import networkx as nx
import EoN
import matplotlib.pyplot as plt


N= 100000
rho = 0.001
gamma = 1

G = nx.configuration_model([2,6]*int(N/2)) #N nodes, half have degree 6 and half degree 2

#assign edge weights to be product of degree.  Also give another weight to be inverse of product of degrees
weight_sum = 0
inv_weight_sum = 0

for edge in G.edges():
    G.edge[edge[0]][edge[1]]['weight'] = G.degree(edge[0])*G.degree(edge[1])
    G.edge[edge[0]][edge[1]]['inv_weight'] = 1./(G.degree(edge[0])*G.degree(edge[1]))
    weight_sum += G.degree(edge[0])*G.degree(edge[1])
    inv_weight_sum += 1./(G.degree(edge[0])*G.degree(edge[1]))

#first do it with weight, scaled so that average weight is 1.
t, S, I = EoN.fast_SIS(G, G.number_of_edges()/weight_sum, gamma, rho = rho, transmission_weight= 'weight', tmax = 5)
plt.plot(t, I, label = 'weight')


t, S, I = EoN.fast_SIS(G, G.number_of_edges()/inv_weight_sum, gamma, rho = rho, transmission_weight= 'inv_weight', tmax = 5)
plt.plot(t, I, label = 'inv_weight')

plt.legend(loc = 'lower right')
plt.savefig('SIS_weighted.pdf')
import networkx as nx
import EoN
import matplotlib.pyplot as plt

r'''This code simulates an SIS epidemic in a graph.  The edges are weighted by 
two methods: the product of the degrees, or the inverse of that product.

I then run simulations with the transmission rates scaled by the edge weights
such that a random edge would have expected transmission weight 1 (though since
there will be biases in which nodes are most likely to be infected, the
random edge that has an infected node will have higher transmission weight).

At present (16 May 2017) the Gillespie code cannot run weighted networks.  I 
am considering adding that, but it would DRAMATICALLY slow down the simulations,
so it won't be even close to competitive with the event-driven versions.  So it's
a low priority.
'''

N= 100000
rho = 0.001
gamma = 1

G = nx.configuration_model([2,6]*int(N/2)) #N nodes, half have degree 6 and half degree 2

#assign edge weights to be product of degree.  Also give another weight to be inverse of product of degrees
weight_sum = 0
inv_weight_sum = 0

for edge in G.edges():
    G.edge[edge[0]][edge[1]]['weight'] = G.degree(edge[0])*G.degree(edge[1])
    G.edge[edge[0]][edge[1]]['inv_weight'] = 1./(G.degree(edge[0])*G.degree(edge[1]))
    weight_sum += G.degree(edge[0])*G.degree(edge[1])
    inv_weight_sum += 1./(G.degree(edge[0])*G.degree(edge[1]))

#first do it with weight, scaled so that average weight is 1.
t, S, I = EoN.fast_SIS(G, G.number_of_edges()/weight_sum, gamma, rho = rho, transmission_weight= 'weight', tmax = 5)
plt.plot(t, I, label = 'weight')


t, S, I = EoN.fast_SIS(G, G.number_of_edges()/inv_weight_sum, gamma, rho = rho, transmission_weight= 'inv_weight', tmax = 5)
plt.plot(t, I, label = 'inv_weight')

plt.legend(loc = 'lower right')
plt.savefig('SIS_weighted.pdf')
Improve documentation of SIS_weighted.