Figure 7.4 ------------- :download:`Downloadable Source Code ` .. image:: fig7p4.png :: import networkx as nx import EoN from collections import defaultdict import matplotlib.pyplot as plt import scipy import random N=10**6 tau = 1. gamma = 1. colors = ['#5AB3E6','#FF2000','#009A80','#E69A00', '#CD9AB3', '#0073B3','#F0E442'] kave = 5 G = nx.fast_gnp_random_graph(N, kave/(N-1.)) initial_infecteds = random.sample(range(N),int(0.01*N)) print('simulating') t, S, I, R = EoN.fast_SIR(G, tau, gamma, initial_infecteds = initial_infecteds) report_times = scipy.linspace(0,10,101) S, I, R = EoN.subsample(report_times, t, S, I, R) plt.plot(report_times, S, color =colors[1], label = 'simulation') plt.plot(report_times, I, color = colors[1]) plt.plot(report_times, R, color = colors[1]) print('doing ODE models') t, S, I, R = EoN.SIR_effective_degree_from_graph(G, tau, gamma, initial_infecteds=initial_infecteds, tmax = 10, tcount = 51) plt.plot(t,S, color = colors[2], dashes = [6,6], label = 'effective degree') plt.plot(t,I, color = colors[2], dashes = [6,6]) plt.plot(t,R, color = colors[2], dashes = [6,6]) t, S, I, R = EoN.SIR_heterogeneous_pairwise_from_graph(G, tau, gamma, initial_infecteds=initial_infecteds, tmax = 10, tcount = 51) plt.plot(t, S, color = colors[3], dashes = [3,2,1,2], linewidth=3, label = 'pairwise') plt.plot(t, I, color = colors[3], dashes = [3,2,1,2], linewidth=3) plt.plot(t, R, color = colors[3], dashes = [3,2,1,2], linewidth=3) #, dashes = [6,3,2,3] t, S, I, R = EoN.EBCM_from_graph(G, tau, gamma, initial_infecteds=initial_infecteds, tmax = 10, tcount =51) plt.plot(t, S, ':', color = colors[4], label = 'EBCM') plt.plot(t, I, ':', color = colors[4]) plt.plot(t, R, ':', color = colors[4]) plt.axis(xmax=10, xmin=0) plt.xlabel('\$t\$') plt.legend(loc = 'center right') plt.savefig('fig7p4.png')