Figure 4.10
-----------

:download:`Downloadable Source Code <fig4p10.py>` 

.. image:: fig4p10.png

::

    import EoN
    import networkx as nx
    import matplotlib.pyplot as plt
    import scipy
    
    
    N=1000
    gamma = 1.
    iterations = 200
    rho = 0.05
    tmax = 10
    tcount = 1001
    
    report_times = scipy.linspace(0,tmax,tcount)
    
    deg_dist1 = [18,22]*int((N/2)+0.01)
    deg_dist2 = [5,35]*int((N/2)+0.01)
    ax1 = plt.gca()#axes([0.1,0.1,0.9,0.9])
    ax2 = plt.axes([0.44,0.2,0.4,0.4])
    for deg_dist, ax in zip([deg_dist1, deg_dist2], [ax1, ax2]):
        kave = sum(deg_dist1)*1./N
        tau = 2*gamma/kave
        Isum = scipy.zeros(tcount)
    
        for counter in range(iterations):
            G = nx.configuration_model(deg_dist)
            t, S, I = EoN.fast_SIS(G, tau, gamma, tmax=tmax, rho=rho)
            I = I*1./N
            I = EoN.subsample(report_times, t, I)
            Isum += I
        ax.plot(report_times, Isum/iterations, color='grey', linewidth=5, alpha=0.3)
        
        S0 = (1-rho)*N
        I0 = rho*N
        
        t, S, I = EoN.SIS_homogeneous_meanfield(S0, I0, kave, tau, gamma, tmin=0, tmax=tmax, 
                                    tcount=tcount)
        ax.plot(t, I/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, tau, gamma, tmin = 0, 
                                    tmax=tmax, tcount=tcount)
        ax.plot(t, I/N)
    
    ax1.set_xlabel('$t$')
    ax1.set_ylabel('Prevalence')
    
    plt.savefig('fig4p10.png')