Figure 6.4 (a and b) ---------------------------------------------------- :download:Downloadable Source Code  .. image:: fig6p4a.png :width: 45 % .. image:: fig6p4b.png :width: 45 % :: 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**3 p=0.25 kave = 5. Ns = [100, 400, 1600, 6400]#, 25600] for index, N in enumerate(Ns): r'''First we do it with the same network for each iteration''' print(N) xm = {m:0 for m in range(1,N+1)} G = nx.fast_gnp_random_graph(N, kave/(N-1.)) 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) freq = scipy.array(freq) m= scipy.array(m) cum_freq = scipy.cumsum(freq) plt.figure(1) plt.plot(m/N, 1-cum_freq, color = colors[index]) plt.figure(1) plt.xlabel(r'$\rho$') plt.ylabel(r'y_{G,p}(\rho)') plt.savefig('fig6p4a.png') for index, N in enumerate(Ns): '''Now we generate a new network for each iteration''' print(N) xm = {m:0 for m in range(1,N+1)} for counter in range(iterations): G = nx.fast_gnp_random_graph(N, kave/(N-1.)) t, S, I, R = EoN.basic_discrete_SIR_epidemic(G, p) xm[R[-1]] += 1./iterations items = sorted(xm.items()) m, freq = zip(*items) freq = scipy.array(freq) m= scipy.array(m) cum_freq = scipy.cumsum(freq) plt.figure(2) plt.plot(m/N, 1-cum_freq, color = colors[index]) plt.figure(2) plt.xlabel(r'$\rho$') plt.ylabel(r'Y_{\mathcal{G},p}(\rho,N)') plt.savefig('fig6p4b.png')