Figure 6.2 (a and b)

Downloadable Source Code

import networkx as nx
import EoN
from collections import defaultdict
import matplotlib.pyplot as plt
import scipy

colors = ['#5AB3E6','#FF2000','#009A80','#E69A00', '#CD9AB3', '#0073B3','#F0E442']

iterations = 5*10**5



N=6400
kave = 5
G = nx.fast_gnp_random_graph(N, kave/(N-1.))

for index, p in enumerate([0.18, 0.19, 0.2, 0.205, 0.21, 0.22]):
    print(p)
    xm = defaultdict(int)
    for counter in range(iterations):
        t, S, I, R = EoN.basic_discrete_SIR_epidemic(G, p)
        xm[R[-1]] += 1./iterations
    items = sorted(xm.items())
    m, freq = zip(*items)


    plt.figure(1)
    plt.plot(m, freq, color=colors[index])
    plt.yscale('log')

    freq = scipy.array(freq)
    m= scipy.array(m)
    plt.figure(2)
    plt.plot()
    plt.plot(m/N, N*freq, color = colors[index])

plt.figure(1)
plt.axis(xmin=0, xmax=300)
plt.xlabel('number infected $m$')
plt.ylabel(r'$\log_{10}$ probability $x(m)$')
plt.savefig('fig6p2a.png')

plt.figure(2)
plt.axis(xmin=0,xmax=0.5, ymax = 1.5, ymin=0)
plt.xlabel('proportion infected $m/N$')
plt.ylabel(r'normalized probability $Nx(m)$')
plt.savefig('fig6p2b.png')