bgneal@35: """Chapter 5.5, exercise 6 in Allen Downey's Think Complexity book.
bgneal@35: 
bgneal@35: 2. Use the WS model to generate the largest graph you can in a reasonable amount
bgneal@35:    of time. Plot P(k) versus k and see if you can characterize the tail
bgneal@35:    behavior.
bgneal@35: 
bgneal@35: """
bgneal@35: import sys
bgneal@35: 
bgneal@35: from matplotlib import pyplot
bgneal@35: 
bgneal@35: from Graph import Vertex
bgneal@35: from SmallWorldGraph import SmallWorldGraph
bgneal@35: 
bgneal@35: 
bgneal@35: 
bgneal@35: def main(script, n, k, p):
bgneal@35: 
bgneal@35:     # create a SmallWorldGraph with n vertices, k regular edges between
bgneal@35:     # vertices, and rewiring probability p.
bgneal@35: 
bgneal@35:     n, k, p = int(n), int(k), float(p)
bgneal@35:     vs = [Vertex() for i in xrange(n)]
bgneal@35: 
bgneal@35:     g = SmallWorldGraph(vs, k, p)
bgneal@35: 
bgneal@35:     # retrieve probabilities
bgneal@35:     p = g.get_p()
bgneal@35: 
bgneal@35:     # plot P(k) versus k on a log-log scale
bgneal@35: 
bgneal@35:     vals = p.items()
bgneal@35:     vals.sort(key=lambda t: t[0])
bgneal@35:     x, y = zip(*vals)
bgneal@35: 
bgneal@35:     assert abs(sum(y) - 1.0) < 1e-6
bgneal@35: 
bgneal@35:     pyplot.clf()
bgneal@35:     pyplot.xscale('log')
bgneal@35:     pyplot.yscale('log')
bgneal@35:     pyplot.title('P(k) versus k')
bgneal@35:     pyplot.xlabel('k')
bgneal@35:     pyplot.ylabel('P(k)')
bgneal@35:     pyplot.plot(x, y, label='P(k) vs. k', color='green', linewidth=3)
bgneal@35:     pyplot.legend(loc='upper right')
bgneal@35:     pyplot.show()
bgneal@35: 
bgneal@35: 
bgneal@35: if __name__ == '__main__':
bgneal@35:     main(*sys.argv)