ggn.soma.push()
ordered_tree = h.SectionList()
ordered_tree.wholetree()
h.pop_section()
for sec in ordered_tree:
sec.push()
for ii, seg in enumerate(sec):
if seg.diam < dialim:
seg.diam = seg.diam * diascale
# print('Changed diameter of segment', ii, seg.x, 'of section', sec.name(), 'to', seg.diam)
count += 1
h.pop_section()
print('Scaled diameters of', count, 'sections whose diameters were <',
dialim, 'by', diascale)
g0 = nu.nrngraph(ggn)
g, nmap = ng.renumber_nodes(g0, ggn.soma.name())
type_node_map = defaultdict(list)
# Reverse lookup table for node by stype
for n, d in g.nodes_iter(data=True):
stype = d['s']
if g.node[n]['orig'] is not None: # Skip the dummy nodes at terminal
type_node_map[stype].append(n)
synnodes = []
if args.synfile is None:
for branch_id in inputs:
nodes = type_node_map[branch_id]
size = syncount
if len(nodes) < syncount:
size = len(nodes)
for n in np.random.choice(nodes, size=size, replace=False):
# print(n, g.node[n])
'--origlabel',
action='store_true',
dest='origlabel',
help='If the original neuron section names should be used.')
args = parser.parse_args()
h.xopen(args.filename)
cellclass = getattr(h, args.cellname)
cell = cellclass()
if args.stick:
graph = nu.nrngraph(cell)
for n in graph.nodes:
print(n)
root = '{}.soma'.format(list(graph.nodes)[0].partition('.')[0])
else:
graph, root = cell_to_morph3d(cell)
numeric, nmap = ng.renumber_nodes(graph, start=root)
print(len(graph.nodes))
print(max(numeric.nodes))
print(len(numeric.nodes))
if args.origlabel:
reverse = {node: name.split('.')[-1] for name, node in nmap.items()}
label_nodes = reverse.keys()
labels = reverse.values()
else:
label_nodes = list(numeric.nodes)
labels = label_nodes
morph3d.show_neurons({args.cellname: numeric},
label_nodes_dict={args.cellname: label_nodes},
labels_dict={args.cellname: labels},