N=200 gamma = 1 k = N-1. tau_c = gamma/k rho = 0.1 for tau, label in zip([0.9*tau_c, tau_c, 1.2*tau_c, 1.5*tau_c],['a', 'b', 'c', 'd']): plt.clf() t, S, I = complete_graph_lumped(N, int(N*rho), 0 , 20, 1001) plt.plot(t, I) S0 = (1-rho)*N I0 = rho*N t, S, I = EoN.SIS_homogeneous_meanfield(S0, I0, k, tau, gamma, tmin=0, tmax=20, tcount=1001) plt.plot(t, I, '--') S0 = (1-rho)*N I0 = rho*N SI0 = (1-rho)*N*k*rho SS0 = (1-rho)*N*k*(1-rho) t, S, I = EoN.SIS_homogeneous_pairwise(S0, I0, SI0, SS0, k, tau, gamma, tmin = 0, tmax=20, tcount=1001) plt.plot(t, I, ':') plt.xlabel('$t$') plt.ylabel('Prevalence') plt.savefig('fig4p5{}.png'.format(label))
if sum(sequence) % 2 == 0: break return sequence graph_function = lambda: nx.configuration_model(generate_sequence(Pk, N)) simulate_process(graph_function, iterations, tmax, tcount, rho, kave, tau, gamma, symbol) symbol = '--' S0 = (1 - rho) * N I0 = rho * N t, S, I = EoN.SIS_homogeneous_meanfield(S0, I0, kave, tau, gamma, tmax=tmax, tcount=tcount) plt.plot(t, I / N, symbol) symbol = '-' S0 = (1 - rho) * N I0 = rho * N SI0 = (1 - rho) * N * kave * rho SS0 = (1 - rho) * N * kave * (1 - rho) t, S, I = EoN.SIS_homogeneous_pairwise(S0, I0, SI0, SS0, kave,