mapping = {n: n + maxnode for n in cellgraph.nodes()}
cellgraph = nx.relabel_nodes(cellgraph, mapping)
combined_cellgraph = nx.union(combined_cellgraph, cellgraph)
label_nodes = []
if args.branches:
label_nodes, degrees = zip(*ng.branch_points(combined_cellgraph))
label_nodes = list(label_nodes)
if args.leaves:
label_nodes += [node for node in combined_cellgraph.nodes()
if combined_cellgraph.degree(node) == 1]
if len(args.sid) > 0:
sid_node_map = ng.get_stype_node_map(combined_cellgraph)
for sid in args.sid:
label_nodes += sid_node_map[sid]
if module == 'mplot3d':
ax = mp.plot_3d_lines(combined_cellgraph)
plt.show()
elif module == 'vtk':
neuron3d(combined_cellgraph, lines=lines, label_nodes=label_nodes,
labels=label_nodes, nodecolor=colormap,
background=background, axes=args.scalebar,