Figure 5.5
-----------


.. image:: fig5p5.png

:download:`Downloadable Source Code <fig5p5.py>` 

::

    import EoN
    import networkx as nx
    import matplotlib.pyplot as plt
    import scipy
    import random
    
            
    
    def sim_and_plot(G, tau, gamma, rho, tmax, tcount, ax):
        t, S, I, R= EoN.fast_SIR(G, tau, gamma, rho = rho, tmax = tmax)
        report_times = scipy.linspace(0, tmax, tcount)
        I = EoN.subsample(report_times, t, I)
        ax.plot(report_times, I/N, color='grey', linewidth=5, alpha=0.3)
        
        t, S, I, R = EoN.SIR_heterogeneous_meanfield_from_graph(G, tau, gamma, rho=rho, 
                                                        tmax=tmax, tcount=tcount)
        ax.plot(t, I/N, '--')    
        t, S, I, R = EoN.SIR_compact_pairwise_from_graph(G, tau, gamma, rho=rho,
                                                        tmax=tmax, tcount=tcount)
        ax.plot(t, I/N)
    
        t, S, I, R = EoN.SIR_homogeneous_pairwise_from_graph(G, tau, gamma, rho=rho, 
                                                        tmax=tmax, tcount=tcount)
        ax.plot(t, I/N, '-.')
    
    N=50000
    gamma = 1
    rho = 0.05
    tmax = 15
    tcount = 1001
    
    deg_seq = [30]*int(N/2) + [70]*int(N/2)
    G = nx.configuration_model(deg_seq)
    kave = sum(deg_seq)/N
    ksqave = sum(k*k for k in deg_seq)/N
    
    tau= 2*gamma*kave/ksqave
    
    fig = plt.figure(1)
    main = plt.axes()
    sim_and_plot(G, tau, gamma, rho, tmax, tcount, main)
    plt.ylabel('Prevalence')
    
    
    deg_seq = [5]*int(N/2) + [15]*int(N/2)
    G = nx.configuration_model(deg_seq)
    
    kave = (sum(deg_seq)/N)
    ksqave = sum(k*k for k in deg_seq)/N
    
    tau= 2*gamma*kave/ksqave
    
    fig = plt.figure(1)
    ax1 = plt.gca()
    inset = plt.axes([0.5,0.45,0.4,0.4])
    sim_and_plot(G, tau, gamma, rho, tmax, tcount, inset)
    
    ax1.set_xlabel('$t$')
    ax1.set_ylabel('Prevalence')
    
    plt.savefig('fig5p5.png')