Figure 5.3
----------

.. image:: fig5p3.png

:download:`Downloadable Source Code <fig5p3.py>` 

::

    import EoN
    import networkx as nx
    import matplotlib.pyplot as plt
    import scipy
    
    
    
    def sim_and_plot(G, tau, gamma, rho, tmax, tcount, ax):
        t, S, I = EoN.fast_SIS(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, = EoN.SIS_heterogeneous_meanfield_from_graph(G, tau, gamma, rho=rho, 
                                                        tmax=tmax, tcount=tcount)
        ax.plot(t, I/N, '--')    
        t, S, I = EoN.SIS_compact_pairwise_from_graph(G, tau, gamma, rho=rho,
                                                        tmax=tmax, tcount=tcount)
        ax.plot(t, I/N)
    
        t, S, I = EoN.SIS_homogeneous_pairwise_from_graph(G, tau, gamma, rho=rho, 
                                                        tmax=tmax, tcount=tcount)
        ax.plot(t, I/N, '-.')
    
    
    tcount = 1001
    tmax = 10.
    gamma = 1.
    N=10000
    rho = 0.05
    
    fig = plt.figure(1)
    main = plt.axes()
    
    
    target_kave = 50.
    G = nx.fast_gnp_random_graph(N, target_kave/(N-1.))
    kave = sum(G.degree(node) for node in G.nodes())
    ksqave =sum(G.degree(node)**2 for node in G.nodes())
    tau_c = gamma*kave/ksqave
    tau = 2*tau_c
    
    sim_and_plot(G, tau, gamma, rho, tmax, tcount, main)
    
    
    inset = plt.axes([0.45,0.175,0.45,0.45])
    target_kave = 10.
    
    G = nx.fast_gnp_random_graph(N, target_kave/(N-1.))
    kave = sum(G.degree(node) for node in G.nodes())
    ksqave =sum(G.degree(node)**2 for node in G.nodes())
    tau_c = gamma*kave/ksqave
    tau = 2*tau_c
    
    sim_and_plot(G, tau, gamma, rho, tmax, tcount, inset)
    
    main.set_xlabel('$t$')
    main.set_ylabel('Prevalence')
    plt.savefig('fig5p3.png')