VPython for Graph Visualization

I asked a colleague who might possibly have done some graph
visualization using VPython, and here is what he said:

I haven't really done anything along those lines, although it would be
quite straightforward in principle to do Kamada-Kawai or something
similar.  Attached is a simple program from my undergrad class to do
simulated annealing on the traveling salesman problem, which shows how
one could visualize a graph or network.

> From: Mark Adam <[email protected]>
> Date: Tue, Jul 10, 2012 at 3:29 PM
> Subject: [Visualpython-users] VPython for Graph Visualization
> To: networkx-discuss-/[email protected]
> Cc: visualpython-users-5NWGOfrQmneRv+LV9MX5uipxlwaOVQ5f@public.gmane.org
>
>
> I'm wondering if there's been anyone who's explored using the 3-d
> visualization extension of python for graph visualization?
>
> Cheers,
>
> mark
> pangaia.sf.net
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#!/usr/bin/env python

from __future__ import print_function,division

from math import sqrt,exp
from numpy import empty
from random import random,randrange
from visual import sphere,curve,display

N = 25
R = 0.02
Tmax = 1.0
Tmin = 1e-3
a = 1e-4

# Function to calculate the magnitude of a vector
def mag(x):
    return sqrt(x[0]**2+x[1]**2)

# Function to calculate the total length of the tour
def distance():
    s = 0.0
    for i in range(N):
        s += mag(r[i+1]-r[i])
    return s

# Choose N city locations and calculate the length of the route and
# the initial value of the distance
r = empty([N+1,2],float)
for i in range(N):
    r[i,0] = random()
    r[i,1] = random()
r[N] = r[0]
D = distance()

# Set up the graphics
display(center=[0.5,0.5])
for i in range(N):
    sphere(pos=r[i],radius=R)
l = curve(pos=r,radius=R/2)

# Main loop
t = 0
T = Tmax
while T>Tmin:

    # Cooling
    t += 1
    T = Tmax*exp(-a*t)

    # Update the visualization every 100 moves
    if t%100==0:
        l.pos = r

    # Choose two cities to swap and make sure they are distinct
    i,j = randrange(1,N),randrange(1,N)
    while i==j:
        i,j = randrange(1,N),randrange(1,N)

    # Swap them and calculate the change in distance
    oldD = D
    r[i,0],r[j,0] = r[j,0],r[i,0]
    r[i,1],r[j,1] = r[j,1],r[i,1]
    D = distance()
    deltaD = D - oldD

    # If the move is rejected, swap them back again
    if random()>exp(-deltaD/T):
        r[i,0],r[j,0] = r[j,0],r[i,0]
        r[i,1],r[j,1] = r[j,1],r[i,1]
        D = oldD