Figure 4.7 (a, b, c, and d)
---------------------------

:download:`Downloadable Source Code <fig4p7.py>` 

   - Note that the book has a typo.  For (c), $\\tau = 1.1\\tau_c$

.. image:: fig4p7a.png
    :width: 45 %
.. image:: fig4p7b.png
    :width: 45 %
.. image:: fig4p7c.png
    :width: 45 %
.. image:: fig4p7d.png
    :width: 45 %


::

    import EoN
    import networkx as nx
    import matplotlib.pyplot as plt
    import scipy
    
    
    print(r"warning --- plot c in book is mislabeled.  It should be \tau = 1.1\tau_c, not 1.2\tau_c")
    N=1000
    kave = 20
    gamma = 1.
    iterations = 200
    tmax = 40
    tau_c = gamma/kave
    rho = 0.05
    tcount=1001
    
    report_times = scipy.linspace(0,tmax,tcount)
    
    for tau, label in zip([0.9*tau_c, tau_c, 1.1*tau_c, 1.5*tau_c],['a', 'b', 'c', 'd']):
        plt.clf()
        Isum = scipy.zeros(len(report_times))
        for counter in range(iterations):
            G = nx.configuration_model([kave]*N)
            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
        plt.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)
        plt.plot(t, I/N, '--')
        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, tau, gamma, tmin = 0, 
                                    tmax=tmax, tcount=tcount)
        plt.plot(t, I/N)
        plt.xlabel('$t$')
        plt.ylabel('Prevalence')
        plt.savefig('fig4p7{}.png'.format(label))