network = np.empty((4,len(params['N_subs'])))
kappas = np.empty((len(params['N_subs']),params['num_fits']))
for n, N_sub in enumerate(params['N_subs']):
    print 'N_sub = %d' % N_sub
    estimate = np.empty((params['B'],2,params['num_fits']))
    network_obs = np.empty((4,params['num_fits']))
    for num_fit in range(params['num_fits']):
        alpha_sub, kappa_sub, A_sub, x_sub = subnetwork(N_sub)
        print 'kappa_sub = %.2f' % kappa_sub
                
        network_obs[0,num_fit] = 1.0 * np.sum(A_sub) / N_sub
        network_obs[1,num_fit] = np.max(np.sum(A_sub, axis = 1))
        network_obs[2,num_fit] = np.max(np.sum(A_sub, axis = 0))
        network_obs[3,num_fit] = 1.0 * np.sum(np.diagonal(A_sub)) / N_sub