def test_create_random_graph():
base_dir = str(Path(__file__).parent/"examples")
in_mat = np.load(base_dir + '/997/997_Default_est_cov_0.1_4.npy')
G = nx.from_numpy_array(in_mat)
n = 10
p = 0.5
start_time = time.time()
rG = netstats.create_random_graph(G, n, p)
print("%s%s%s" % ('thresh_and_fit (Functional, proportional thresholding) --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))
assert rG is not None
def smallworldness(G, rep = 1000):
n = nx.number_of_nodes(G)
m = nx.number_of_edges(G)
p = float(m) * 2 /(n*(n-1))
ss = []
for bb in range(rep):
rG = create_random_graph(G, n, p)
swm = smallworldness_measure(G, rG)
ss.append(swm)
mean_s = np.mean(ss)
return mean_s