import numpy as np
import time
import scipy.sparse as sprs
import progressbar
B = 10
L = 6
k = 6
xi = 0.3
N_meas = 10
k_nx, k_c = 0.0, 0.0
t_nx, t_c = 0.0, 0.0
seed = 1
print "measuring only creation"
bar = progressbar.ProgressBar()
for meas in bar(xrange(N_meas)):
tic = time.time()
G2 = cMHRN.fast_mhrn(B, L, k, xi, seed=seed)
toc = time.time()
t_c += (toc - tic) / N_meas
k_c += len(G2) * 2. / B**L / N_meas
#print "python: k = %4.2f, t = %fs" % (k_nx,t_nx)
print "c++ : k = %4.2f, t = %fs" % (k_c, t_c)
print
import numpy as np
import networkx as nx
import netwulf as nw
import cMHRN
# load edges from txt file and construct Graph object
N, edges = cMHRN.fast_mhrn(8,3,7,0.18,True)
G = nx.Graph()
G.add_edges_from(edges)
# visualize and save visualization
network, config = nw.visualize(G)
nw.save("MHRN.json",network,config)
B = 10
L = 3
k = 13
xi = 0.3
N_meas = 20
k_nx, k_c = 0.0, 0.0
t_nx, t_c = 0.0, 0.0
seed = 1
print("measuring only creation")
bar = progressbar.ProgressBar()
for meas in bar(range(N_meas)):
tic = time.time()
N, G2 = cMHRN.fast_mhrn(B,
L,
k,
xi,
seed=seed + 1,
allow_probability_redistribution=True,
use_giant_component=False)
toc = time.time()
t_c += (toc - tic) / N_meas
k_c += len(G2) * 2. / B**L / N_meas
#print "python: k = %4.2f, t = %fs" % (k_nx,t_nx)
print("c++ : k = %4.2f, t = %fs" % (k_c, t_c))