def select_good_nodes_by_sid(g, sid_list, counts, replace=False):
"""For each sid in `sid_list` select `count` random nodes with an
underlying `section` from `g` - a neurongraph.
Returns a list of selected nodes.
@seealso: neurograp.select_random_nodes_by_sid
"""
good_nodes = [
n for n, data in g.nodes(data=True) if data['orig'] is not None
]
type_node_map = ng.get_stype_node_map(g.subgraph(good_nodes))
synnodes = []
for branch_id, count in zip(sid_list, counts):
size = len(type_node_map[branch_id])
if (count > size) and (replace == False):
print(
'Changing number of nodes to maximum {} available in branch, since replace={}'
.format(size, replace))
count = size
synnodes += list(
np.random.choice(type_node_map[branch_id],
size=count,
replace=replace))
return synnodes
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,
nodecolor=colormap,
background=background,
axes=args.scalebar,
fullscreen=args.fullscreen,
relative=args.relative)
#
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
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())
# This also gets the dummy nodes at the tips
stype_node_map_all = ng.get_stype_node_map(g)
stype_node_map = {}
# Collect only nodes with sections associated.
for stype, nodes in stype_node_map_all.items():
good = [node for node in nodes if g.node[node]['orig'] is not None]
stype_node_map[stype] = good
synnodes = nu.select_good_nodes_by_sid(g,
inputs, [syncount] * len(inputs),
replace=True)
synsecs = [g.nodes[n]['orig'] for n in synnodes]
synsegs = [sec(1.0) for sec in synsecs]
# Create the exp2syn
# ereversal = 0 for glutamatergic synapse
synlist = nu.insert_exp2syn(synsegs, args.rise_t, args.decay_t, 0.0)
print('Number of synapses', syncount, 'synlist', len(synlist))
# Create stimulation system
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,
fullscreen=args.fullscreen)
elif module == 'vispy':
neuron3d(combined_cellgraph)
elif module == 'vpython':
neuron3d(combined_cellgraph)
else:
