def testIntOverflow():
"""
Avoid integer overflow in scipy.sparse representation.
"""
for n in [10, 200, 2000, 33000]:
adj = sp.lil_matrix((n, n), dtype=np.int8)
adj[0, 1:] = 1
deg = Network(adjacency=adj).degree()
assert (deg.min(), deg.max()) == (0, n - 1)
def test_path_lengths():
res = Network.SmallTestNetwork().path_lengths()
exp = np.array([[0., 2., 2., 1., 1., 1.], [2., 0., 1., 1., 1., 3.],
[2., 1., 0., 2., 1., 3.], [1., 1., 2., 0., 2., 2.],
[1., 1., 1., 2., 0., 2.], [1., 3., 3., 2., 2., 0.]])
assert np.allclose(res, exp)
def test_assortativity():
res = Network.SmallTestNetwork().assortativity()
exp = -0.47368421
assert np.allclose(res, exp)
def test_local_outmotif_clustering():
res = Network.SmallDirectedTestNetwork().local_outmotif_clustering()
exp = np.array([0.5, 0.5, 0., 0., 0., 0.])
assert np.allclose(res, exp)
def test_local_clustering():
res = Network.SmallTestNetwork().local_clustering()
exp = np.array([0., 0.33333333, 1., 0., 0.33333333, 0.])
assert np.allclose(res, exp)
def test_max_neighbors_degree():
res = Network.SmallTestNetwork().max_neighbors_degree()
exp = np.array([3, 3, 3, 3, 3, 3])
assert (res == exp).all()
def test_nsi_degree_cumulative_histogram():
res = Network.SmallTestNetwork().nsi_degree_cumulative_histogram()
exp = (np.array([1., 0.66666667,
0.5]), np.array([4., 5.46666667, 6.93333333]))
assert np.allclose(res, exp)
def test_outdegree_cdf():
cdf_ref = np.array([1., 0.83333333, 0.83333333, 0.5])
assert np.allclose(Network.SmallTestNetwork().outdegree_cdf(), cdf_ref)
def test_BarabasiAlbert_igraph():
net = Network.Model("BarabasiAlbert_igraph", n_nodes=100, n_links_each=1)
assert np.allclose(net.link_density, 0.02)
def test_adjacency():
adj_ref = np.array([[0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 1, 0], [1, 1, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0]])
assert np.array_equal(Network.SmallTestNetwork().adjacency, adj_ref)
def test_len():
assert np.allclose(len(Network.SmallTestNetwork()), 6)
def test_str(capsys):
print(Network.SmallTestNetwork())
out, err = capsys.readouterr()
out_ref = "Network: undirected, 6 nodes, 7 links, link density 0.467.\n"
assert out == out_ref
def test_init():
Network(
adjacency=[[0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 1], [0, 1, 0, 1, 1, 0],
[0, 0, 1, 0, 1, 0], [0, 0, 1, 1, 0, 1], [0, 1, 0, 0, 1, 0]])
assert True
import numpy
from pyunicorn import Network, mpi
offset = 10
n_max = 1000
s = 0
n = mpi.rank + offset
while n <= n_max + offset:
s += Network.BarabasiAlbert(n_nodes=n).global_clustering()
n += mpi.size
numpy.save("s" + str(mpi.rank), s)
def test_msf_synchronizability():
res = Network.SmallTestNetwork().msf_synchronizability()
exp = 6.77842586
assert np.allclose(res, exp)
def test_outdegree_distribution():
dist = Network.SmallTestNetwork().outdegree_distribution()
dist_ref = np.array([0.16666667, 0., 0.33333333, 0.5])
assert np.allclose(dist, dist_ref)
def test_ConfigurationModel():
net = Network.Model("Configuration", degrees=[3 for _ in range(0, 1000)])
assert int(round(net.degree().mean())) == 3
def test_nsi_degree_histogram():
hist = Network.SmallTestNetwork().nsi_degree_histogram()
hist_ref = (np.array([0.33333333, 0.16666667, 0.5]),
np.array([0.11785113, 0.16666667,
0.09622504]), np.array([4., 5.46666667, 6.93333333]))
assert np.allclose(hist, hist_ref)
def test_WattsStrogatz():
net = Network.Model("WattsStrogatz", N=100, k=2, p=0.1)
assert int(round(net.degree().mean())) == 4
def test_average_neighbors_degree():
res = Network.SmallTestNetwork().average_neighbors_degree()
exp = np.array([2., 2.33333333, 3., 3., 2.66666667, 3.])
assert np.allclose(res, exp)
def test_edge_list():
edges = Network.SmallTestNetwork().edge_list()[:8]
edges_ref = [[0, 3], [0, 4], [0, 5], [1, 2], [1, 3], [1, 4], [2, 1],
[2, 4]]
assert np.array_equal(edges, edges_ref)
def test_nsi_max_neighbors_degree():
res = Network.SmallTestNetwork().nsi_max_neighbors_degree()
exp = np.array([8.4, 8., 8., 8.4, 8.4, 8.4])
assert np.allclose(res, exp)
def test_undirected_adjacency():
net = Network(adjacency=[[0, 1], [0, 0]], directed=True)
adj_ref = [[0, 1], [1, 0]]
assert np.array_equal(net.undirected_adjacency().A, adj_ref)
def test_global_clustering():
res = Network.SmallTestNetwork().global_clustering()
exp = 0.27777777
assert np.allclose(res, exp)
def test_laplacian():
lap_ref = np.array([[3, 0, 0, -1, -1, -1], [0, 3, -1, -1, -1, 0],
[0, -1, 2, 0, -1, 0], [-1, -1, 0, 2, 0, 0],
[-1, -1, -1, 0, 3, 0], [-1, 0, 0, 0, 0, 1]])
assert np.allclose(Network.SmallTestNetwork().laplacian(), lap_ref)
def test_transitivity():
res = Network.SmallTestNetwork().transitivity()
exp = 0.27272727
assert np.allclose(res, exp)
def test_degree():
deg = Network.SmallTestNetwork().degree()
deg_ref = np.array([3, 3, 2, 2, 3, 1])
assert (deg == deg_ref).all()
def test_nsi_global_clustering():
res = Network.SmallTestNetwork().nsi_global_clustering()
exp = 0.83529192
assert np.allclose(res, exp)
def test_indegree():
deg = Network.SmallDirectedTestNetwork().indegree()
deg_ref = np.array([2, 2, 2, 1, 1, 0])
assert (deg == deg_ref).all()
def test_outdegree():
deg = Network.SmallDirectedTestNetwork().outdegree()
deg_ref = np.array([2, 2, 0, 1, 2, 1])
assert (deg == deg_ref).all()
def test_coreness():
res = Network.SmallTestNetwork().coreness()
exp = np.array([2, 2, 2, 2, 2, 1])
assert (res == exp).all()
