update to make Python 3 compatible

events.py

@@ -66,7 +66,7 @@ def __divide_at_midpoint(G):
     Returns subgraphs by cutting the recurrence network at the midpoint.
     """
     n = G.vcount()
-    inds1, inds2 = range(0, n / 2), range(n / 2, n)
+    inds1, inds2 = range(0, int(n / 2)), range(int(n / 2), n)
     G1, G2 = G.subgraph(G.vs[inds1]), G.subgraph(G.vs[inds2])
     return G1, G2
 
@@ -82,10 +82,10 @@ def community_strength_data(G, time, wsize, wstep,
     n = G.vcount()
     nwind = ((n - wsize) / wstep) + 1
     tmid = []
-    Q, LD = [np.zeros(nwind) for i in range(2)]
+    Q, LD = [np.zeros(int(nwind)) for i in range(2)]
     _printmsg("Estimating intra-community link fraction...", verbose)
     prog_bar = _progressbar_start(n, pbar)
-    for i in range(nwind):
+    for i in range(int(nwind)):
         k = i * wstep
         start, stop = k, k + wsize
         t_ = (time[start:stop]).sum() / wsize
@@ -109,11 +109,11 @@ def community_strength_random_model(G, time, wsize, wstep, nsurr,
     n = G.vcount()
     nwind = ((n - wsize) / wstep) + 1
     tmid = []
-    Qsurr, LDsurr = [np.zeros((nwind, nsurr), "float") for i in range(2)]
+    Qsurr, LDsurr = [np.zeros((int(nwind), int(nsurr)), "float") for i in range(2)]
     _printmsg("Estimating intra-community link fraction...", verbose)
     prog_bar = _progressbar_start(nwind*nsurr, pbar)
     count = 0
-    for i in range(nwind):
+    for i in range(int(nwind)):
         k = i * wstep
         start, stop = k, k + wsize
         t_ = (time[start:stop]).sum() / wsize